Mark Heynen is the Co-founder and Chief Product Officer at Knapsack, where he's building private AI automations for enterprise use. A seasoned entrepreneur and technology executive, Mark has founded five companies and held key positions at tech giants including Google and Meta (formerly Facebook). His career spans from pioneering online pricing analytics in London to expanding mobile technology access in emerging markets.

Episode Highlights:

[00:00-03:21] From Startup to Big Tech: Heinen's Journey

[03:21-06:36] Knapsack's Three Pillars for Enterprise AI

[06:36-10:00] Edge Computing Transforms Small Language Models

[10:00-15:40] AI Applications Across Industry Sectors

[15:40-20:05] AI Automation Reshapes Future of Work

[20:05-23:23] Transforming Professional Work Through AI 

Episode Links:  

Knapsack: https://www.knapsack.ai/ 

Mark Heynen’s LinkedIn: https://www.linkedin.com/in/markheynen/ 

Mark Heynen’s Twitter: http://x.com/markheynen 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist:   https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter:  https://x.com/dyakobovitch  

- LinkedIn:  https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy

Rob Gurzeev is the CEO and Co-Founder of CyCognito, a cutting-edge cybersecurity company trusted by over 20 of the Global 100 companies. With a background in the elite Israeli Intelligence Corps unit 8200, Rob brings a unique blend of offensive security expertise and innovative thinking to the cybersecurity landscape. Prior to founding CyCognito in 2017, he led the Offensive Security group at C4 (later acquired by Elbit Systems), where he developed intelligence-gathering platforms for agencies.

Episode Highlights:

[00:00] Introduction: HumAIn and Rob Gurzeev

[01:01] Rob's Journey: Intelligence to Silicon Valley

[02:03] Technology Potential vs. Implementation Gap

[04:02] Application Security's Coverage Problem

[06:20] Attackers Exploit Path of Least Resistance

[09:03] AI: Double-Edged Sword in Cybersecurity

[11:35] AI Revolutionizing Reconnaissance

[15:40] Precision and Recall in Security AI

[17:19] Asset Classification and Attribution Challenges

[21:01] Scale of Vulnerability Management

[26:04] Critical Thinking in AI Age

[28:39] CyCognito's External Attack Surface Management

[30:51] Closing Thoughts

Episode Links:  

CyCognito: https://www.cycognito.com/ 

Rob Gurzeev’s LinkedIn: https://www.linkedin.com/in/gurzeev/ 

Rob Gurzeev’s Twitter: https://x.com/Rob_Gurz 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist:   https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter:  https://x.com/dyakobovitch  

- LinkedIn:  https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy 

Siqi Chen is the CEO and Founder of Runway, a finance platform revolutionizing business planning and analysis. With a diverse background spanning gaming, social media, and technology, Siqi has been a serial entrepreneur and leader in the tech industry for over two decades. He previously served as CEO of Sandbox VR and held executive positions at Postmates and Zynga. Siqi's experience ranges from software engineering at NASA's Jet Propulsion Laboratory to founding and selling a gaming company to Zynga. His expertise in product development, growth strategies, and financial planning has led him to create Runway, a platform that aims to disrupt the $80 trillion business industry by integrating ambient intelligence into financial planning and analysis. Siqi is also an angel investor, supporting various successful startups in the tech ecosystem. He holds a BA in Mathematics and Computer Science from the University of California, San Diego.

Episode Highlights:

[00:03] Introducing Runway: Revolutionizing Financial Planning

[01:39] Redefining Finance Through Software

[03:30] Sandbox VR: Catalyst for Financial Innovation

[06:12] Reimagining Interfaces: Design-First Financial Approach

[08:07] Ambient Intelligence: New AI Paradigm

[10:46] Building Complex Products: Challenges and Innovations

[13:44] Common Pain Points in Financial Planning

[16:33] Disrupting Finance: Overcoming Industry Challenges

[18:25] Integrations: Creating Holistic Business Simulations

[20:52] Future of Finance Teams: Strategic Partners

Episode Links:  

Runway: https://runway.com/ 

Siqi Chen’s LinkedIn: https://www.linkedin.com/in/siqic/ 

Siqi Chen’s Twitter: https://x.com/blader 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist:   https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter:  https://x.com/dyakobovitch  

- LinkedIn:  https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy 

Protik Mukhopadhyay is the Chief Operating Officer (COO) at Protecto.ai, a venture-backed company specializing in secure and privacy-focused Retrieval-Augmented Generation (RAG) solutions. With over 15 years of experience in artificial intelligence, large language models, and data privacy, Protik is a seasoned entrepreneur and thought leader in the AI industry.

OUTLINE:

3:03 RAG Systems Key Dimensions

5:46 RAG Implementation Challenges

8:31 Effective RAG Use Cases

11:16 AI Ethics in RAG

14:01 Protecto's Data Protection Approach

17:31 RAG Development Lessons Learned

20:16 On-premise vs. SaaS Deployment

22:46 Role-based Access in RAG

Episode Links:  

Protecto AI: https://www.protecto.ai

Whitepaper: https://www.protecto.ai/trustworthy-ai-whitepaper 

Sign up for a GenAI Strategy Roadmap Session: https://aistrategynow.com/  

Protik Mukhopadhyay’s LinkedIn: https://www.linkedin.com/in/protikm/ 

Protik Mukhopadhyay’s Twitter: https://twitter.com/protik_m 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist:  https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter: https://x.com/dyakobovitch  

- LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy

Sean White is the CEO of Inflection AI, a pioneer in human-centered artificial intelligence. With a career spanning decades in tech innovation, Sean has been at the forefront of computer vision and AI technology. His experience includes key roles at Mozilla as Chief R&D Officer, work on augmented reality at the Smithsonian and Columbia University, and contributions to early web-based email systems. Sean's passion for human-computer interaction and collaborative intelligence drives Inflection AI's mission to create AI systems that enhance human capabilities and improve organizations.

OUTLINE:

0:00 - Introduction and Sean's background

4:09 - Inflection AI's position in the AI landscape

8:43 - Balancing consumer and enterprise AI products

10:14 - Inflection AI Studio approach

12:59 - Emotional intelligence in AI development

15:12 - Open source philosophy in AI

18:09 - Ideal use cases for Inflection AI in enterprises

21:00 - Rollout strategy for enterprise AI solutions

23:16 - Closing thoughts and call to action

Episode Links:  

Inflection AI: https://inflection.ai/ 

Request Inflection AI API Access: https://docs.google.com/forms/d/e/1FAIpQLScM9Iz1KzaRlfgDrYrldoPDnXbhO5LW3-hqmQCd56YpheEN7g/viewform 

Sean White’s LinkedIn: https://www.linkedin.com/in/seanwhite/

Sean White’s Twitter: https://twitter.com/seanwhite 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist:  https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter: https://x.com/dyakobovitch  

- LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy

Seth Dobrin is a prominent figure in the AI and data science industry. He is the co-founder and GP of One Infinity Ventures, a venture fund focused on deep tech and responsible AI. Seth is also the founder of Quantum AI, a consulting company specializing in AI strategy, governance, and education for Fortune 500 companies and governments worldwide. Previously, he served as the Chief AI Officer at IBM. With nearly two decades of experience in data and AI transformations, Seth is the author of "AIQ: For a Human-Focused Future," which outlines his methodology for successfully implementing AI in enterprise settings.

OUTLINE:

01:04 Introduction of Seth Dobrin and his background

04:58 Early corporate AI initiatives described as a "scam"

08:30 Aligning AI with business strategy

10:41 The concept of AI IQ

13:01 Role of the Chief AI Officer

16:37 Data quality and governance

19:23 Coexistence of traditional AI/ML and Gen AI

21:30 Balancing innovation with ethical considerations

23:31 Early warning signs of AI initiatives going off track

24:55 Fostering an AI-ready culture

28:23 Challenges and opportunities in AI adoption

29:52 Closing remarks and book promotion

Episode Links:  

Seth Dobrin’s LinkedIn: https://www.linkedin.com/in/sdobrin/ 

Seth Dobrin Website: https://drsethdobrin.com/ 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist:  https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter: https://x.com/dyakobovitch  

- LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy

Paul Baier is the CEO of GAI Insights, a company specializing in generative AI strategies for enterprises. With over 25 years of experience in B2B sales and venture-backed companies, Paul has become a thought leader in the AI space. He previously worked at FirstFuel, using traditional AI for building efficiency analysis. Paul is known for developing frameworks like "Own Your Own Intelligence" (OYOI) and WINS, which help businesses navigate the rapidly evolving landscape of generative AI. He also leads weekly Gen AI learning labs and is actively involved in initiatives to grow AI talent in Massachusetts.

0:00 - Introduction

2:15 - Paul's AI journey

4:30 - OYOI concept

7:45 - WINS framework

11:20 - Gen AI learning labs

15:40 - AI Blueprint for MA

19:30 - Embracing AI change

22:45 - GAI Insights initiatives

24:15 - Closing remarks

Episode Links:  

Paul Baier’s LinkedIn: https://www.linkedin.com/in/paulbaier 

GAI Insights OYOI: https://gaiinsights.com/own-your-own-intelligence 

AI Blueprint: https://ai-blueprint-ma.com/ 

GAI Insights News: https://gaiinsights.com/news-1-0 

GAI Insights Learning Lab: https://gaiinsights.com/learning-lab 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist: https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter: https://x.com/dyakobovitch  

- LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy

Kit Merker is the CEO of Plainsight Technologies, a company specializing in computer vision and AI solutions. With over 20 years of experience in the tech industry, Kit has held positions at major companies like Microsoft and Google, where he was an early team member for Kubernetes. His expertise spans developer tools, DevOps, cloud computing, and now AI applications for business. Kit is passionate about responsible AI development and implementing ethical practices in the rapidly evolving field of artificial intelligence.

OUTLINE:

0:00 - Introduction and Kit's background

4:08 - Plainsight's mission and technology

7:57 - Evolution of computing power and AI applications

11:12 - Ethical AI and the future of work

15:41 - AI demos and technological hype

20:40 - Responsible AI and data usage in business

28:38 - Importance of ethical AI implementation

30:09 - Conclusion and call to action

Episode Links:  

Kit Merker’s LinkedIn: https://www.linkedin.com/in/kitmerker/ 

Plainsights Website: http://plainsight.ai/filters 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Full episodes playlist: https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter: https://x.com/dyakobovitch  

- LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy 

Martin Musiol is an AI expert, founder, and CEO of GenerativeAI.net. With a background from the Technical University of Munich, Martin has been at the forefront of generative AI since 2016. He created the world's first online course on generative AI and has worked as the Gen AI lead for Europe at Infosys. Martin is the author of "Generative AI: Navigating the Course to the Artificial General Intelligence Future" and is currently building a startup focused on multi-agent AI frameworks.

0:00 - Introduction

3:00 - GANs to Transformers

8:00 - Mamba architecture

14:00 - Current state of Generative AI

20:00 - Martin's book on Generative AI and AGI

27:00 - AI-powered robotics in industry

29:30 - RAG systems

34:30 - Context windows in language models

35:10 - Martin's new venture

39:00 - Closing thoughts on Generative AI economy

Episode Links:  

Martin Musiol LinkedIn: https://www.linkedin.com/in/martinmusiol1/ 

MartinMusiol Website: https://generativeai.net/ 

Generative AI Book: https://www.amazon.com/dp/1394205910 

PODCAST INFO:

Podcast website: https://www.humainpodcast.com  

Apple Podcasts: https://apple.co/4cCF6PZ 

Spotify: https://spoti.fi/2SsKHzg 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

Full episodes playlist:  https://www.humainpodcast.com/episodes/ 

SOCIAL:

- Twitter: https://x.com/dyakobovitch  

- LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

- Events: https://lu.ma/tpn 

- Newsletter: https://bit.ly/3XbGZyy

Bio: Javier Luraschi is the CEO and Founder of Hal9. With over 15 years of experience in software engineering, Javier has worked at companies like Microsoft Research, RStudio (now Posit), and SAP. He co-created open-source tools such as MLflow and ported PyTorch and Spark to R. Javier is passionate about democratizing AI and helping enterprises leverage generative AI technologies.

Show Notes:

1. From Microsoft Access to Modern Data Science: Javier's Journey Through Tech Evolution
2. The Transition from Statistics to Data Science and Machine Learning
3. AI2 and the Genesis of Hal9: Focusing on Code Generation for Enterprise AI
4. Democratizing AI: Empowering Business Users with Code Generation Tools
5. The Future of Work: Human-AI Collaboration vs. Full Automation
6. Redefining Roles: From Prompt Engineer to Prompt Specialist
7. Hal9's Enterprise Solution: Bridging the Gap Between ChatGPT and Custom AI Integration
8. The Strategic Value of AI Customization for Businesses
9. The Future Landscape of LLMs in Enterprise: Diversity and Customization
10. Call to Action: Exploring Hal9 for Personal and Enterprise Use

Episode Links:  

Javier Luraschi LinkedIn: https://www.linkedin.com/in/javierluraschi/ 

ESG Flo Website: https://hal9.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Support and Social Media:  

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

In this episode we discuss:

Max Cho's Journey: From Tech Giants to Revolutionizing Insurance

The Birth of Coverage Cat: Addressing Personal Pain Points in Insurance

Unveiling Inefficiencies: The Current Landscape of the Insurance Industry

Crisis Management: Navigating Insurance Challenges in Florida and Beyond

AI's Double-Edged Sword: Potential and Pitfalls in Insurance

Global Perspective: Comparing U.S. Insurance Complexities with International Markets

Coverage Cat's Innovation: Data-Driven Solutions for Insurance Consumers

Regulatory Reform: Shaping a More Transparent Insurance Industry

Empowering Consumers: Expert Advice on Navigating Insurance Choices

Episode Links:  

Max Cho LinkedIn: https://www.linkedin.com/in/maxrcho/

Coverage Cat Website: https://www.coveragecat.com/

Learn More:

• https://www.coveragecat.com/umbrella-insurance 
• https://www.coveragecat.com/carrier-comparison

Jackie Peters: Co-founder and CEO of Blind Insight, Jackie brings over 25 years of experience in tech, with a strong focus on healthcare and privacy. Her career spans product development, health tech, and decentralized technologies, including a role as the founding product person at Orchid.

Nick Sullivan: Technical co-founder of Blind Insight, Nick has extensive expertise in cryptography, security, and privacy-enhancing technologies. With a decade of experience building security and cryptography systems at Cloudflare, Nick is passionate about applying privacy technologies to solve real-world data security challenges.

In this episode we discuss:

Encrypted Database Innovation

Founders' Diverse Tech Backgrounds

Data-Driven Economy in 2024

Privacy and Security Challenges in Data Utilization

Blind Insight's Encrypted Analytics Solution

Public Beta Launch and Current Capabilities

Developer-Centric Product Design

Expanding Encrypted Data Operations

Pioneering "Encryption in Use" Market

Episode Links:  

Jackie Peters LinkedIn: https://www.linkedin.com/in/jackiepeters/ 

Nick Sullivan LinkedIn: https://www.linkedin.com/in/ntsullivan/

Blind Insight Website: https://www.blindinsight.com

Sign up for the Beta - free for 30 days no credit card. beta.blindinsight.io

[Audio] 

Patrick Obeid, is the founder of ESG Flo, the leading ESG software that leverages artificial intelligence to seamlessly automate the collection and transformation of ESG data into audit-ready metrics.

In this episode we discuss:

Introduction to the HumAIn podcast and ESG Flow

Patrick's journey from consultant to entrepreneur

Transition from advisor to operator in tech industry

Discovery process: Interviewing 100 executives in 60 days

Identifying the need for non-financial data infrastructure

Why ESG matters now: Climate crisis and wealth gap

ESG Flow's focus on heavy industries and key metrics

Three-layer approach to ESG data management

CSRD compliance and creating the ESG auditability market

Episode Links:  

Patrick Obeid LinkedIn: https://www.linkedin.com/in/patrick-obeid-esg/

ESG Flo Website: https://www.esgflo.com/

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Support and Social Media:  

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

[Audio] 

Max Galka is the CEO of Elementus, the first universal search engine for blockchain and institutional grade crypto forensics solution.

In this episode, we talk about all things Blockchain, Bitcoin, Data, and AI.

Episode Links:  

Max Galka LinkedIn: https://www.linkedin.com/in/maxgalka/

Elementus Website: https://www.elementus.io/

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

Support and Social Media:  

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

[Audio] 

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Steven Banerjee is the CEO of NExTNet Inc. NExTNet is a Silicon Valley based technology startup pioneering natural language based Explainable AI platform to accelerate drug discovery and development. Steven is also the founder of Mekonos, a Silicon Valley based biotechnology company backed by world-class Institutional investors (pre-Series B) — pioneering proprietary cell and gene-engineering platforms to advance personalized medicine. He also advises Lumen Energy, a company that uses a radically simplified approach to deploy commercial solar. Lumen Energy makes it easy for building owners to get clean energy.  

Please support this podcast by checking out our sponsors:

Episode Links:  

Steven Banerjee LinkedIn: https://www.linkedin.com/in/steven-banerjee/ 

Steven Banerjee Website: https://www.nextnetinc.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(05:20)- So I am a mechanical engineer by training. And I started my graduate research in semiconductor technologies with applications in biotech almost more than a decade ago, in the early 2010s. I was a Doctoral Fellow at IBM labs here in San Jose, California. And then I also ended up writing some successful federal grants with a gene sequencing pioneer at Stanford, and Ron Davis, before I went, ended up going to UC Berkeley for grad school research, and then I became a visiting researcher.  

(09:28)- An average cost of bringing a drug to market is around $2.6 billion. It takes around 10 to 15 years, like from the earliest days of discovery, to launching into the market. And unfortunately, more than 96% of all drug R&D actually fails . This is a really bad social model. This creates this enormous burden on our society and our healthcare spending as well. One of the reasons I started NextNet was when I was running Mekonos, I kept on seeing a lot of our customers had this tremendous pain point of, where you go, there's all this demand and subject matter experts, as scientists, they're actually working with very little of the available biomedical evidence out there. And a lot of the times that actually leads to false discoveries. 

(13:40)- And so there are tools, they're all this plethora of bioinformatics tools and software and databases out there that are plagued with program bugs. They mostly lack documentation or have very complicated documentation and best, very technical UI’s. And for an average scientist or an average person in this industry, you really need to have a fairly deep grasp or a sophisticated understanding of database schemas and SQL querying and statistical modeling and coding and data science.  

(22:36)- So, a transformer is potentially one of the greatest breakthroughs that has happened in NLP recently. It's basically a neural net architecture that was incorporated into NLP models by Google Brain researchers that came along in 2017 and 2018. And before transformers, your state of the art models and NLP basically were like, LSTM, like long term memories are the widely used architecture. 

(27:24)- So Sapiens is, our goal here is to really make biomedical data accessible and useful for scientific inquiry, using this platform, so that, your average person and industry, let's say a wet lab or dry lab scientist, or a VP of R&D or CSO, or let's say a director of research can ask and answer complex biological questions. And a better frame hypothesis to understand is very complex, multifactorial diseases. And a lot of the insights that Sapiens is extracting from all this, with publicly available data sources are proprietary to the company. And then you can map and upload your own internal data, and begin to really contextualize all that information, by uploading onto the Sapiens.  

(31:34)- We are definitely looking for early adopters. This includes biotech companies, pharma, academic research labs, that would like to test out Sapiens and like this to be a part of their journey of their biomedical R&D. We're definitely, as I said, looking for investors who would like to partner with us, as we continue on this journey of building this probably one of the most sophisticated natural language based platforms, or as we call it, an excellent AI platform.  

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Steve received his PhD from Johns Hopkins University in Cognitive Science where he began his AI research and also taught Statistics at Towson State University. After receiving his PhD in 1979, AI pioneer Roger Schank invited Steve to join the Yale University faculty as a postdoctoral researcher in Computer Science. In 1981, Roger asked Steve to help him start one of the first AI companies, Cognitive Systems, which progressed to a public offering in 1986.  

Steve then started Esperant, which produced one of the leading Business Intelligence products of the 1990s. During the 1980s, Steve published 35 articles and a book on AI, spoke at many AI conferences, and received two commercial patents on AI. As the AI Winter of the 1990s set in, Steve transitioned into a career as a successful serial software entrepreneur and investor and created several companies that were either acquired or had a public offering.  

He tries to use his unique perspective as an early AI researcher and statistician to both explain how AI works in simple terms, to explain why people should not worry about intelligent robots taking over the world, and to explain the steps we need to take as a society to minimize the negative impacts of AI and maximize the positive impacts. 

Please support this podcast by checking out our sponsors:

Episode Links:  

Steven Shwartz LinkedIn: https://www.linkedin.com/in/steveshwartz/ 

Steven Shwartz Twitter: https://twitter.com/sshwartz 

Steven Shwartz Website: https://www.device42.com 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

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YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(09:42) – So most of the things that are taking jobs for example, is conventional software, not AI software.

(10:57)- Exactly. And that's automated but it's conventional software. It's not AI. And most of the examples of where computers are replacing people, it's conventional software. It's not AI software.

(14:49)- How you get data quality into your AI models and it's what they do that's really interesting. And I hadn't actually focused on it until I talked to this company. There's a big industry to clean data for tools like business intelligence that have been around for a long time. And there are, there are companies that are multi-billion dollar companies that provide data, cleaning tools, data extraction, and so forth.

(17:13)- Everybody thought that with AI, you could diagnose illnesses from medical images better than the radiologists. And it's never actually worked out that way. I have friends who are radiologists, who use those AI tools and they say yes, sometimes they find things that I might've missed. But at the same time, they miss things that we would have found.

(22:17)- I think we're seeing a lot of the rollout of a specific type of AI supervised learning, which is a type of machine learning. We're seeing it applied in many different areas. I actually have a database I keep before every time I see a new application of supervised learning and it's fascinating. It's being used in almost every area of business, of government, of the nonprofit world. It is fascinating how much application there is.  

(27:06)- And they're not really going to make sense if you drill down into them. So what's going to be the implication of that. Is it only going to be useful if there's all kinds of search engine optimization where you don't really care If what you're right makes sense. We're going to generate a lot of crap using GPT three and put it out there for search engine optimization purposes.

(31:19)- And I think there's a lot of opportunity for companies that are helping develop software and services to help companies build non-biased explainable systems. And then you have a whole issue around when you build a machine learning system, it deteriorates over time. So it might only work for a couple of days and then start to go downhill. It might work for weeks, but you have to monitor those systems and go back and retrain them when the performance goes down. And all of that is a lot of effort. 

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Gianluca Mauro is the CEO of AI Academy, which he founded with the mission of helping people understand what artificial intelligence is and its place in their organizations and their career. Gianluca is the author of the book "Zero to AI - A nontechnical, hype-free guide to prospering in AI era" 

Over the years, Gianluca and his team have done both technical consulting and training workshops, working with companies like P&G, Merck, Brunello Cucinelli, Daikin, Fater, Bayer, and EIT Innoenergy 

Gianluca teaches Artificial Intelligence to people without a tech background, without any code or math. Why? Because he believes, the future of artificial intelligence is in the hands of people who can find use cases in their organizations, and then define and run AI projects. 

Please support this podcast by checking out our sponsors:

Episode Links:  

Gianluca Mauro LinkedIn: https://www.linkedin.com/in/gianlucamauro/ 

Gianluca Mauro Twitter: https://twitter.com/gianlucahmd 

Gianluca Mauro Website: https://ai-academy.com 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(04:15)-Sometimes it's not a concept that people are familiar with. It sounds weird to anybody who works in tech. But, a lot of companies, in these industries, are still struggling with the cloud. So, when you go to these companies and start talking about this technology, they are excited. They're like, this sounds amazing, but you have to keep into account the reality of where they are, they're not in a place where they can invest in hiring a full-blown data science team, because then nobody knows how to interact with them. 

(09:29)- So, having the right governance for how to use the data, how to keep it in the right shape, and making sure that the quality is what we need, and then actually bring into the laptops of the data scientists that they can make tests and run experiments and make graphs. So, I always like to say it doesn't really matter how good your technology is. How good is your data warehouse or whatever kind of stock you use if using that data is not easy. If using that data it's not straightforward for a data scientist. 

(17:32)- And in the same way, if we want to use AI for marketing, you need to give tools to the marketers that understand the problem to use AI on their data for their problems. When I talk about sales, well, I understand sales data set and takes me a lot of time to understand the logics of sales, have a sales team of the data that its Sales team works with to a sales team who really understands this data, the right tools to, they don't have to be able to do everything but the list to get started, well, then they know much better than me the data.  

(18:17)- So, it's kind of a paradox, because the most important thing of the app is the recommender system. But the reason why that works is not because of the tech, but because of how the UX feeds the tech. And if you think about this, think about this concept, well, then your UX designers, they need to understand this, they need to understand what it means to feed an algorithm with the right data.  

(23:40)- And so we have seen cases where these things went wrong. And I may start from the stuff that everybody knows about, the elections in 2016, fake news and all this stuff up until more niche, let's say topics that maybe not a lot of people aren't aware of. But that actually had a strong impact on people. An example is AI in hiring. There was a very interesting research made by MIT Technology Review about how a lot of companies that sell software for hiring and leverage AI are actually biased. 

(31:01)- And it has been amazing, honestly, because then you'll have people coming from all sorts of backgrounds. I give them the tools and the foundational knowledge that they need to talk about these topics in a way that is productive and they bring the wrong perspectives. They bring their own experience. And I had to say, I've been amazed by the insights that we were able to get from these conversations. 

[Audio] 

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Ben Zweig is the CEO of Revelio Labs, a workforce intelligence company. Revelio Labs indexes hundreds of millions of public employment records to create the world’s first universal HR database. This allows Revelio Labs to understand the workforce dynamics of any company. Revelio customers include investors, corporate strategists, HR teams, and governments.

Ben worked as a data scientist at IBM where he led analytic teams. He is an economist and entrepreneur and also an adjunct professor at Columbia Business School and NYU Stern School of Business respectively. He teaches courses currently at NYU Stern School of Business including future of work, data boot camp and econometrics.

Please support this podcast by checking out our sponsors:

Episode Links:  

Ben Zweig LinkedIn: https://www.linkedin.com/in/ben-zweig/ 

Ben Zweig Twitter: https://twitter.com/bjzweig 

Ben Zweig Website: https://www.reveliolabs.com 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

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YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

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– Support on Patreon: https://www.patreon.com/humain/creators 

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– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(02:56)- So, I started my career in academia, I was doing a Ph.D. in economics and specialized in labor economics. So I was always very interested in labor data, and understanding occupational dynamics, social mobility, things like that. My first job was a data scientist, this was very early on at a hedge fund in New York. It was an emerging market hedge fund. I started that in 2012. That was kind of interesting. I was like the lone data scientist on the desk. So that was kind of interesting. And then went to work at IBM, in their internal data science team was called the Chief Analytics Office. 

(08:13)- The workers that were really hardest hit from remote work are really junior employees. They're just getting started and they need that mentorship. And it's much harder to feel like you're developing and learning from others in a remote environment. But as we're sort of going back, the more senior positions, will probably not have that same benefit as junior employees. 

(15:53)- One phenomenon that we see quite a lot is that companies have a huge contingent workforce that is not reported on their financial statements. So, for example, I mentioned I used to run this workforce analytics team at IBM. And at IBM, we had 330,000 employees, that was like the number that's in their HR database, but you go to their LinkedIn page, and it looks like 550,000 people say that they work at IBM. So, what's going on here? Why are there so many more people that claim to work at a company, then the company claims to work there? And that, of course, is just a sample; only a sample of people actually have online profiles.  

(29:33)- But when it comes to human capital data, and employment data, that really does not exist, it's not even really close to that. There's so much data that's siloed in internal HR databases, which like I mentioned before, really only include a fraction of the overall workforce of a company. But what's cool about this is that when an employee is stored in an HR database, that information is mirrored in the public domain. 

(21:22)- So, we really have to create a taxonomy that updates that changes with an evolving occupational landscape and the changing economy. We also really need to infer the activities that people do, because those are the building blocks of a job, or the job is a bundle of activities. So, we really need to understand that when one person says lawyer and another person says, attorney, those are probably the same occupation, but when one person says Product Manager in Facebook versus a Product Manager at JPMorgan, those might be totally different occupations. 

(30:21)- So, what are the HR tech companies that are really dominating, and then it gets even specific, who's dominating the self-driving car market, how benefits help retention of women in the workforce, that's something that we've seen some changes in the past couple of years. We did a piece that I really liked, which was tracking the rise and fall of hustle culture. 

[Audio] 

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Edo Liberty is the CEO of Pinecone, a company hiring exceptional scientists and engineers to solve some of the hardest and most impactful machine learning challenges of our times. Edo also worked at Amazon Web Services where he managed the algorithms group at Amazon AI. 

As Senior Manager of Research, Amazon SageMaker, Edo and his team built scalable machine learning systems and algorithms used both internally and externally by customers of SageMaker, AWS's flagship machine learning platform. 

Edo served as Senior Research Director at Yahoo where he was the head of Yahoo's Independent Research in New York with focus on scalable machine learning and data mining for Yahoo critical applications.

Edo is a Post Doctoral Research fellow in Applied Mathematics from Yale University. His research focused on randomized algorithms for data mining. In particular: dimensionality reduction, numerical linear algebra, and clustering. He is also interested in the concentration of measure phenomenon. 

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Edo Liberty LinkedIn: https://www.linkedin.com/in/edo-liberty-4380164/ 

Edo Liberty Twitter: https://twitter.com/pinecone 

Edo Liberty Website: https://www.pinecone.io 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

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Support and Social Media:  

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– Support on Patreon: https://www.patreon.com/humain/creators 

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– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(06:02)- It's funny how being a scientist and building applications and building platforms are so different. It's kind of like for me it's just by analogy, I mean, kind of a scientist, if you're looking at some achievement, like technical achievement as being a top of a mountain and a scientist is trying to like hike, they're trying to be the first person to the summit. 

(06:28)- When you build an application, you kind of have to build a road, you have to be able to drive them with a car. And when you're building a platform on AWS or at Pinecone, you have to like build a city there. You have to really like, completely like to cover it. For me, the experience of building platforms and AWS was transformational because the way we think about problems is completely different. It's not about proving that something is possible, it is building the mechanisms that make it possible always for, in any circumstance. 

(13:43)- And so on and today with machine learning, you don't really have to do any of that. You have pre-trained NLP models that convert a string, like a, take a sentence in English to an embedding, to a high dimensional vector, such that the similarity or either the distance or the angle between them is analogous to the similarity between them in terms of like conceptual smelts semantic similarity.

(18:17)- Almost always Pinecone ends up being a lot easier, a lot faster and a lot more production ready than what they would build in house. A lot more functional. We've spent two and a half years now baking a lot of really great features into Pinecone. And we're, we've just launched a version 2.0 that contains all sorts of filtering capabilities and cost reduction measures and you name it.    

(21:22)- And so I'm a great believer in knowing your own data and knowing your own customers and training your own models. It doesn't mean that you have to train them from scratch. It doesn't mean you don't have to use the right tools. You don't have to reinvent the wheel, but I'm not a big believer in completely pre-trained, plucked off of a random place in the internet models. I do want to say that there are great models for just feature engineering for objects that don't change so much. So we have language models like BERT that transform text and create great embeddings and they're a good starting point. 

(31:01)- So I think you'll see two things. First of all, with Pinecone specifically, we're focused on really only two things; making it easy to use and get value out of Pinecone and making it cheaper. That's it! I mean that, those are the only two things we care about. Like if you can get a ton of value out of it and it doesn't cost you too much, that's it, you're a happy customer and we're happy to get you there. So that pretty much sums up all of our focus. 

[Audio] 

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Thor Ernstsson is the CEO of Strata, a company that helps customers invest in their networks, no matter how busy they are. Strata enables intelligent outreach recommendations that strengthen professional relationships. With their easy to use platform, clients become more thoughtful and helpful to the most important people in their network.

Thor is also the founder of Feedback Loop, which companies use to build real time feedback loops with their target markets. Basically customer development delivered at scale. Used by half of the F100 as well as some of the best tech companies around. Thor previously served as CTO of Audax Health and lead architect at Zynga where helped build up Zynga's first remote studio. Thor and the team at Zynga created and released Frontierville as the company's most successful product launch at the time. 

Episode Links:  

Thor Ernstsson´s LinkedIn: https://www.linkedin.com/in/thorernstsson/

Thor Ernstsson´s Twitter: https://twitter.com/ThorErnstsson

Thor Ernstsson´s Website: https://www.strata.cc/

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

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Support and Social Media:  

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– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:24) – It starts in the very beginning in rural Iceland. I grew up on the Northern coast of Iceland, in a little fishing village. We're about 450 people in technology there, which is a little bit different than how we think of it today. But, in a roundabout way, we ended up in New York, 20 years in the US and 10 in New York and absolutely love it here. And the reason is primarily that there's so much creative energy around, exactly your topic.

(03:34) – So what we were doing at Feedback Loop, the core of it is really you take a business question: Is this going to work, for example. Which is not a well-formed research question. So we have to translate it into the intent of the question. What you're intending to do is assess functionality or competitors features or price point or messaging or whatever it is.

(07:13) – Because, even though you can only juggle in your mind, let's just say 150, and the number is a bit fuzzy, but let's say that it is 150. You interact with thousands of people throughout your career, and you go to a conference and you meet a bunch of great, interesting people that you want to stay in touch with. You have coworkers that you may have worked with five years ago, 10 years ago, doing either something really fascinating and you want to stay in touch, or they're just friends and you liked interacting with them and you want to stay in touch.

(10:10) – Most people, when they first think about it, they're like: I want more out of my network. But when we interview, especially the more senior, and we interview people, what we learn is the same thing over and over. It's not that they want to get something out of their network. It's not that they want to know who they should reach out to for sale or for deal or for VC. You need to stay in touch with their LPs and stuff like that, but it's really more about giving back.

(13:31) –You just highlight a perfect example, people can't actually track all the communication again. There are so many things that fall through. So what we do first is we start with a bunch of rules. So there's heuristics around what might be important. It's this sort of static analysis of your communication and your calendar of your stuff like that. And then what we learn over time is who's important to you.

(17:30) – The COVID and just in general, digitization of everything and making everything Zoom makes this problem much worse, because before you would get a coffee, you would see somebody in person, you have all these nonverbal cues, you have all these triggers and all those memories that are way more than what you have when it's just pixels on a screen. 

(21:22) – We're helping you uncover the things you should be doing, even if you don't know what you should be doing. That's kind of the key here is that it's doing the thinking and the heavy lifting for you. You click to accept it. You can reach out. You can action it. You can say like create a task out of it, basically. So that if I say to you in an email, or if you just send many emails ago, like that you used to introduce me to other speakers or podcasts.

(24:53) – There's a lot of really interesting work that has been done that we can leverage in your right, that like building this from scratch even 10 years ago would not be possible. It's everything from memory constraints on the actual servers. The fact that I can spin up a 90, it was a 96 or 92 core Amazon instance and just at the click of a button and trained a model. I couldn't have done that before. So it would have been prohibitively expensive and improvely hard, actually, it's just not wasn't there. 

(25:53) – So there's lots of ways that email threads end, then we're trying to figure out. Can we tell which ones are natural and which ones are effectively errors, where you were when you dropped the ball on something. It's a fascinating problem. We have millions of messages to train on where you can see this. This ended and this didn't, and then we've got to figure out, how do you know if it was intentional or not.

(28:55) – It's a combination of things. So, it's definitely the chief of staff in that way, but, arguably, it's more like a social secretary. So it's like helping organize the most important relationships you have. So for example, if you're traveling to Chicago, who should you reach out to? Because I've started heuristics, so obviously people that live there, fine. Second, people you met last time you were there, fine. Third, people you've talked about meeting up with in Chicago. Maybe you will remember that maybe you have a super memory where you're not limited by only 150 relationships and you can actually classify all minus like 30,000 people.

(32:37) – We have a few products that we launched: the recommendations where you get three recommendations every week, plus memes and so corporate communication seems to be working. So that's live now called Reconnect. So definitely go to Straddled that CC and sign up for that. Then we're going to be launching the broader platform that I'm talking about that has all these integrated triggers, and nudges, and juristics, and patterns like travel, list building, list sharing, all those things that I suspect just about everybody who's listening to this does right now, and it'd be great to hear feedback.

[Audio] 

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Stephen Miller is the Cofounder and SVP Engineering at Fyusion Inc. He has conducted research in 3D Perception and Computer Vision with Profs Sebastian Thrun and Vladlen Koltun while at Stanford University. His area of specialization is AI and Robotics, which included 2 years of undergraduate research with Prof Pieter Abbeel. 

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Stephen Miller’s Website: http://sdavidmiller.com/ 

Podcast Details: 

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Support and Social Media:  

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:42) – Started in robotics around 2010, training them to perform human tasks (surgical suturing, laundry folding). Clearest bottleneck was not “How do we get the robot to move properly” but “How do we get the robot to understand the 3D space it operates in?”   

(04:05) – The Deep Learning revolution around that era was very focused on 2D images. But it wasn’t always easy to translate those successes into real world systems: the world is not made up of pixels; it’s made up of physical objects in space.

(06:57) – When the Microsoft Kinect came out; I became excited about the democratization of 3D, and the possibility that better data was available to the masses. Intuitive data can help us more confidently build solutions. Easier to validate when something fails, easier to give more consistent results. 

(09:20) – Academia is a vital engine for moving technology forward. In hindsight, for instance, those early days of Deep Learning -- one or two layers, evaluating on simple datasets -- were crucial to ultimately advancing the state of the art we see today. 

(14:48) – Now that Machine Learning is becoming increasingly commodified, we are starting to see a growing demand for people who can bridge that gap on both sides: conferences requiring code submissions alongside a paper, companies encouraging their engineers to take online ML courses, etc.

(17:41) – As we do finally start to see real-time computer vision productized for mobile phones, it does beg the question: won’t this exacerbate the digital divide? Flagship devices, always-on network connectivity: whether computing on the edge or in the cloud, there is going to be a disparity. 

(20:33) – Because of this, I think the ideal model is to treat AI as one tool among many in a hybrid system. Think smart autocomplete, as opposed to automatic novel writing. AI as an assistant to a human expert: freeing them from the minutia so they can focus on high-level questions; aggregating noise so they can be more consistent and efficient. 

(23:08) – Computer Vision has gone through a number of hype cycles in the last decade –real-time recognition, real-time reconstruction, etc. But the showiest of these ideas seem to rarely leave the realm of gaming, or tech demonstrator. I suspect this is because many of these ideas require a certain level of perfection to be valuable. It’s easy to imagine replacing my eyes with something that works 100% of the time. But what about 90%? At what point is the hassle of figuring out whether I’m in the 10% bucket or the 90% bucket, outweighing the convenience?

[Audio] 

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Nell Watson is an interdisciplinary researcher in emerging technologies such as machine vision and A.I. ethics. Her work primarily focuses on protecting human rights and putting ethics, safety, and the values of the human spirit into technologies such as Artificial Intelligence. Nell serves as Chair & Vice-Chair respectively of the IEEE’s ECPAIS Transparency Experts Focus Group, and P7001 Transparency of Autonomous Systems committee on A.I. Ethics & Safety, engineering credit score-like mechanisms into A.I. to help safeguard algorithmic trust.

She serves as an Executive Consultant on philosophical matters for Apple, as well as serving as Senior Scientific Advisor to The Future Society, and Senior Fellow to The Atlantic Council. She also holds Fellowships with the British Computing Society and Royal Statistical Society, among others. Her public speaking has inspired audiences to work towards a brighter future at venues such as The World Bank, The United Nations General Assembly, and The Royal Society.

Episode Links:  

Nell Watson’s LinkedIn: https://www.linkedin.com/in/nellwatson/ 

Nell Watson’s Twitter: https://twitter.com/NellWatson 

Nell Watson’s Website: https://www.nellwatson.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(2:57)- Even though the science of forensics and police work has changed so much in those last two centuries, principles are great, but it's very important that we create something actionable out of that. We create criteria with defined metrics that we can know whether we are achieving those principles and to what degree.

(3:25)- With that in mind, I’ve been working with teams at the IEEE Standards Association to create standards for transparency, which are a little bit traditional big document upfront very deep working on many different levels for many different use cases and different people for example, investigators or managers of organizations, etcetera.

(9:04)- Transparency is really the foundation of all other aspects of AI and Ethics. We need to understand how an incident occurred, or we need to understand how a system performs a function in order to. I analyze how it might be biased or where there might be some malfunction or what might occur in a certain situation or a certain scenario, or indeed who might be responsible for something having gone through it is really the most basic element of protecting ourselves, protecting our privacy, our autonomy from these kinds of advanced algorithmic systems, there are many different elements that might influence these kinds of systems.

(26:35)- We're really coming to a Sputnik moment and AI. We've gotten used to the idea of talking to our embodied smart speakers and asking them about sports results or what tomorrow's weather is going to be. But they're not truly conversational.

(32:43)- Fundamentally technologies and a humane society is about putting the human first, putting human needs first and adapting systems to serve those needs and to truly and better the human condition to not sacrifice everything for the sake of efficiency to leave a bit of slack and to ensure that the costs to society of a new innovation or the costs to the environment are properly taken into effect.

[Audio] 

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Ryan McDonald is the Chief Scientist at ASAPP working on NLP and ML research focusing on CX and enterprise. He is also an Associate researcher in the NLP group at Athens University of Economics and Business. Ryan was a Research Scientist in the Language Team at Google for 15 years where he helped build state-of-the-art NLP and ML technologies and pushed them to production. 

He managed research and production teams in New York and London that were responsible for a number of innovations used in Translate, Assistant, Cloud and Search. He was the first NLP research scientist in both New York and London, and helped grow those groups into world-class research organizations. Prior to that, he did his Ph.D. in NLP at the University of Pennsylvania. 

Episode Links:  

Ryan McDonald’s LinkedIn: https://www.linkedin.com/in/ryanmcd/ 

Ryan McDonald’s Twitter: https://twitter.com/asapp 

Ryan McDonald’s Website: http://www.ryanmcd.com 

CX: The Human Factor Report: https://ai.asapp.com/LP-2021-09-CX-The-Human-Factor_Landing-Page.html

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(3:00)- The kinds of problems that deploying AI runs into for enterprise is more about scalability. Instead of having a single user of the technology, we have hundreds of users of the technology and how can we deliver a unique experience and an excellent experience for each of those users and this necessitates questions around adopting machine learning and natural language processing models to new domains. 

(10:49)- And this is exactly the technology we're building out. How can we sort of regularize that? How can we look at the conversation and the issue that the customer's happening? That's sort of embodied in the dialogue, up to a point in time and then allow AI to make recommendations to the agent; Here is a workflow that we think you should use and all the steps you need to follow in order to solve this issue

(28:33)- So we design everything and that's why it's critical to design these things from the bottom up with AI in mind. All of our artificial intelligence has been designed to serve those latency needs. So to kind of give you a couple of examples, the first is automatic speech recognition. So a huge number of calls that come into call centers are still voice, they're not digital. It's not people call contacting over chat. It's people calling in on their phone. 

(30:41)- So we've focused on building out something called SRU, which is an architecture where we can take super high, accurate AI models and then distill them into these faster architectures, which allows us to get into these millisecond range. So we can get responses back to agents and milliseconds, and that really is going to affect how much they use those suggestions at the end of the day.

(32:38)- Beyond what's happening in the conversation and see everything, all the information and all the actions that the agent can possibly do on their computer. And so agent journey is a product where we, you know, put a piece of software on the agent's computer and it allows us to access into all the tools they're using, how they're using them, how that interacts with the conversation.

(33:49)- Agent journey is our efforts in that space to understand everything holistically that the agent is doing to really make headway in task-oriented dialogue.

[Audio] 

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Humphrey Chen is the CEO and Co-Founder of CLIPr. He has a BS in Management Science from MIT. His work in tech specializes in the use of technology to make people and companies more productive.    

Please support this podcast by checking out our sponsors:

Episode Links:  

Humphrey Chen’s LinkedIn: https://www.linkedin.com/in/humphreychen/ 

Humphrey Chen’s Twitter: https://twitter.com/humphreyc?s=20 

Humphrey Chen’s Website: https://aws.amazon.com/es/rekognition/?blog-cards.sort-by=item.additionalFields.createdDate&blog-cards.sort-order=desc 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:36) – CLIPr operating premise is that not all minutes of video content are equally relevant to everyone. So it uses machine learning to fully index that video and make it fully searchable.

(05:02) – Watching a whole video can be inefficient when a participant only wants to watch specific sections. CLIPr team's speeds up and accelerates more efficient automations to be helpful for both consumers and enterprises. 

(06:42) – The tools that CLIPr provides are a way to guarantee target audience engagement rates to be really informative. CLIPr focuses on this video insight when it comes to engagement and interaction around the video itself in a category called video analysis and management.

(08:04) – CLIPr aims to hand out the tools to efficiently find content that matters, bookmark it, share it, react to it, comment on it. 

(08:27) – The tools and the skills required to edit a video are completely opposite from the skills and tools required for editing inside of a document. CLIPr bridges the two effectively, by building a video-based document type.

(11:57) – There has not been as much disruption around video. Some use cases that have been thought out include recording customer meetings; customers’ feedback, integrations with a CRM record, and also, provide a score over time around the actual probability of closing a sale based on the relative perception for the customer reaction.

(14:20) – AI, additionally with the hospitals and the medical universities and researchers alike are still using antiquated technology and they're not extracting insights from these video moments. CLIPr is also useful in telemedicine. For surgeons, CLIPr means high value, highly visual, high-impact in a short time.

(24:26) – Machine learning, in general, it's all about the data and about engagement and interaction and training new models around the data. So, machine learning allows people to create things and bring solutions. Technology is actually going to find meaningful problems to solve more effectively and more efficiently. 

(28:21) – The purpose of services is to build businesses and to augment either with the stable technology or the experimental technology for what will be the future of AI, of natural language processing of emotion, detection of different technologies. Additional progress still needs to happen beyond the data in telemedicine, EMRs or courtrooms.

(31:49) – As new features get uncovered with specific use cases, anyone can benefit from CLIPr video analytics and management platform. There is continued acceleration for product led growth, closing a 5 million seed round with a strategic partner and keeping focus on machine learning and cloud-based services. Rather than just being an endpoint, it analyzes the data, allows for referential utility, allows for collaboration and allows for monthly recurring revenue.

[Audio] 

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Dave Berchberger is a Senior Graph Architect at Amazon Web Services (AWS). He is known for his expertise in distributed data architecture being a thought leader in graph databases, and the co-author of Graph Databases in Action by Manning Publications. Dave uses his 20+ yrs experience working on and managing teams delivering full-stack software solutions to take a holistic approach to solve complex data problems.    

Episode Links:  

Dave Bechberger’s LinkedIn: https://www.linkedin.com/in/davebechberger/ 

Dave Bechberger’s Twitter: https://twitter.com/bechbd?s=20 

Dave Bechberger’s Website: https://www.manning.com/books/graph-databases-in-action?a_aid=bechberger 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:29) – Corporate environments need to be able to help solve certain types of problems that traditional relational databases or other data technologies are not very good at solving. The new approach is to build out high-performance data platforms on top of a mix of technologies, focused around solving them with graphic, graph database technologies. 

(02:53) – Graphs are the mathematical construct of a graph. It's really about networks, connected data of different people connected to other people or things of that nature. It's about building out networks and using those connections to be able to answer specific types of questions and draw insight and information out of that data that isn't necessarily available from other technologies. 

(06:49) – Fraud is another canonical use case, because it is all about figuring out connections and patterns within data, to be able to discern whether this activity is fraudulent or not. 

(08:32) – Other technologies don't do a great job linking together entities in such a way that those links and those connections are also treated as first-class citizens inside that data. Graphs bring those connections in your data up to being “first-class citizens”. 

(09:29) – With a graph, those connections are brought up and given first class status in the languages and queries that you run. It's called traversing them, to be able to move across them, to be able to drive insight from how those connections are made and how those connections basically connect this network of data together.

(12:38) – Using Graphs makes developers able to not only process data in a real-time transactional mode, but being able to use those along with something like graph type analytics, and then use that in conjunction with AI and ML technologies to augment data back into your graph in order to provide a better real-time user experience.

(14:32) – Any enterprise build or any consumer service build is really about creating a better, faster and easier to use experience for your customers. Those are really the driving forces behind any kind of business initiative. Graphs is one of those technologies.

(16:38) – There's certain types of analytics that can be run on top of graphs that are very helpful to be used as inputs into machine learning algorithms of different types. Some examples show working in a fraud area.

(18:20) – Machine learning in general and graphs-based machine learning specifically, is this concept of a graph neural network, which is basically a neural network that instead of taking only vector features as input, it actually takes in a graph itself. So, graphs as an input to be able to create predictive models on the output. It's building a graph of different connected objects inside the algorithm itself as it's training and learning.

(20:33) – To really be able to build graph-based stack applications or applications on top of graph databases, you don't necessarily need to have all of that very academic understanding. And being able to condense that down into a system that helps people start to think about problems that way was really the purpose with Graph Databases in Action by Manning Publications.

(25:54) – The biggest ways graphs are being adopted today is used in conjunction with other technologies, be those relational databases or document databases or key value stores or whatever other technologies that are out there. 

(28:18) – Graphs is one of those technologies that is definitely a double-edged sword because you're able to drive insights and you'll be able to see connections between things. People could use those connections in nefarious type ways.

[Audio] 

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Alex Beard is the Senior Director at Teach For All , and author of the book Natural Born Learners. After starting out as an English teacher in a London comprehensive, He completed an MA at the Institute of Education before joining Teach For All. His book, “Natural Born Learners”, is a user's guide to transforming learning in the twenty-first century, taking readers on a global tour into the future of education, from Silicon Valley to Seoul, Helsinki to Hounslow.   

Episode Links:  

Alex Beard’s LinkedIn: https://www.linkedin.com/in/alex-beard-08901915/ 

Alex Beard’s Twitter: https://twitter.com/alexfbeard?s=20 

Alex Beard’s Website: https://www.alexbeard.org/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:43) –The methods used to teach would probably be familiar to Socrates two and a half thousand years ago in ancient Greece. Few things have been done differently inside the classroom. The gap between what is possible, and what was currently true in the classroom is at the heart of our education crisis.

(03:03) – The pandemic has widened the educational divide. The pandemic has exacerbated the crisis and intensified some of these questions about the future of education.

(06:30) – Education must consider access and quality. But with schools shut down, access becomes an infrastructure through the internet and that's a relatively technical solution.

(07:38) – If you're not going to school, quality of education is knowledge received sitting in your bedroom via your laptop, which has completely disrupted our idea of what a quality education is.

(08:19) – The vast majority of primary and middle school kids are just not equipped with self motivation yet, so quality has to mean something about human to human engagement. Learning, for most people, is better when it's social.

(13:40) – Practitioners have had to develop new pedagogies, new ways of learning, how to engage kids through the medium of technology. You need to know how to engage a student.

(15:16) – We might be strengthening bonds between teachers and parents, as a result of the pandemic to support early learning, virtually, and that involves engaging parents more actively in supporting their kids to learn.

(18:48) – Our intelligence is unlimited, and it's teachers in schools that cultivate that potential. We need to be more explicit about the different roles that teachers play, and set up our system to enable teachers as subject specialists who help kids to do better. 

(21:12) – Teachers need to be experts in tech, at least to understand how they can use the latest tools to outsource bits of their practice to save themselves time. 

(30:22) – AI is sort of an adversary to help us enhance our own creativity. The dangers are more connected to the intentions. It all comes down to human choices if you deploy technology and in certain ways undermine the ability of humans to get better at things. Lots of people are designing to enhance the humans in the loop, which is how we should be thinking about it.

(36:33) – There are great advances to be made in the deployment of technology in education, but the advances will be made not by trying to improve tech, but by trying to improve what the humans who are doing with tech. Investment in people and not an investment in technology. 

[Audio] 

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Ivy Lu is the head of data science and machine learning at Oxygen. Ivy's onboarding marked the launch of Oxygen’s banking platform. She has bachelor's degree in Geographical Information System from Peking University, a Ph.D in Earth Systems and Geoinformation Science and a Master's degree in Geographic Information Science and Cartography both from George Mason University. 

Episode Links:  

Ivy Lu’s LinkedIn: https://www.linkedin.com/in/ivy9lu/ 

Ivy Lu’s Twitter: https://twitter.com/oxygenbanking 

Ivy Lu’s Website: https://www.blog.oxygen.us/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:42) – I joined Capital One as a data scientist after my graduation from George Mason University with a PhD in Geographic Information Science. After I moved to the west coast, I joined Apple. So, at Apple, I work on an anti-fraud team where we fight against all kinds of fraud and abuse within the whole Apple ecosystem to bring trust and safety to the Apple customers. Both experiments helped me prepare for my new challenge at Oxygen as a FinTech company. So, that's my career , how I passed from the traditional banking industry to a large technology company. And now I'm at the spin hat company Oxygen. 

(04:05) – A collaboration challenge, since you are the only one and only data scientist on the team, basically, you are collaborating with so many different teams and departments: from operations to marketing customer support or product features. So, you need to collaborate with every single one in the different departments and understand their needs, understand their pain. That also comes related to the first challenge. Collaboration comes with prioritization.

(06:57) – Data science teams should be positioned as the foundation and the cross team within the whole organization. So for each line of the business, data scientists should have domain knowledge about the problem that they are trying to deal with

(09:20) – I collaborate with our fraud team to set up a lot of protections in the core sets. We collaborate with different fraud vendors on how to set up all the parameters, all the controls in place in the fraud vendors for our sign up status. After the sign up flow is pretty under control, I built a preliminary machine learning model for the fraudsters, to detect fraudsters after sign up for the behaviors they show with our card.

(14:48) – I see these days, as data scientists it may require different skills than before. Nowadays, maybe, coding skills are not required anymore with such a good tool for data scientists and for machine learning engineers. But, ultimately, I still think the important thing is the study section background on the machine learning algorithm, the deep understanding of the machine learning algorithms. Also what's important is the deep understanding of the problem they're solving.

(17:41) – There are two types of team structure. One is like the data science team belongs to one centralized team and then people may wear multiple hats. So, one day you may work on project A, then another day and work on project B, versus another one that is more embedded.

(20:33) – We launched a new product called Elements. So we are now offering four tiers of the product, with increasing cashback with different saving APRs, as well as other retail and travel benefits like priority pass, launch access, reimbursements, like digital subscriptions, like Netflix, and the Peloton Digital.  

(23:08) – We are going to raise our series B soon and a series B is all about metrics. Whether your company is going to be sustainable, what's your retention, what's your user growth. So a lot of KPIs and the metrics you send show to not only our internal business, but also to work presents for our VC.

[Audio] 

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Asra Nadeem is the Co-Founder of Opus AI, a streaming platform powered by proprietary tech that turns plain text into movies and playable 3D worlds in real-time. She is the first female Pakistani venture capitalist. She has a BA in Economics, and has a Masters in Film/TV/Theater and English Literature from Beaconhouse National University.

Please support this podcast by checking out our sponsors:

Episode Links:  

Asra Nadeem’s LinkedIn: https://www.linkedin.com/in/bretgreenstein/

Asra Nadeem’s Twitter: https://twitter.com/AsraNadeem?s=20 

Asra Nadeem’s Website: https://opus.ai/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:55) –Nadeem’s background and her thesis: There is not any kind of freedom without financial freedom, and technology is a great enabler for that. 

(07:13) – Through a platform that grants access to some of the most brilliant minds in the world for free, anyone can learn and interact now.

(09:02) – ”Naseeb” revolutionized the traditional marriage arrangements in Pakistan, by allowing younger generations to create connections online and get married. 

(11:26) – Formal education has mainly three purposes: learning something, networking and better job opportunities. Those three things are available through technology. 

(13:54) – The Big Names in the tech industry don't request a college degree to work for them, only the skills. It's a different world that is crafting narratives and stories, building stories for the creative industry, and this is a space that's a massive opportunity that has not been tapped into yet.

(14:59) – Opus.ai, an engine that takes any literary text and converts it into a movie. So you have a code without having to know how to code. It can be that tool to enable digital natives who may not have any coding experience in order to democratize content creation.

(23:29) – The technological progress or the leaps and bounds of automation make generative design come of age. Using AI to boost creativity makes anything possible and accessible.

(26:01) – New types of film will be generated and created. And creativity generates, potentially, new jobs. There is no match for human creativity. And this inherent desire to explore new places or explore new worlds, that's something that's very uniquely human and not replicable by a machine. 

(32:14) – Network effects are built into platforms, who want to get users in front of as many people, because that's how they drive ad revenues or eyeballs. Figure out trends that your product market fit, and then that platform creator fit that's working for you. 

(38:14) – The current conditions are opportunities to reinvent, to try new technology and to show that you as a human, can be part of a new wave. We're continuing to move forward into a world that could be without code, could be no code, low code. Build your creative muscle.

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Steven Shillingford is President and CEO of DeepSee.ai, a Knowledge Process Automation (KPA) platform to mine unstructured data, operationalize AI-powered insights, and automate results into real-time action for the enterprise. He is the creator of the Knowledge Process Automation industry category, delivering AI-powered automation and productivity via easy to deploy, cloud-based business flows for critical business operations in the Capital Markets and Insurance verticals. He has led several startup enterprises, building cloud-scale platforms and helped found a successful cybersecurity platform for big data analytics supporting network surveillance systems for a range of verticals, from intelligence agencies to Fortune 500 companies. 

Please support this podcast by checking out our sponsors:

Episode Links:  

Steven Shillingford’s LinkedIn: linkedin.com/in/steve-shillingford

Steven Shillingford’s Website: https://deepsee.ai/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

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Support and Social Media:  

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– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

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– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:31) – Innovation Cycles used to be about features, but now consumers and enterprises look for innovation around processes

(06:53) – Using AI to surface the information that is most useful through a configurable tool bias towards action.

(13:52) – NLP to support different tools for different types of business problems inside the enterprise

(16:29) – A hybrid approach where people need interaction to lead us to “enhanced accelerated productivity”

(22:26) – Reducing processing time to offload a non-human optimized work to the machine, keeping Computers working on behalf of the humans 

(23:42) – Operationalize data science and the innovation that comes from AI around outcomes to achieve knowledge, reduce cost, mitigate risk and improve customer satisfaction, not only in capital markets or insurance, but across a number of industries

(26:53) – A platform that matches unstructured data in different business models, but same processes. Automation of checkpoints by a machine using the Deepsee platform as in capital markets

(30:27) – Helping research get faster results. Streamlining paper processes to innovate in new therapeutics, new vaccines, medical supplements and medications, as well as the technology used for blockchain

(33:50) – More than document digitization it’s document and data analysis, preserving data provenance across all actions to build trust through transparency and achieve wide-scale adoption.

[Audio] 

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Oliver Schabenberger is the Chief Innovation Officer at SingleStore. He is a former academician and seasoned technology executive with more than 25 years of global experience in data management, advanced analytics, and AI. Oliver formerly served as COO and CTO of SAS, where he led the design, development, and go-to market effort of massively scalable analytic tools and solutions and helped organizations become more data-driven. 

Previously, Oliver led the Analytic Server R&D Division at SAS, with responsibilities for multi-threaded and distributed analytic server architecture, event stream processing, cognitive analytics, deep learning, and artificial intelligence. He has contributed thousands of lines of code to cutting-edge projects at SAS, including, SAS Cloud Analytic Services, the engine behind SAS Viya, the next-generation SAS architecture for the open, unified, simple, and powerful cloud. He has a PHD from Virginia Polytechnic Institute and State University

Please support this podcast by checking out our sponsors:

Episode Links:  

Oliver Schabenberger’s LinkedIn: https://www.linkedin.com/in/oschabenberger/ 

Oliver Schabenberger’s Twitter: https://twitter.com/oschabenberger?s=20 

Oliver Schabenberger’s Website: https://www.singlestore.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

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YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:38) – From forestry to statistics to Software development to advance analytics

(04:07) – To understand the data is not only to build a mental model, but a probabilistic model of how the data came about, and once that model is accepted, as a good abstraction, then it is used to ask questions about the world. 

(05:39) – Many of the assumptions into our established models and established thinking about industries and supply chains had to be questioned because of unforeseen events like the pandemic. Scenario modeling is not just making a prediction, it must also guide the decisions and the need to provide the right abstractions.

(07:19) – There is an approach steeped in mathematical statistics and probability theory. And a more computationally-driven approach which shows how computer science, as a discipline, changed its focus from focus on compute, to focus on data.

(10:34) – There are transactional systems, analytics systems, machine learning and data science, all somewhat based on existing technology purpose-built for a certain use case, and what we're seeing is the use cases coming together. These worlds need to come together through a data foundation where the workloads can all converge. Silos and empires that need to be connected.

(16:15) – The explosion of neural network technology over the last 15 years due to the availability of big compute and cloud computing has allowed to solve much deeper problems, and we need larger amounts of data to train those models. 

(16:33) – Modern AI, data-driven AI and machine learning applications recognize patterns. Neural networks are trained to detect patterns. The next generation of models might be more contextual or build out from individual component models where humans can interact with the system and understand how it drives its conclusion, and then correct it.

(20:35) – We need to empower all of us to work with data and to contribute to driving the world with data and driving the world with models more. We need to be more data literate. But we also need better tooling that allows low-code and no-code contributions 

(23:28) – The future of data science is decision science. 

(25:38) – We have technology at our disposal, that makes us “prosumers” who consume and produce at the same time. And data should be the same way. We should be able to produce what we need based on data, not just consume. 

(28:28) – Innovation is key to success in technology. Innovation is about turning creativity and curiosity into value, and value has to be tied to the core of what we do, core of the business, core of what our customer needs. 

(30:51) – The elements of building technology: connectivity, automation and culture.

(32:43) – Turn the data into decisions and drive the business, and that is SingleStore’s specialty.

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Brad Schneider is the Founder and CEO of NoMad Data. He was previously the CEO of Adaptive Management. Throughout his career, Brad has focused on using alternative data to improve decision making and prediction. Brad has been a Portfolio Manager at Tiger Management, and Managing Director at Jericho Capital, a $2bn AUM TMT-focused hedge fund. Prior to Jericho, Brad also worked at Palo Alto Investors as an equity analyst and was a co-founder and head of product development for InfoLenz, a predictive analytics company. Brad holds a Bachelor of Science degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and is a CFA charterholder.  

Please support this podcast by checking out our sponsors:

-Work Patterns: https://www.workpatterns.com

-Imagine Golf: https://www.imaginegolf.com

-Art of Manliness: https://www.artofmanliness.com/podcast/

-Keep Optimising: https://keepoptimising.com

Episode Links:  

Brad Schneider’s LinkedIn: https://www.linkedin.com/in/bradschneider/ 

Brad Schneider’s Twitter: https://twitter.com/bschneider222?s=20 

Brad Schneider’s Website: https://www.nomad-data.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

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YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:16) –A tech guy who started in the analytics space and moved to the world of investment, which led him back to the field of data

(02:33) – Building software over the years helped him, as the user of data, to more easily interact with that data and find ways to connect the use case to the dataset. 

(03:57) – NoMad Data's goal is at a high level to be the search engine for these datasets, making it a lot easier for people in the AI space, for researchers, for computer science, for marketers, for strategy professionals, consultants, investors, help them connect those everyday business problems that they have to real datasets.

(05:33) – Data that is more frequently purchased include credit transaction data and customs data, which allows to see trade flows 

(06:48) – Data sets are so powerful, but they're also so broad.Customs data set help to understand a single company on the aspect of one company or region and economic competitive wins and losses for factories. And because they're so broad it's very hard to describe on a webpage what this dataset can be used for.

(08:07) – The build vs. buy dilemma: it really depends on your timeline and the availability of the data you need. Even if the data we collected was a hundred percent accurate, it would become very challenging, because we wouldn’t have enough data points to even make a simple linear regression model. So, in a lot of cases, it's better to buy. 

(10:25) – Getting that data from where it started, whoever is creating it or whoever you're purchasing it from, and getting it somewhere that you can write that first query has historically been a bottleneck. Some services like Snowflake are creating these marketplaces where people are putting the data in a common database format.

(12:05) – It's hard to fully automate the data search process today, and the main reason being the data you need, the metadata about the data, doesn't really exist, and the term metadata is used very broadly. Cutting edge NLP and machine learning is used to find similar concepts.

(13:47) – The biggest change that the pandemic caused was really the need for data. Buyers are looking at more and more datasets to fill in the holes in their understanding. And because of the increasing number of those holes in their knowledge, there's been an increasing need for data.

(15:49) – Searching the area that we're focused on is one of the biggest problems holding back the market. People know they want to see something, they want to be able to calculate some statistics, but they don't really know the data that would provide the requirement to do that.

(16:33) – Companies need to be really pinpointed on what they focus on, and because people have a really difficult time finding the right data, finding the best data to address their use case, services like Nomad help unlock this industry, which ultimately means you bring more and more buyers into the market. 

(19:08) – Many of the companies today haven't given much thought to data as they have for software. The data revolution has already started. And the first step in that was companies looking at their internal data. The next frontier is external data or alternative data. It's these data sets that are coming from outside your four walls, and in a lot of different businesses, it gives you a perspective that you don't have. It gives you a perspective that isn't biased by your own internal processes

(21:00) – If you're a company where your brand is extremely important, you’d be more reticent to sell data because there's potential brand risk associated with doing that. We support anonymity on both sides of the market. In Nomad, they can post their data. It's completely anonymous.

(22:40) – Nomad has raised $1.6 million and that was led by Bloomberg beta and some other higher profile VCs as well. Some great angels in the data space.

(23:51) – As we get out three to five years, awareness of this space and interest in this space is going to explode in orders of magnitude growth on both the number of people selling data and the number of people buying data.

(24:40) – If you're a startup, NYC is a wonderful environment to be in. It's also helping a lot, that housing is coming down.It’s attracting more and more people. People that don't want to commute here don't have to anymore. It's going to be a Renaissance for the city.

[Audio] 

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Ashutosh Garg works with startups across the enterprise stack. He is particularly excited about how machine learning and deep learning are reinventing existing software categories and creating new consumer experiences. Ashutosh has invested in AI-enabled business applications (such as marketing technology and HR technology), data platforms, data center infrastructure, security & privacy, as well as online video. Before joining Foundation Capital in 2008, Ashutosh was the general manager for Microsoft’s online-advertising business and led field marketing for the software businesses. Previously, Ashutosh worked at McKinsey & Company, helping technology companies scale their go-to-market efforts. Earlier in his career, Ashutosh founded TringTring.com, one of the first search engines in Asia, set up Unilever’s Nepal operations, and led the marketing and pre-sales teams at Cadence Design Systems.

Ashutosh has a bachelor’s degree from the Indian Institute of Technology (IIT) in New Delhi and an MBA from the Indian Institute of Management at Bangalore, where he received the President’s Gold Medal.

Episode Links:  

Ashutosh Garg’s LinkedIn: https://www.linkedin.com/in/ashugargvc/ 

Ashutosh Garg’s Twitter: https://twitter.com/ashugarg?s=20 

Ashutosh Garg’s Website: https://foundationcapital.com/member/ashu-garg/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

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YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:31) –Eightfold.ai was created in 2016 as a talent intelligence platform that is being used by the leading enterprises across the globe to hire, engage, and retain a diverse workforce.

(04:21) – Large enterprises’ number one challenge is people. They are not able to hire fast enough. Enterprises should think about diversity, about their own biases, to understand what talent exists. We added exits to bring the right people on board and that is where data and AI comes into play.

(05:43) – We can't keep looking for people who have done the work. We have to look at the people who can do the work, and that is a fundamental shift in the mindset.

(09:00) – We need to reach out to the people who may not have had all the privileges that we have and support them. We have to look at people beyond what we perceive for their face color, age.

(10:14) – Machines have the ability to forget and ignore. We have our biases because of the lack of knowledge. Knowledge and moving out of biases can really help us solve this problem when hiring candidates.

(11:59) – There has to be an audit process to ensure that your algorithms are not going crazy and that they are doing the right thing. Let's use them to help humans do a better job. 

(13:53) – It's all about humans. These systems are designed to come in and replace humans. In that case, not only are you taking the snitch system correctly, you're teasing that: I really don't need to worry about humans, and that has to be front and center.

(16:00) – One of the things Eightfold believes is that it's not that people are good or bad, or one is better or worse, but who is the best fit for which flow in that company.

(18:24) – You have to really assess the people at their full potential.

(22:32) – What Eightfold.ai is trying to do through machines is help hiring managers understand that candidates past, be able to dig deeper with you, look at the peer group of the community to see what their peer group is doing today.

(25:27) – Some of the success stories of the companies that we know today in the world come from combining experience with young talent. 

(27:26) – The talent market rate landscape is completely going to go through a massive shift in next 18 months. This is also a good time to hire great talent, because many people are looking up.

[Audio]

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Robert Stevenson is the Chief Executive Officer at INTELITY. He is a business and technology executive with 20 years’ of rich experience across a wide array of disciplines. Robert specializes in the productization, strategy and market delivery of new technologies. In addition to undergraduate studies in Design and Computer Science, Robert holds an MBA from the Schulich School of Business at York University and the Kellogg School of Management at Northwestern University, including work at the Hong Kong University of Science & Technology.

Episode Links:

Robert Stevenson’s LinkedIn: linkedin.com/in/robertstevenson

Robert Stevenson’s Twitter: https://twitter.com/intelity?lang=en

Robert Stevenson’s Website: https://intelity.com/

Podcast Details:

Podcast website: https://www.humainpodcast.com

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

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YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators

– Twitter: https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline:

Here’s the timestamps for the episode:

(00:00) – Introduction.

(01:45) – Hospitality Tech has been reluctant to embrace the latest and greatest technologies.

(03:28) – INTELITY is a mobile platform being built to modernize the guest experience.

(05:36) – INTELITY customer segment and customer ecosystem and market is that 80% who are not major hotel brands.

(08:09) – INTELITY has been conceived as a B2B2C.

(12:41) – How the pandemic stroke Hospitality industry but leveraged a long-expected change.

(13:53) – Mobile experience and automation to improve the market.

(14:53) – Using AI and data to drive revenue.

(18:31) – Using AI and data to predict customers behavior and offer a better service.

(19:59) –Automate the experience to elevate the guest and improve the travel P&L for the hospitality space.

(21:17) – The voice space in hospitality has been slow to customize and adapt these tools.

(23:54) – Mobile technology has led the way, but major changes will emerge in mobile computing devices.

(27:56) – The power of the devices will continue to get stronger, better and more demanded.

(28:38) – The trend will be to see new hotel apps rolling out to promote contactless experiences because of COVID.

(29:55) – The hospitality industry needs AI and Machine Learning to adapt to customer needs.

• Claire Leibowicz currently leads the AI and Media Integrity program at the Partnership on AI. She holds a BA in Psychology and Computer Science from Harvard College, and a master’s degree in the Social Science of the Internet from Balliol College, University of Oxford, where she studied as a Clarendon Scholar.
• Not only tech companies should be involved in creating good, responsible, ethical AI, but also civil society organizations, academic venues, other parts of industry and especially media.
• AI and media integrity proposes a very simple way to have good, healthy, beneficial information online by using AI systems to do that. 
• Not everyone agrees what type of content should be allowed online. Even humans don't agree about what misinformation is or what content should be shown to people through technology. Some tech companies feel empowered to take comments off platforms. So, not only just to declare a label or more context around people, but really to take a public figure off a platform, which is really an emboldening of platform agency in contributing to who is allowed to speak and who's not.
• In terms of tactics for misinformation, how people create misinformation, how they spread content, is generally applicable to social media. There's misinformation flowing in WhatsApp groups, in texts, in all these different venues. There is a real movement towards this kind of misinformation that's not just total misrepresentation of an event or a fact, but a slant or a leaning, or a caption that may make a post have a different connotation than it would if it was written by someone else.
• AI and Media integrity seeks to reach a public that can distinguish credible information from misleading information. Labeling is an interesting, almost in-between option, because it's not limiting speech or saying you can't share this post or saying someone's information shouldn't be seen. It's giving you more context. The idea is to find a middle ground for platforms to seem like they're giving the user control and autonomy, and being able to judge for themselves what's credible. 
• Some people are really skeptical about platforms. Labels might encourage major division in user attitudes between those who think they're important for people to be healthy consumers of content and those who find them biased and partisan and error prone. Automating that label deployment is really complicated. And we don't really know what the best intervention is right now to help bolster credible content consumption. 
• With the de-platforming of Donald Trump, we're living in a new society where we are giving the rights of freedoms to platforms to say, we can get content so that we're providing the best interest for our users without acknowledging whether the users really want that.
• The platforms have been emboldened, and that has a connotation that we're going to become the arbiters of truth. Those who value free speech and principles might frown upon, since the internet was founded as a venue for democratizing speech and allowing people to speak. There are other solutions that the platforms can take to change how content gets shown beyond just labeling. Platform labels alone are insufficient to address the question of what people trust and why there is this general distrust, in the principle of platforms to self-regulate and for fact-checkers and media companies to offer non-politicized ratings. 
• We need to better design interventions that don't repress people, but really respect the intelligence and autonomy that has raised awareness of looking into a source and media literacy. 
• So holistic, digital literacy, educational interventions to focus community-centric moderation,. And that people in the community rather than the platform itself, are the ones doing the moderation, which might increase trust in how the speech is being labeled and ultimately decided upon.
• A lot of the policies that platforms have about speech on the platforms have to do with the way in which they cause real world harm. You may have a policy that says we don't label speech, we don't do anything until there's a perception that post might prompt real-world harm. Manipulated media is basically any visual artifact that has been altered in some way by any means, and whereas there's no harm to the public square, there might be harm to other types of political speech or those that are misleading. So when we talk about manipulated media, it's really important to underscore what makes that misleading or problematic. So a lot of people have advocated for AI-based solutions to deal with manipulated media. 
• It's not just how an artifact has been manipulated that matters. It's partially the intent, why it's been manipulated and what it conveys that really matters. Just because something has been manipulated doesn't mean it's inherently misleading or automatically misinformation.
• But rather, what's the effect of that manipulation. And that's a really hard task for machines to gauge, let alone people. 

Shownotes Links

https://www.linkedin.com/in/claire-leibowicz-17156a65/  

https://twitter.com/CLeibowicz   

https://www.partnershiponai.org/manipulated-media-detection-requires-more-than-tools-community-insights-on-whats-needed/    

https://medium.com/partnership-on-ai/a-field-guide-to-making-ai-art-responsibly-f7f4a5066ee  

https://arxiv.org/abs/2011.12758  

https://medium.com/swlh/it-matters-how-platforms-label-manipulated-media-here-are-12-principles-designers-should-follow-438b76546078  

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

- Eric Daimler is the CEO & Co-Founder of Conexus.com. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage.

- Daimler believes the Obama administration made big efforts to bring in more technologists into government for innovation and digital modernization, and is optimistic that sensibility around a digitally native environment will be expressed inside of the Federal Government, and continue to trickle down into states' governments for the benefit of all. 

- Human failure has come before machines got trained on human failures. Therefore, technologists can't use massive amounts of data on every human problem and expect to come out with mind blowing results. So there's limitations on technology. What can be done is to transform these whole domains of knowledge and map them onto others through a new type of math.

-There's a discovery in this domain called category theory. Categorical mathematics, category theory, is really at a level above all those other mathematics that transforms a problem from geometry, into another problem called safe set theory, applying it to databases. The math of category theory changes how we relate to data. This is “the math of the future”.

-It's at a higher level of math, a level of abstraction to model the world in which companies operate their business, and make bigger decisions better and faster, reasoning large amounts of data at a higher level to power a whole new change in our environment, as business people, as academics, as citizens. 

-Daimler suggests three ways to solve data issues: matching data in a unified database, create a silo and then they sell a subscription to data silos and data interoperability math analysis through category theory.

-AI definition has been misinterpreted over the years as algorithms that collect data and have machines do stuff, when in reality, AI should be understood as a system that senses plans, acts and learns from the experience. And it senses plans and acts from inputs that are given to it. 

-Not everyone needs to be a programmer in a basement. People need to be playing a multitude of roles. There's not just a choice between computer science or an English degree. What the current world of tech needs is policy considerations, places to get involved, and a way to focus educational efforts. Automation doesn't mean no human intervention. Societies benefit by that exchange of ideas and communication of values.

Shownotes Links: 

https://www.linkedin.com/in/ericdaimler  

https://youtu.be/YP9kodLGvT8  

https://youtu.be/jqn4wnSBKuE  

https://youtu.be/c92rK_UZaXU 

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

Show notes:

• Steven Umbrello is the Managing Director at the Institute for Ethics and Emerging Technologies with a research focus on responsible innovation and the ethical design methods for emerging technologies. His work focuses on ethics and design thinking around building AI systems, and how policy can shape the future of these autonomous systems. 
• Ethics clarification of what would normally be abstract, philosophical concepts like human values to engineers can be implemented into design requirements. Design has to be approached so that engineering can incorporate human values, which are often abstract, into technological design.
• The difficulty with AI and with many technologies in a globalized world is that technology can be developed in X, but unfortunately, that technology has cross-cultural, cross-domain, cross-border impacts. So, it's about trying to incorporate different understandings of values from across the globe into a single technology. These are some of the difficulties that designers are facing right now.
• Technology is not purely deterministic. Nor is society purely constructive and nor is technology purely instrumental. It's just a neutral tool. It doesn't embody any type of values whatsoever. And that really is important, because that means that the decisions that engineers make today, as designers, philosophers, do have a real substantive impact into the future.
• We can begin to really break down the debate on whether we should ban or not ban autonomous weapon systems. Technological innovations have always played a key role in military operations. And autonomous weapon systems, at least within the last few years are receiving asymmetric attention, both in public and, as well, academic discussions.  
• Scientists should not apologize for, but show the nuance in debate that level five autonomy in and of itself is not the problematic point of interest, but rather what type of system has this level five autonomy. There's all these assessments. This is the nuance in the debate. 
• For those who are interested in the philosophical foundations of meaningful human control, or even value sensitive design more generally you can find my work on my website and my social media. If people are interested in following the debate on the prohibition of AWS they can watch many of the online multilateral meetings, both hosted by the UN and outside their auspices as they take place. People can check out Human Rights Watch and the Campaign to Stop Killer Robots for news on these events.

Show notes Links: 

• https://www.frontiersin.org/articles/10.3389/frobt.2018.00015/full 
• https://www.hrw.org/
• https://www.stopkillerrobots.org/

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

• Jordan Tigani is the Chief Product Officer at SingleStore. He was the co-founding engineer on Google BigQuery. He also led engineering teams then product teams at BQ. 
• SingleStore powers Comcast with their streaming analytics to drive proactive care and real-time recommendations for their 300K events per second. Since switching to SingleStore, Nucleus Security converted its first beta account to a paying customer, increased the number of scans Nucleus can process in one hour by 60X, and saw speed improvement of 20X for the slowest queries.  
• To be more competitive in our new normal, organizations must make real-time data-driven decisions. And to create a better customer experience and better business outcomes, data needs to tell customers and users what is happening right now. 
• With the pandemic accelerating digitization, and new database companies going public (Snowflake) and filing IPOs (Couchbase), the database industry will continue to grow exponentially, with new advanced computing technologies emerging over the next decade. Companies will begin looking for infrastructure that can give real-time analytics -- they can no longer afford to use technology that cannot handle the onslaught of data brought by the pandemic. 
• True Digital in Thailand utilizes SingleStore’s in-the-moment analytics to develop heat maps around geographies with large COVID-19 infection rates to see where people are congregating, pointing out areas to be avoided, and ultimately, flattening the curve of COVID-19. In two weeks’ time, SingleStore built a solution that could perform event stream processing on 500K anonymized location events every second for 30M+ mobile phones. 
• Businesses need to prioritize in-app analytics: This will allow you to influence customer's behaviors within your application or outside of it based on data. Additionally, businesses must utilize a unified database that supports transactions and analytics to deliver greater value to customers and business. 
• Enterprises must access technology that can handle different types of workloads, datasets and modernize infrastructure, and use real-time analytics.

Shownotes Links: 

- https://www.linkedin.com/in/jordantigani 

- https://twitter.com/jrdntgn  

- www.SingleStore.com  

- https://www.linkedin.com/company/singlestore/

-https://www.singlestore.com/media-hub/releases/research-highlights-spike-in-data-demands-amid-pandemic/  

-https://www.singlestore.com/media-hub/releases/businesses-reconsidering-existing-data-platforms/   

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

• Jeff Wald is an Entrepreneur, Speaker and author of the book “The End Of Jobs: The Rise Of On-demand Workers And Agile Corporations”. Wald has started three technology companies, the most recent, WorkMarket , sold to ADP, is enterprise software that enables companies to organize, manage and pay their freelance workforce. He is also a Board member to other companies with an expertise in audit, governance and cyber security. 
• Robotics, AI and technology as a whole are the key factors in what’s being called the fourth Industrial Revolution. Wald mentions three phases: fear-mongering, where society believes all jobs will be automated, dislocation, when job losses occur, and finally, changes in the way of work and society’s standard of living.
• New technology doesn't replace existing jobs. Companies, workers and society adjust differently to changes in labor, but eventually, that transition is slow and social and economic dislocations do happen, but not immediately. Plus, from a technology standpoint, there is a need for customer service and a human factor which cannot be disregarded.
• The pandemic has definitely impacted the labor market, but is a complete guess what the outcome will be in a post pandemic world. Economic growth can be predicted, but only as the economy recovers, real estimations could be made related to unemployment rates.
• The hard tech jobs are growing even through the pandemic, and they will grow post pandemic. They were growing pre pandemic. The pandemic is not impacting that. But hard human jobs, those that involve human connection are also predicted to grow because computers and AI systems can’t do those jobs. Automation is easily applicable to those jobs that are repetitive, high-volume, task-driven jobs.
• Remote and flexible work have also been growing due to the pandemic. Companies had been reluctant to change their mindsets, infrastructures, policies and procedures for remote work. But now that they've been forced to do it, there is a great number of people who prefer working under the current work arrangements. Not meaning that workers will never again be at the office, just less often than prior to the pandemic, and more frequently than now, pursuing human interaction. But no prediction is accurate until vaccination can really incide in variants.
• Everyone needs to become a lifelong learner, constantly upskilling in industries that will continue to grow or rescaling because an industry is at very high risk of automation and displacement,
• https://www.amazon.com/End-Jobs-Demand-Workers-Corporations/dp/1642934356/
• https://www.jeffwald.com/

Episode Show Notes: 

• Robert Scoble works with companies that are implementing Spatial Computing technologies
• Robert is a futurist and technology strategist and the author of four books about technology trends, being the first to report on technologies from autonomous vehicles to Siri. 
• Previous positions held by Robert include being a strategist at Microsoft, a futurist at Rackspace, Chief Strategy Officer at Infinite Retina, and the producer and host of a video show about technology at Fast Company.
• Spatial computing is the next step in the ongoing convergence of the physical and digital worlds. It does everything virtual-reality and augmented-reality apps do: digitize objects that connect via the cloud; allow sensors and motors to react to one another; and digitally represent the real world.
• Spatial computing will soon bring human-machine and machine-machine interactions to new levels of efficiency in many walks of life, among them industry, health care, transportation and the home. Major companies, including Microsoft and Amazon, are heavily invested in the technology.    
• Is computer vision about to change everything (and already is) and what should business people do to prepare for the changes that will come in 2022? How will the war between Facebook and Apple go and why we will soon give a LOT more data to these big companies?
• Call to Action: Learn about spatial computing and how it will roll up all the other AI advantages into a new way of computing.

Links: 

- https://twitter.com/Scobleizer  

- https://www.linkedin.com/in/scobleizer/  

- https://varjo.com/  

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

Episode Show Notes: 

• Geoffrey Horrell was the Head of Refinitiv Labs, London and currently the Global Head of Innovation and Labs at the London Stock Exchange Group. 
• This episode unpacks key trends from Refinitiv’s new global research report ‘The Rise of the Data Scientist’. Data scientists are moving across teams and realizing (and scaling) new opportunities for AI across their firms - e.g. NLP. In addition, 75% of firms are now using Deep Learning while 17% of firms rely solely on unstructured data for AI/ML use-cases. 
• Data strategy is set to overtake tech strategy in importance. There is a further evolution of teams and talent across finance - more citizen data scientists in trading, investment teams etc.
• Data scientists are going from supportive to strategic roles as investments in tech and talent put data strategy in the spotlight. COVID-19 is upsetting ML models where you need to recalibrate and calculate disruptive events. 
• NLP use-cases are going into production and impacting accuracy with more advancements increasing the need for ‘explainability’  
• Models, data and people are prepared for tomorrow’s unexpected shocks - COVID proved that models need re-calibrating / new data to calculate disruptive events (e.g. data enrichment). 

Learn More about Refinitiv Labs:

• https://www.refinitiv.com/mlreport2020 
• https://www.refinitiv.com/en/labs 
• https://www.refinitiv.com/en/artificial-intelligence-machine-learning

About HumAIn Podcast

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

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Sagi Eliyahu is the co-founder and CEO of Tonkean, a next generation business dashboard that connects the dots between the tools organizations use every day and the insight only teams can provide. With Tonkean, Sagi seeks to help companies of all sizes and types simplify and automate the process of staying updated on the most important details they need to more successfully manage their businesses.

Episode Links:  

Sagi Eliyahu ’s LinkedIn: https://www.linkedin.com/in/eliyahusagi/ 

Sagi Eliyahu ’s Twitter: @esbsagi

Sagi Eliyahu ’s Website: https://tonkean.com/  

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

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Support and Social Media:  

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– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:39) – Tonkean enables more people to use software. And what impact would that have on enterprises and business in everyone's life? That's what we're all about. 

(02:43) –  We joined from acquisition and grew the team there from a handful of people to over 150 people. Even though we had all those great tools in place and the top CRMs and the top project management tools like most companies, it didn't feel like it helped to force people into those softwares. You look at the CRM, you don't have the information that you need. You look into the project manager system, it's not there. So I tried to hack those systems together, trying to connect them together with the likes of integration platforms.

(04:24) –  The biggest moment for me was to realize that business processes are actually not about data, they're about people. But software in enterprise is almost a hundred percent about data. How do you actually go about using technology in a process that is very dynamic, very asynchronous and very human-centric? And the answer is that you didn't actually have anything to do that for you. That sounds like a big enough problem to pursue. So that's when we decided to start Tonkean. 

(06:01) – We call it the Operating System for business operations. It's really abstract into the complexity of business processes, which are human-centric, highly dynamic, highly complex, simplifying it to non-coders business professionals, operation teams like sales operations, marketing operations, legal operations, and general operations, and so on, to be able to build their own solutions that are across a process, not necessarily creating a new app where you can view and manage data, but actually streamline a process end to end across different systems and across different teams.

(09:28) – When the pandemic hit and everyone moved in almost overnight, it was really quick to be fully remote. I always had a remote team or more accurately a team that is, like you said, distributed on both sides of the globe. Everything is more measured, and not because we want to, because we're forced into it. All that coordination and all that work that was not in the spotlight becomes more in the spotlight because we're remote, and because everyone is remote. That definitely pushed a lot of the automation world in a lot of our sort of human-centric processes world to the top of mind, because now you can see how much of the work you actually do every day. And all of us are not necessarily in systems. It's between us people and how much of it is manual.

(12:29) – One of the big things we're pushing forward is a concept we came up with, which is people-first process design. It's not even about what technology you have. We also believe that most companies and most people misuse technology in the way that they even structured the processes. 

(15:11) – If we're not designing the process into their strength, then we're actually replacing one inefficiency with another. And that's kind of where we strive to help operation teams. They know the process, they understand it, provide them with a tool set, with a platform where they can actually create efficiency on top of existing systems and on top of existing behaviors. 

(16:26) – There's the personalization for the role. What is important for that role? What is important for that team? What are the things that work well and what are the things that are not working well as part of this end-to-end operation? 

(17:47) – Work is more global. And to get the best case scenario, outcome, you need to actually leverage everyone. And that is something that I feel our platform allows to do, but more of that, the movement of no-code and low- code release, all about enabling more people to create more solutions that are more customized for their own processes, their own team, their own company, versus buying packaged solutions off the shelf.

(18:55) –  No Code and Low Code are both playing on the same, call it a wave of future improvement and future next steps off software. So for many reasons they are in the same global area, but at the same time, they're night and day, they're actually very different. Low-code, by definition, is the ability for developers to do more things with less code, but it's a low code because you still need to code. And even if you're not writing scripts, like Python or any other coding language, you still expect that to be a developer mindset and skillset. The low code movement allows you to move faster. So it's basically saying the same people that can code today can code faster.

(20:39) –  No-code is about expanding the pie, making the pie bigger of people that can actually build things. So it's, instead of saying, you can do more things faster, it's saying more people can do more things. And why that is important is because if you think about the impact of technology and the growth of technology over decades and over many generations, any duration in software specific to the big leaps do not come from making things faster. Those are linear growths. The big things come from opening the door for different new people that can now code.

(22:15) –  With Tonkean, we believe that operation ops people, again, sales ops, legal ops, finance ops, professionals that understand processes really well, they understand what needs to happen and why, and what's important, but they don't know how to code. So they don't even understand how API works or well enough to create mission critical solutions.

(22:54) – If you give them low code, it's not very useful for them. They can do toys, they can do small things that create small impact, but they will never be able to build huge complex systems with low code because the gap is not in the speed. The gap is in the knowledge that they come with. With Tonkean, being fully no-code, we focus on those business processes segments and they've created them to be fully no code in the sense that you don't need to be a developer in your mindset.

(24:50) – There's always going to be the need for implementers and the need for architects. To be an architect, you would need to still be the technical person in that case, that understands how networks work and how data flows. Tonkean is a bridge between tech and IT, it incorporates engineering with the operation teams. And empower the operation teams and business analysts to implement their own solutions.

(27:15) – 95% of all IT and operation teams have already adopted or planning to adopt in the next 12 months a no-code or low-code solution. The need for efficiency in those departments was always there.  What we're seeing now is the movement from personal productivity to operational efficiency.

(33:47) – We're focusing mostly on large enterprises these days. So  there's a lot that we're going to add on from that perspective as well. And being able to, like I said, allow people to build true missions, critical processes and things that run for a long time.

(35:02) – Get educated on what's out there. There's a lot of great technology that is very complimentary.  There's a lot of noise marketing wise. A lot of things seem or sound the same, but that's because the opportunity is so big. And there's so many things that we took for granted over the years.

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Ken Grohe is SVP & Chief Revenue Officer, Taos. Additionally, Ken Grohe has had 3 past jobs including SVP & GM at Barracuda Networks. He got a BS in Business Management from Boston College.

Episode Links:  

Ken Grohe’s LinkedIn: https://www.linkedin.com/in/leveragegtm/ 

Ken Grohe’s Twitter: @LeverageSignNow (suspended)

Ken Grohe’s Website:https://www.taos.com  

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

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YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:27) – WEKA as you probably know, and some of the folks that might be data scientists listening in, they had to strip a wekabite. So it's 10 to the 30th power. That's a good way to future-proof it. It's all you can fit in a file system. A new way to do storage. It's all software, it's all service subscription through the people you're buying from every day. So if you run it through AWS in the cloud or on premises with Hewlett Packard, it's a great way to get things done and solve big problems. What WEKA is, is a modern and limitless parallel file system, that's easy to deploy any scale in the cloud or on premises for the people in the data center, solve big problems.

(05:15) –  71% of corporate data goes unused, despite how much money was spent to create this information data. And it's going on use. So that's amazing. So the average sale for us is a petabyte and that's two thirds of the time. It's on premises. One third of the time, it's in the cloud every time to go between the two.

(08:12) –  I can certainly think if you're in a university and at the end of the day, you want an AI project and I'll cut to the chase, not just for the greater good, but to recruit great talent. So when you're doing that and you're recruiting that type of talent, you're putting it into action. And that's probably going to be on premises. We allow you to put the right data at the right place at the right time to get, manage your information across the entire life cycle. So you make the money when you need it, and then you don't lose it when you really want to protect it for data protection.

(13:09) – Where you live in your hat in a COVID world, doesn't matter. They kept going. When you think about it, traditional Hollywood shut down during the beginning of COVID. Because you couldn't break the unions, you couldn't get the talent, the labor, they, Brad Pitt's Reese Witherspoon's to go on site, you couldn't see, you couldn't create any of the content we watched. The tiger came and things like that. But what I've told students able to do is enable them to create content. The need to have a parallel modern file system with no limits, no compromise. It was so important because you're going to bring all these engineers and all these scientists, you want to make breakthrough discoveries.

(16:22) –Some early in the career, 20 something, it says what am I going to do with the rest of my career? I heard AI is great. I'm telling you now the chief data officers are to learn. And as part of it, you may not earn that job right away, but think about, and put this individual's going to be, and typically they've come from the HPC high performance compute environment or the academic environment. So what's happened is a title has risen. It's called chief data opposite. Some of it is compliance and there's certainly a chief compliance officer in there, but more important, more exciting is building out new applications that grab market share and new revenue streams using that.

(20:28) – Storage is going to have a Renaissance or is we're living in right now, part of AI. 

(24:53) – I see three different paradigms. GPU's being prevalent. NBME being everywhere in the network, but especially in the GPU and the server itself.

(27:27) – All the intended AI practices and initiatives, it was going to be a fallout that over 50% of them were not going to have ROI. And that's unfortunate. Now that number has shrunk to less than 12% per the analysts we spoke with yesterday. You never want to have strengthened aptitude and intelligence, but you don't have the ability to use it at that time. So the pro file system lets everyone use it all the time. We take care of the locking and the overriding, all the other management is part of it. 

(29:52) –  You can start as small as you want and go as large as you want, but bring the ability and the imagination to solve big problems. Because storage and more importantly, AI centric accelerated storage from WEKA is certainly huge. And I love I'm going to use an ops shoot of your bottomless. I'm going to call it limitless. So it's kind of the solutions of limitless.

(31:01) – You want the right data at the right place at the right time. No in all the cases. So you can capitalize, you can make, go faster and go actually press your advantage. And wherever it might be, whether it be retail or manufacturing. The reason I say extensibility is for naming conventions, whatever file you create, you want that same name and convention whether you're on premises, we on a cloud, we were an object store or whatever. And what's great about WEKA.

(33:03) – The fast, eat the slow. If that's the case, the ability to move the correct data, the right naming conventions based on the right policies, the right security allows you to happen. So we're kind of known as the information life cycle management type company. 

(34:14) – We were involved with vaccine development, obviously with those, most of those vendors did all suppliers do that through the cloud and we have a solution for them in the cloud but ultimately a hybrid solution as well.

(39:51) – We're not a very sales dominant culture. We're all about solving big problems and very technical by nature to get into your use cases. In fact, most of our people spend most of the time trying to move data scientists or people represent data scientists. So if you're in that category, we'd love to help you out.

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Dr. Vasco Pedro is the co-founder and CEO of Unbabel. He owns the vision, overall business strategy and sets the direction for Unbabel’s product development. Responsible for the company’s culture, Vasco is heavily involved in recruiting and spearheads Unbabel’s fundraising efforts, which total USD$91 million in venture capital to date. He is a leading presence in the burgeoning Lisbon startup scene, with Unbabel known for being the first Portuguese company to be accepted into the Y Combinator accelerator program.

Vasco received his Ph.D. in Computer Science in May 2009 from Carnegie Mellon University (CMU), working with Jaime Carbonell and Eric Nyberg. His thesis, titled “Federated Ontology Search,” focused on developing new methods using ontologies (a set of concepts which compartmentalizes variables for computations and establishes the relationships between them) in large scale data-processing scenarios. From 2001-2009 he was a Research Assistant at the Language Technologies Institute, contributing in the field of Question Answering (a computer system capable of answering questions posed in natural language), alongside the team that eventually went on to create IBM’s Watson. Vasco was a Fulbright Scholar, 2001-2005, and was awarded a scholarship from Fundação para a Ciência e a Tecnologia, Portuguese Foundation for Science and Technology (FCT), Ph.D. Scholarship, 2006-2010.

Episode Links:  

Vasco Pedro’s LinkedIn: https://www.linkedin.com/in/vascopedro/ 

Vasco Pedro’s Twitter: @justvasco

Vasco Pedro’s Website: https://unbabel.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:30) – We need to create a new version of the translation service that blends artificial intelligence and humans in a number of different varieties to provide just this very simple, straightforward API for translation. That was the original idea. 

(04:21) –  Companies are pressured earlier to be able to serve multiple markets. And as you expand to multiple markets, you face the fact that people in that market will speak a different language and I need to be able to serve them.

(06:49) –  Our goal is to build the language operations platform that enables every enterprise to seamlessly scale across languages. And a big part of that is the full stack that we've built on translation and different components of AI, quality estimation or anonymization, or the actual interfaces for humans to translate and all the different components.

(08:43) – AI will have the biggest impact in areas that are highly commoditized and require a lot of human effort. A lot of humans can acquire the knowledge and the skillset to do translation and to do transcription. Overall, AI is not replacing humans, it is augmenting humans. And it's enabling humans to be more productive as a tool, so far.

(10:43) –You will need a smaller amount of human effort per unit, but that human effort overall would be more valuable, because it translates into a higher value. I don't see, unless you're talking about very basic repetitive tasks, I see the real value is in this interaction of being able to give the boring task to AI and to let the human do the higher cognitive load function type of tasks. 

(15:10) – We started by focusing on customer service and the drive behind that was a number of things. One, conversational interaction is particularly suited for enabling AI to have a large impact. There's this sense of almost the inequality of customer service, depending on language.

(16:59) – We're still focused on text, chat and email, but in a way that I, as a customer service agent, don't have to really care about the language you're talking. You, as an agent, focus on being an amazing customer service agent and really understanding your product and providing that level of customer service. And we act, we sit in between to make sure that that communication happens at a high quality human level on both ways, both from the customer to the customer service agent and vice versa.

(20:07) – Unbabel is a platform and solution for language operations that relies on multiple things. So the portal is really the product that the LangOps use to implement, manage and scale the translation layer. This is powered by the underlying platform, which is the actual bit that does a translation and would set up pipelines. And that's where a lot of the AI and human work combined to provide fast, scalable, robust and high quality translations.

(24:13) –  The digital-first world that we're accelerating into, and despite all the very, really bad things that the pandemic brought, that's probably the silver lining in terms of accelerating into the future, highlighting the need for that, for the ability to overcome language challenges. It's very clear that even in Unbabel, which is a company that's focused on eliminating language barriers, everyone that we hire needs to speak English, because otherwise we can't really communicate yet at the level that we do, we need to do. You're now really being able to overcome physical barriers, but still have some sort of pseudo physical presence. And so the glaring barrier becomes language. If your appearance and location are not an issue for communication, then, really, the language that you use becomes the number one barrier for it.

(29:34) – Conversational is still going to be, you mentioned the interface, but it's going to expand more beyond text into voice, which was pioneered in the media is going to migrate into a lot of business use cases because we were forced to do it.

(31:49) – If you're a consumer, don't settle for bad customer service, just because they don't speak English.

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Jacqueline Nolis is a Data Science consultant, who helps companies like T-Mobile, Expedia, with their data science problems.She’s got an undergrad in math. Masters in math. She got a doctorate in industrial engineering and then started working as a consultant. For the last ten years she’s been doing data science consulting for all sorts of companies and leading data science teams.

Emily Robinson studied very related fields of statistics. And that's where she started programming in R, went on from there to get a Master's in organizational behavior and then did Metis, which is another data science bootcamp.Went on to Etsy DataCamp. And now she is a senior data scientist at Warby Parker. She got interested in data science because quantitative social sciences are a very good background to lead into data science.

Episode Links:  

Jacqueline Nolis' LinkedIn: https://www.linkedin.com/in/jnolis/ 

Emily Robinson’s LinkedIn: https://www.linkedin.com/in/robinsones/ 

Emily Robinson’s Twitter: @robinson_es

Jacqueline Nolis' Twitter: @skyetetra

Emily Robinson’s Website: https://hookedondata.org/ 

Jacqueline Nolis' Website: https://jnolis.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(04:08) – There's just, clearly, some desire in the world that people are data scientists, or if you're a junior data scientist, a desire in the world to be one of these senior data scientists, giving talks at conferences and joining the community. And so we just noticed organically that this is happening more than us making some grand observation about the state of the world.  You bring up  the current moment also recognizing, how May I become even more valuable to employers? I may end up having to do a job search. What can I do to prepare so that I can be an attractive candidate to different companies? 

(06:23) – The book was put up into four parts, and the first part is, basically, what is data science? What does it look like at different companies? How do you find jobs? What does the interview process look like all the way up to negotiating an offer? So that's the first half. The second half of the book, and the third part is around settling into your job. Putting a machine learning model into production. And dealing with stakeholders. And then, finally, the last half is about when you start settling in it's about continuing to grow by joining the community, handling failure, which is pretty much inevitable when you're a data scientist going on to a new job. And then the final chapter is what are the things you can do even after you become a senior data scientist. So Management, independent consulting or being a principal data scientist. Finally, actually we have an interview appendix with over 30 interview questions, example answers.

(08:51) – No one really knows what's happening. No one, or for the last two months, no one really knows what happened. No one knows what's going to happen for a while. That we're just in a really uncertain time. We don't know if your company is going to be around in six months, everything's more uncertain.

(09:57) –A lot of companies are putting on hiring freezes in general, except for very critical roles. 

(12:18) – Each one of those stakeholders has a different goal, whether it's to make their engineering stronger, to make better decisions, to make their company go to a better place in the long term. And how you work with each one of these groups of people really will differ based on who they are and what their goals are. So we break down that a lot. 

(15:40) –  Some of the key communication strategies include messing up a lot until you remember how you messed up the last time, and then get a little bit better. And you do that for 10 or 20 years. And eventually you're okay. Being consistent. Creating a consistent framework for how you share things. You have to adapt your strategies.

(18:01) – The idea of how you prioritize this work thinking through a lot of the prioritization and deciding what work to do when that's really important to good stakeholder management.

(19:43) – Failure can come in all shapes and sizes. For me, I find one of the most difficult types of failure is that when you're a data scientist, you generally have to get people excited about a project before it starts. You have funding from people, and then you start working with the data. And it turns out that data doesn't have a signal in it. If you can't find it with a simple model, you're never going to find it. And that's a really big source of failure in the data science field. 

(20:54) – So it's also worth thinking about, as a team, maybe not taking on only pie in the sky, very high risks, new cutting edge projects and balancing that with things that you're more confident you can deliver because that can help show people the value of the team. And then, hopefully occasionally, one of those riskier projects does pay off and it will probably pay off in a bigger way.

(22:38) – A lot of the work you need to do to handle a failure really starts long before the failure actually occurred. Companies do have different cultures around failure, and at some places it's not seen as valuable, you might be punished for it.Try to understand if that company has a culture of learning and ongoing feedback, because you do want to be at a place where it can be safe and understood that sometimes things do fail. Startups are more comfortable with failing fast and frequently because startups are lean and exciting.

(27:40) – These softwares to monitor their employee's computers, which will take screenshots every 10 minutes, it hugely invades privacy. You should know what outcomes you're striving for. What success looks like there, trust your team to do the work well, to give them the flexibility. We're not just working remotely, we're working remotely in a pandemic. And having that human understanding that people are going through different stuff.

(35:57) – I am a big component, a proponent of doing public work. In my free time, I've picked up art. So I've been doing a lot of watercolor and oil pastel, and it's been nice to just have something that is totally not tech to put a little bit of my heart into.

(43:05) – At the current moment, it's certainly riskier to leave without another job lined up. You could just ditch the system entirely and become a consultant and work as a freelancer, which is what I've been doing, which can have a huge payout and huge opportunity, but also is incredibly stressful, very risky, and just almost impossible to do right now, given the virus. I really do not care for giant tech companies to come out with giant technology and we're supposed to be excited about it. I find that inaccessible. I really love seeing new projects, new things people are doing. But what I get very excited about, too, is when folks start sharing their side projects or blogs, or sharing some of their work, it's cool. There's more to be done with other groups including people of color, but I've also seen some meetup groups and other efforts for that. So that's what's exciting to me. 

(53:59) – My call to action is to try to find a way to help people. That's why we wrote the book. It was certainly not so we could get fabulously wealthy and retire early. Don't take conventional wisdom and assume because someone told you it has to be true, including us. Challenge conventional wisdom a little bit.

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Babak Hodjat is Vice President of Evolutionary AI at Cognizant, and former co-founder and CEO of Sentient. He is responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world's first AI-driven hedge fund, Sentient Investment Management. He is a serial entrepreneur, having started a number of Silicon Valley companies as main inventor and technologist.

Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. He was also co-founder, CTO and board member of Dejima Inc. Babak is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri.

He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms and distributed AI and has founded multiple companies in these areas. Babak holds a Ph.D. in machine intelligence from Kyushu University, in Fukuoka, Japan.

Episode Links:  

Babak Hodjat's LinkedIn: https://www.linkedin.com/in/babakhodjat/ 

Babak Hodjat's Twitter: @babakatwork 

Babak Hodjat’s Website: https://digitally.cognizant.com/author/babak-hodjat 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:34) – Machine learning and AI based algorithms are being used to get a sense of what is happening in an organization, abstracting out patterns, and then to be able to actually forecast and make predictions into the future. We need to have our AI systems help us with the decision-making itself.

(03:59) – Humans are really good at general intelligence. We know a lot of things about a lot of things. So often that state-of-the-art in AI can not capture things like common sense. The frequency of making decisions is slow enough that we can have a human in the loop.Today, it does still make sense to have a human in the loop. There are cases where we have to rely on our AI systems to make autonomous decisions for us. 

(06:54) – We can build models that are specialized in assessing certainty in our AI systems and the way they do that is based on familiarity on the input side, the context side and familiarity on the output side.

(09:57) – Our systems have to be able to tell us how much we can rely on them. You need confidence in what the AI system is telling you to do, but then there is risk sort of projecting that confidence out. Past performance is no indication of future returns.

(11:36) – Companies and enterprises have definitely reprioritized things. They have maintained, or even in some cases increased their investments in AI enablement, which says a lot about the value that people ascribe to AI based systems. It is a natural next step to digitizing your business.

(14:45) –Evolutionary AI is a set of tools that we use to build AI systems and AI enabled companies. The reason why it's called evolutionary AI beyond the fact that it's an evolution in the way people should think about AI, is that a very strong core component of it is evolutionary computation. We do pull from other AI disciplines as well, such as deep learning and neural networks and so forth, but the essence, the main differentiation here is the fact that we have an element of what I call creativity that is missing in a lot of AI systems. 

15:29) – We're able to search for solutions much more efficiently than we are with your typical machine learning based systems. And that speed and efficiency allows us to be much more creative and find solutions that are either very difficult to arrive at using other methods or impossible to arrive at. So it also gives us a number of very interesting capabilities. 

(20:09) –  What evolutionary AI allows us to do is to actually use machine learning to create what we call a surrogate for the real world. That surrogate is learned off of data that we've seen up until now.

(20:47) – This is the principle of what we call evolutionary surrogate assisted prescriptions, where you have a predictor, which is the surrogate for the real world. You have a prescriptive that you evolve, which gives you a decision strategy. And often you pair that with a certainty model. So when the three of these come together, you have all the elements of a good decision augmentation system, where a human decision maker, let's say a policy maker would ask the AI, how can I achieve this balance of cost and containment.

(24:46) –  Optimization is where we need to be. And that is what decision-making is about. We are constantly optimizing and trying to improve on goals and outcomes. 

(29:25) –  There's a lot of work around new architecture, search and evolving, basically the design and hyper parameters of any kind of deep learning based system.

(36:57) – More and more companies are going to adopt this technology for decision-making and it will start with areas where the decision-making has been captured. So the data around the decision-making is already there, but it will not stay there. It will get to areas where we think decision-making is the soul.

(38:20) – If you are in an organization or enterprise where there's critical decision-making happening, work back from there. You have to have a vision of AI enablement in order to even get the data and digital part of what you do. Build your Data infrastructure, modernize it, report on top of that, build your machine learning and forecasting and predictions on top of that.

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Alberto Todeschini is a Faculty director, consultant and lecturer in artificial intelligence. He has supervised over 150 projects covering a wide variety of industries and techniques, with a special focus on sustainability in energy and water. He also works with the University of California, Berkeley, GetSmarter, and aivancity. 

Episode Links: 

Alberto Todeschini's LinkedIn: https://www.linkedin.com/in/atodeschini/ 

Alberto Todeschini's Twitter:

Alberto Todeschini's Website: https://www.ischool.berkeley.edu/people/alberto-todeschini 

Podcast Details: 

Podcast website: 

https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos  

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch  

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:49) – It has been interesting because in the last few years, a lot of this is about the environment, about energy and about agriculture having been penetrated by data science. I'm pretty optimistic actually, coming out of this big dark cloud. First half of 2022 will be some good news. 

(03:56) – Newer energy technologies have been around for a while, but they really have become mainstream recently, such as wind and solar. They are intrinsically data-driven. So you need to squeeze every last percent of energy out of this massively capital intensive works.

(06:22) – With COVID, we've been forced essentially to experiment. We will see more experimentation around the livable cities for instance. There's a lot of appetite for resilience, for community resilience, maybe at the city level, but also at the regional level and national level.

(09:00) – We've seen the investment moving elsewhere to renewable, which is certainly more future proof. if you talk to the epidemiologists, they'll say, well, there will be another pandemic. As a matter of fact, it could be a lot deadlier. So it will be nice to have this distributed way of storing large amounts of essential items.

(12:40) – 5G enables this distributed system and the ability to communicate incredibly quickly and also to do, technically speaking, inference on the edge.

(17:08) - The market in Europe is pretty fragmented. Partially that has to do with language. So, pretty much most European countries would speak reasonable English, but that's not absolutely not true for the entire population. One of the things that maybe has changed with COVID is the sense of locality.

(20:25) – There's a huge amount of work that needs to be done postmortem, in the real meaning of the term, to understand what went wrong with the data collection. So that next time, collect it better. What went wrong with communication between health authorities and political authorities and the general population.

(24:49) –  Cultivated areas are very interesting because agriculture consumes the majority of fresh workers and about half of agriculture. Currently it is not sustainable. Purely from the point of view of water. And we're not talking about deforestation, we're not talking about runoff of chemicals into the ocean, purely just the water.

7:05) – Some of the main carbon capture technology is very water-intensive. As we increase both the data collection, as well as the predictions, which are two of the main things that we can do with machine learning, we can just use water better.

(28:45) –  These companies that are, from day one, data-driven companies, are all thriving and they're becoming ever more unmatchable.

(38:45) – Let’s use technology to figure out how to improve life in the city or make places where we enjoy walking. We like walking, and we enjoy local restaurants. We enjoy going out. We like biking around the same city, livable cities. So maybe that is something we can think about and work towards.

(41:31) –  It's been awful. It still is awful, but I'm optimistic. Look around your neighborhood and think of things that you want to stay with us. We've been given a great opportunity to reset a lot of our habits.

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Stan Vlasimsky helps companies envision and navigate complex transformations leveraging technology to achieve business outcomes. He is currently a Senior Vice President at Pariveda Solutions focused on digital transformation and helping clients navigate change with a particular focus on innovation, operating with a product mindset, organizational health and leveraging emerging technologies.

Formerly, Stan was a senior executive at Accenture, where he spent 25 years working across the Americas, Europe, and Asia focused on large scale global change initiatives, operational excellence, and technology modernization. He has had the privilege to serve some of the leading companies in the world, including Toyota, Walmart, ExxonMobil, ChevronTexaco, and AmerisourceBergen amongst others.

Episode Links:  

Stan Vlasimsky’s LinkedIn: https://www.linkedin.com/in/stanvlasimsky/ 

Stan Vlasimsky’s Twitter: @Pariveda_Inc

Stan Vlasimsky’s Website: https://www.parivedasolutions.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:30) – We were moving to a more virtual world and we have been for a while, but then all of a sudden over a span of a few weeks  everything was accelerated. How do you make teams effective and motivated where we're used to walking around having team lunches, mentoring, and recognition and all those things? How do we make human relationships?

(03:13) – We've been experimenting with how you scan things, all the collaboration tools we use with our clients we're now having to use with our employees from a career growth perspective.

(06:19) – The most complex algorithm that exists is the human brain and how humans interact with each other, and that's the most challenging thing.

(09:22) – Leveraging both what we do internally and expanding that out into the broader ecosystem condition to other third parties as well. We're doing five or 10 years in five or 10 months. 

(10:57) – We have been changed forever to some extent and we'll have to deal with that new normal and much of that is positive and some it's going to require some more work.

(18:19) – Productivity measured in output of the consulting work we do or to clients has actually gone up.  As we've reduced some of the friction cost of commuting and all those things that happen and then there's an element of, even though we're a very employee friendly company, everybody has seen people in their ecosystem be impacted, furloughed, laid off, whatever. So,  there's an element of the Hawthorne effect, which is ultimately when people believe they're being measured, their productivity changes or generally improves.

(22:22) –  There's going to be  a lot about people and a rethinking of what the models are for things such as restaurants or retail and malls and all the things are going to be similarly impacted as people try to figure out they need a certain density of customers. 

(25:58) – This is going to test every organization, every leader, agility and product and all those are digital, all of the words that we like to use right now, but it's real at this point in time either you figure it out or you don't survive.

(28:32) – Contactless payments helps perhaps with restaurants. The core of this is being able to simplify payment transactions.

(32:14) – It can all be underpinned by ultimately automation, those processes that have traditionally been more manual, but pushed in more traditional ways through different organizations and such again, going back to as things get more digital, that's going to happen. It'd be accelerated because there's so much more data to deal with and every day there's so much more data.  Again, it's never going to add to now the worry is what do you do with the data and what do you do with Intelligent data, because there's no longer a lack of data. It's because I got too much data. 

(35:37) – We're going to be in some sort of hybrid world and  your comments about European flare brands, recognizing what the consumer wants is going to be even more important than it ever was and you're going to have to morph to a hybrid so rather than saying the strong sales experience, people value product expertise, so rather than saying, then having somebody, this is the sales person, this is a person that can help you pick the product.

(38:48) – How do you continue to build those communication skills in a world that is remote? For others of us, it's going to be about empathy.

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About HumAIn Podcast:

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

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Dr. Kari L. Jordan is the Executive Director of The Carpentries. Kari has been on the Core Team of The Carpentries since 2016. Before becoming Executive Director, Kari served as the Acting Executive Director. She has expertise in engineering education, diversity & inclusion, and leadership. In addition to her work as the Acting Executive Director, Kari held the role of Senior Director of Equity and Assessment where she guided The Carpentries through development of an Equity, Inclusion, and Accessibility Roadmap and was the liaison to the Code of Conduct Committee. Before this, Kari was the Director of Assessment and Community Equity where she streamlined The Carpentries assessment strategy and expanded their mentoring program.

Episode Links:  

Kari Jordan’s LinkedIn: https://www.linkedin.com/in/kariljordan/ 

Kari Jordan’s Twitter: @DrKariLJordan

Kari Jordan’s Website: https://www.carpentries.org/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:38) –  I hadn't heard of open source until I started working with The Carpentries and more specifically data carpentry.

(04:11) –  We're all over the place and we work remotely full-time, so the shift that we've seen over the past couple of months from a teamwork perspective has not changed, but in the way we deliver our workshops has totally changed. We’ve moving our workshops online, making sure that the quality in our brand stays the same.

(08:39) – We received quite a substantial amount of support from both the Moore Foundation and the Chan-Zuckerberg Initiative and this funding will help us scale our instructor training program.

(12:02) – I had no idea what open source was, but now I can advocate for it and we can offer opportunities for workshops you may not get in a university, but what does that mean for a degree program? or how can I justify paying or having someone pay for a four-year degree to learn open source or learn to reproduce or all of these things when they can come to a The Carpentries shop? It's a very interesting conversation about the curriculum and who owns it and how it’s shared. 

(14:50) – The growth in open source has to do with problem solving and it comes from the desire to want to solve problems in your own community or want to solve problems that you see things that have been problems for such a very long time that they have not been solved. This is why I talk so much about not only diversity, but inclusion. Bringing people together of all backgrounds and giving them the space to contribute what they have, because every contribution truly does matter.

(19:48) –  There is no wrong way to get involved. There are many ways we can get involved with open source.

(21:39) – There are hundreds of organizations dedicated to allocating resources, to providing opportunities for people to get involved with data and coding and it's not the responsibility of one organization to do all the work, The Carpentries I feel like our zone of genius really is that training teaching data skills training that type of pedagogy. It's really important for this opportunity for access and just sharing what we do is so important.

(24:32) – What do you want your participants to walk away with? That's extremely important to the carbon truth. We don't want anyone leaving our workshop feeling worse than when they came in or feeling they’re never going to learn this. It's more so about that self confidence piece that belonging to a community that's what it's all about, and eventually you're going to learn some code, you're going to learn how to code. 

(28:19) – There's no wrong way, and I very much appreciate the industry acknowledging a four year degree may not be the answer for everything. There are things that I've definitely learned in college, but the industry is noticing that you can pick up skills along the way, you can take a two day course, you can take a month long seminar and be just as effective in your role and learn just as much. So it's all about pathways. 

(30:22) – You don't have to be proficient in any of the programs to be a maintainer, you have to be patient and know how to be organized and how to facilitate conversation around the lesson.

(34:46) – If you ever thought that you could never code, you thought wrong. I have been in your shoes, I shied away from programming for a very long time and now I'm the executive director of a nonprofit that teaches foundational coding and data science skills. There is nothing to be afraid of because there is a community in The Carpentries that values you, that appreciates your contribution and that appreciates your perspective. I want you to visit Carpentries.org, check out the opportunities that we have for mentoring see if there's a workshop, all of our workshops are online right now, actually so this is actually a great opportunity and great time for you to get involved

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Ben Byford has been a freelance web designer since 2009, and he is now mostly a freelance AI / ML teacher, speaker and ethicist and tinkerer – in his spare time he makes computer games. Ben has worked on large scale projects as a web designer with companies such as Virgin.com, medium scale projects with clients including BFI, CEH, Virgin and Virgin unite, as well as having created a myriad of sites for smaller businesses, startups and creatives' portfolios.

He’s mostly been a design and front-end guy, with extensive knowledge of other tech and development languages and has previously worked as a mediator between dev teams and clients. His public speaking and lecturing blends his insights within AI and ethics, web technologies, and entrepreneurship; focusing on the usage of technology as a tool for innovation and creativity. 

He hosts the Machine Ethics Podcast, which consists of interviews with academics, writers, technologists and business people on the theme of AI and autonomy.He also talks about Machine Ethics.

Episode Links:  

Ben Byford’s LinkedIn: https://www.linkedin.com/in/ben-byford/ 

Ben Byford’s Twitter: @benbyford

Ben Byford’s Website: https://www.benbyford.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:24) – The big question and a moral quandary which we're battling with is how much information do we give to organizations, governments, about our movements - and that's always been the case - but we're now having to think differently in the face of a pandemic, about  how much we can give away, and what kinds of things can be done with that data.

(03:31) –  You're really concerned with whom you're giving that data to. And can they be transparent about how they're using that data, and have that data secure, and be able to delete that data when appropriate. And it's very hard to actually believe or have trust in organizations when they say these things. it's a good thing to be doing, but the trust issue is a big one. 

(06:51) –  Whether Americans have a similar legislation put in place is, in my opinion, irrelevant. Because the internet is cross boundary, cross continental. So, if you deal with anyone outside of your own jurisdiction, your own country, then you will fall into someone else's legislation. And it just so happens that GDPR is one of the most robust that we have at the moment, to do with data.

(09:19) – We should be teaching people to reflect on the situation within our educational institutions, so that we are priming people who are going to be making this stuff in the future, to be making design decisions and technical decisions that they can implement it in full respect of other people, and for the respect of the environment. 

(12:20) – We should all be worried about security, as citizens and our data privacy as citizens, because we don't necessarily want to tell everyone what we're talking about, and that comes into our discrimination issue. So, you can be discriminated against in different countries, for all sorts of different things. And you might not want to tell your neighbor or your government certain things about your person, because those things aren't deemed in that country normal or acceptable or legal. 

(13:22) – There are many reasons why you would want to keep your privacy and your security intact.You're using a utility, and the utility doesn't respect the user. We're saying water and electricity is a general need, a civil need, I think the internet is certainly up there as a civil need.

(17:40) – As you're building technology, you have to require consent under GDPR. You have to stipulate usage under GDPR, and you have to give terms of access under GDPR. So, if you are to be amended or deleted for your delayed data or have your data shared to the user, what specific data they have on them. All that has to be implemented. And if you don't implement that, then you could be taken to court and sued for a lot of money. Now it's illegal to be doing some of that stuff, but within the ethics of AI and the ethics of technology and kind of the ethics of mass automation, we have to really go beyond what is under GDPR, beyond what is legal, illegal and think about again, what is it the world we're we're making?  What is equitable to most people? What is useful to people and what isn't just useful to shareholders. 

(22:34) – Face tracking stuff is great. It's a microcosm of what is essentially a really big ethical quandary, which has positive and negative effects. So it is really interesting and really frightening in the same way. You have to create trust. And if it is known that these machines are very good and work very well, and the information maybe doesn't really leave the robot in any meaningful way, or is anonymized in all aspects and isn't actually restricting the citizens mobility we've built something that actually really does work and works enough, and knowing when it works enough is an ethical question. And then also, allowing humans to be in the loop somewhere.

(26:53) – The obvious contradiction here is that the Chinese system seems to be very heavy handed in its use of technology to implement those social norms. We don't really have a similar approach, I don't think, in the West.

(34:02) – You have all these really good applications, all these really interesting applications. And then you have applications which then restrict people's rights or human rights. And again, it might be that we have to look at what human rights actually mean in the digital world.

(38:04) – We want to live in a world where George Floyd or anyone who is discriminated against traditionally in a society can walk up to a police officer, can walk up to a person of power in that society and know that they are going to be trustful, trustworthy, wherever your trust in any situation, you don't want to be in a situation where you are in grave danger and you can't trust your own environment.

(39:11) –  There has been a wealth of interest in ethics and technology and, in Data Science and Machine Learning, and AI has just been an explosion. I'm seeing that with the emergence of quite a few workshops and talks and conversations around AI, responsibility, transparency, and diversity and equity and all those sorts of terms. Into the future, I am most interested in how the interaction of moral agencies appears in technologies that we actually use and within society's reaction to it. 

(44:19) – Be mindful. We all have our autonomy and we all should be thinking about the things that we are doing, and you should be empowered to think about what you are doing. It's easy for me to say it on this podcast, but please be mindful of how you affect the world. 

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As the Head of AI, Labor, and the Economy, Katya Klinova directs the strategy and execution of the AI, Labor, and the Economy Research Programs at the Partnership on AI, focusing on studying the mechanisms for steering AI progress towards greater equality of opportunity and improving the working conditions along the AI supply chain. In this role, she oversees multiple programs including the AI and Shared Prosperity Initiative.

Katya holds an MPA in International Development from Harvard University (USA), a B.Sc. cum laude in Applied Mathematics and Computer Science from Rostov State University (Russia), and a Joint M.Sc. in Networks and Data Science from University of Reading (UK), Aristotle University of Thessaloniki (Greece), and Universidad Carlos III de Madrid (Spain), where she was a Mundus Scholar.

B is a technology and facilitation expert who is passionate about creating social change through empowering everyone to participate in technological and social governance. B is a Congressional Innovation Fellow serving in the US Senate advising policy makers on technology policy.

B received a Bachelor of Science in Economics from the University of Texas at Dallas, and was selected as an MIT-Harvard Assembly Fellow for the 2019 Ethics and Governance in Artificial Intelligence Initiative cohort.

Episode Links:  

Katya Klinova’s LinkedIn: https://www.linkedin.com/in/katyaklinova/ 

B. Cavello’s LinkedIn: https://www.linkedin.com/in/bcavello/ 

Katya Klinova’s Twitter: @klinovakatya

B. Cavello’s Twitter: @b_cavello

Katya Klinova’s Website: https://www.partnershiponai.org/ 

B. Cavello’s Website: https://bcavello.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:55) – AI and technological change have been contributing to the polarization of labor market skill bias. What we saw as the pandemic is that people with college degrees, people who have the opportunity to work remotely have been hit economically much less comparatively with people who are not able to work remotely. And that's disproportionately people who did not have access to higher education and college degrees.

(04:52) –  We see a lot of formal sector jobs  falling away as a result of precautions taken to manage the virus. But as a result of this, we also see a proliferation in oftentimes lower wage on-demand or gig work playing out. There are many, several silver linings to take from this trend that we're seeing playing out, but there are also a lot of highly disruptive technologies in the space of robotics and information technology, especially in the AI space, that could lead to possible exciting futures, but they could also lead to some less ideal outcomes.

(08:14) –  Some people might have found out that they're just as productive working from home, and they save time commuting. So some companies might have discovered that they're saving a lot of money on office space. So they might choose, even if it's not because of healthcare considerations, they might choose to stay remote. And that might become more of a norm.

(10:41) – We see a whole new level of disparity across the board. The office, the workplace is in some ways a leveler, in that everyone has access to the same coffee machine, the same conference room, the same equipment, but as more of our work is distributed, that might not be the case.

(13:19) –  I also want to shine a spotlight on the role that we human beings are playing in the process of facilitating the development of these technologies. And while we recognize that, we're building incredibly fabulously capable machines, really continuing to interrogate to what end and to whose benefit those are being built. Taking a more active stance in the future of work debate, and being more deliberate about choosing the direction of technological change when it comes to AI and other technologies as well is what is missing.

(18:41) – We need to be realistic about our ability to quickly enough upskill everyone globally to keep pace with the technological advancement and think about how do we lower the barrier to entry, lower the barrier that's needed in terms of skill requirements for people to be able to use these technologies to their economic advantage and extract economic value from that and be able to use it for their earning opportunities. I'm genuinely curious to what extent certain jobs that are considered as low skilled or high-skilled, which we recognize as the flawed language of economics, where we're really what we're referring to is educational attainment and how much pre-training someone has.

(28:24) – The benchmark that we hold our technology against is not these questions of what would make a worker's job easier or their output better. But rather this question of, is it going to be able to perform at the level of a human? Can we make a technology that will make a person,  that will then be able to do whatever a person can do? And there's this sort of fetishization in the AI sphere. And it comes from a really beautiful, fascinating space. The scifi nerd in me does really wonder, Oh man, what would it be like to create other ways of thought, what would it be like to develop these thinking machines.

(31:04) – We have something like 8 billion humans, those humans now more than ever are in need of gainful jobs. And if we think of technological progress as the type of technological change that helps society prosper and overcome its economic condition, the last thing that we need to do is to be building machines that do what humans can already do better than them. And creates competition for those humans.

(40:25) –  I work in the AI space because  that can be a thing that does bring about incredible opportunity and prosperity and new horizons of understanding and collaboration that we haven't even seen before. And that's really exciting to me. So, I wanted to just clarify that this stance is not one that says we shouldn't have AI. We shouldn't go down this road. We shouldn't build these technologies, but rather, that this technology isn't moving on its own, it's moving because our hands are doing the work, at least for the time being

(44:01) – There's a lot of talk in conversations about structural issues and structural change. At the end of the day, these structures are built by us as people, the humans in the AI loop, and we have the power to shift it. And we also have the power to do things that we couldn't do before.

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Felipe Longé is the CTO of Play Magnus and the CEO of Solve Oslo. His mission is to empower talented people in creating empathic user experiences utilizing product development strategies and startup methodologies.

He’s always been absorbing competency from all disciplines during his 10+ years of experience with software and product development. He believes that the larger picture can only be understood and engineered if there's a deep empathy for the end-user. The user-centric focus combined with an understanding of technological opportunities, lead to better decisions and sustainable strategies for digital businesses.

Episode Links:  

Felipe Longe’s LinkedIn: https://www.linkedin.com/in/flonge/ 

Felipe Longe’s Twitter:   @felonge

Felipe Longe’s Website: https://welcome.ai/  

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(04:01) –  Magnus Carlsen himself and his father wanted to enter the digital space and do something with this brand building. We built apps because they are a good way to spread your brand, to create awareness and also to spread joy. 

(09:54) – We were very thorough with it, making sure that the app feels personal. When you open the app, it feels like you're going to actually play Magnus on it. It's a totally different experience from displaying random chess against AI.

(11:41) –  It's actually AI against AI at some points, because they try to memorize as much as possible within the branches that they play. Maybe it's a merger between machine, man and machine. We'll see a huge revolution in how sports is executed based on what machines can learn about it. And not only humans. 

(21:37) – When you repeat something a lot, you are creating shortcuts, you're wiring your brain to do that specific task perfectly. You do this over and over, and it just becomes embedded in your software, so to say.

(24:59) – Freedom for most people gives responsibility, which is good. This will be the way to work moving forward. Not only because of COVID, it would be because it's more effective and it gives this type of freedom. 

(28:21) – We do both consulting and product development in house. One of our cool engagements has been to work with a camera that can scan your eye and recognize patterns on diabetes to people with diabetes II, and therefore, find out if you're becoming blind. 

(30:19) – New phones will come out. Wearables will be a huge thing moving forward. At some point we'll figure out how to come closer to the connection between a scan and psychological States of the mind.

(32:53) – We're wearing the technology that we previously had in a living room, and in our offices. Most people will have smartwatches and bluetooth devices all over their body. And of course the smartphone, but that will shrink, or at least become thinner and thinner up to a point where it's maybe a bracelet or something and you can bend. Nanotechnology will at some point become cheap to use and the manipulation of genes, it's all there, but there are so many sciences just expanding exponentially.

(36:14) – We need to become more connected on these things, especially on med tech. At some point it would just become global,  pure global collaborations.

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Jared Goldberg is the Head of Data Science at WeatherOptics. He owns a Bachelor of Science in Biopsychology, Cognition, and Neuroscience; Applied Statistics from University of Michigan.

Episode Links:  

Jared Goldberg’s LinkedIn: https://www.linkedin.com/in/jared-goldberg-427462103 

Jared Goldberg’s Twitter: @weatheroptics

Jared Goldberg’s Website:https://www.weatheroptics.co/ https://github.com/jaredbgo 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:36) – WeatherOptics started as a weather blog way back when, from our founder and CEO, Scott Pecoriello, he was a weather nut growing up. And he had this blog, it was on Facebook and social media. And his snowfall accuracies were crazy good. In November of 2017, he reached out to me to bring the business into this tech side of things and into the data side of things. And we have just been gaining momentum since then.

(03:15) –  Quantitative forecasting was started in the 1920s by the Norwegians. The modern era of forecasting started in the 1980s and that's where we had global forecasting models based on a more complex system of observations, but still building off these physics concepts that were used originally. Since the 1980s things have just gotten more complex. These models have gotten better. And now there's this whole wild system that no one really realizes is happening where you have all of these different inputs from all these weather gauges, like airplanes and satellites, and they're all amalgamated and interpolated into these models.

(09:52) –  To some extent, everyone has this inherent understanding that the weather changes our behavior. However, we need to keep in mind that our business and where we really understand the weather better is the short term weather events. It is these sorts of impacts that obviously are not as flashy as something like a hurricane or a tornado, but we feel understanding how weather impacts daily life at these smaller scales and these less major events actually can save people and companies a lot of money and can really improve their processes.

(14:11) – While some industries have been excluded or cut off, and obviously a lot of people are losing jobs, there are other industries that we are leaning on much heavier. And one of these industries is logistics. And one major application of our weather data is building useful ways to understand, not just that it is going to rain or I guess in this case, it is not just going to snow in. It's how is that going to affect your route? We are aware that weather has an impact on sales. So we consider weather data, a viable source of alternative data in terms of quantitative investing and things like that. We think these weather signals can help explain variations in other datasets that help us understand the market. 

(17:46) –We have a combination of meteorological expertise, as well as machine learning. We have been very thorough to truly understand how the raw weather data paired with these non weather variables, add up to these actual impacts and we feel by delivering impacts as opposed to raw weather data, we are going to allow businesses to make impactful decisions, that way they do not have to wrestle with the data itself.

(23:39) – We expect that these self-driving cars will need to have an even better safeguard against these road conditions that could be disruptive to normal driving. It is those sorts of interactions between variables that we feel our impact indices would allow people to have the upper hand to understand that just because it is raining does not mean that the roads are not going to be dangerous. And perhaps these cars, these very smart and intelligent cars should know the level of danger and how prepared they need to be in order to uphold the safety of the people using them.

(27:37) – Power outages can be in terms of how weather affects humans on a day-to-day level. California outages would be the perfect use case where if the emergency management companies and government groups that were preparing for these things, if they had a really accurate forecast of what was going to happen in the future, based on the weather, then they could have had a better response.

(32:32) – The whole idea of our company is these impact indices and all of our forecasts allow these companies to have the heads up to say, we think something disruptive is going to happen. So you should change your behavior in order to mitigate loss.And  once a company has identified that they would like the heads up about this bad weather, and they would like to understand how weather is going to impact their day to day operations, the whole idea is we want to deliver that information in a format that makes the most sense.

(34:45) – Our insight portal is for more of the non-technical audience. And this is for individuals who perhaps are managing a certain geographic area.The insight portal is our attempt at the most user-friendly nontechnical delivery of these same insights. Our most technical offerings you could argue are our APIs, which are delivering the raw weather data itself, such that we give you those impacts very granularly. And then your data science team would get a chance to play around with it and use it in the way that is best for them we are building this middle ground to deliver things like Excel templates that have this weather data aggregated up. 

(39:16) – We cannot blame individual events, but we do know that these large term changes can be attributed or are more evident that things are happening.So it's important to know that as the climate changes and as these big term big level changes happen, it's going to result in these small level things that are going to start affecting our lives. That's why it is just going to become increasingly important to know when those individual bad weather events are going to happen in order to prepare for these bad things and mitigate loss as we've discussed, but also we need to keep track of them. 

(42:48) – In some ways, the weather can pop up relatively randomly and be quite disruptive across industries.Moving forward is getting these crop indices up, testing their accuracy and deploying them across our product suite. 

(45:28) – This could even feed into that fire in terms of technology and improving and people realizing how important prediction is going to be. Maybe it'll just make people more excited about technology. I certainly hope so. 

(47:40) – If people can use weather as a framework for technology and artificial intelligence as a whole, it will allow people to understand how powerful prediction is.

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Sam Horodezky is the Founder of Strathearn Design. He has been dedicated to user experience (UX) for more than 20 years. During that time, he founded a company specializing in this field called Strathearn Design. With more than 15 years of management experience, he has worked with and overseen multiple teams of designers and developers, and created a wide variety of unique, focused strategies for companies that needed to improve their UX strategy.

At Strathearn Design, clients are pushed to think beyond the aesthetics of their UX. Their main goal is to educate and enlighten clients about their entire business and product suite. They put their expertise to practical use, advising clients about the skills their teams possess and the quality of their product. They can also manage and repair their entire UX from the ground up, studying every detail of their business and their market.

Episode Links:  

Sam Horodezky’s LinkedIn: https://www.linkedin.com/in/sam-horodezky-3b19552/ 

Sam Horodezky’s Twitter:   @StrDesign

Sam Horodezky’s Website: https://www.strathearn-design.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:43) –  Some of the tools that are becoming available now are specifically meant to democratize design or bring design to the masses. Wix has this thing called the ADI (Artificial Design Intelligence), and it helps create a website that is very straightforward.

(04.27) –  Either machine learning or AI have to be able to generate as much as possible. We're quite there yet when it comes to design. Not as far as I can tell, but that is definitely the idea, to reduce the amount of work required of the human.

(05:18) – Microsoft does do a lot of artificial intelligence, ML type stuff, whether they're actually using that or not, you can never really tell. All they have to do is put in a graphic and then some texts that can have different groups of texts and different pieces of graphics and it'll give you lots of options. I'm sure they're developing all sorts of interesting techniques to make design so that non-designers essentially can get good results.

(07:32) – Logo Joy was centered around logos. It's now called Looka and this one will generate a bespoke logo. You only pay once you decide you want a high resolution image. It's not the same quality as if you were really to hire yourself a designer and get a bespoke logo. But at the same time for 50 bucks, this is giving you a lot.

(10:49) –  What really is AI and what is not? What is definitely true is that you're able to take a photo or a video and then transform it into something that looks totally different than it actually can be, quite professional. It's another example of increasing the ability to have tools for users that aren't really designers.

(14:04) – There's a lot of interesting tools out there, but they seem like they're more kind of experiments than they are things that are genuinely going to change how we do work. Photoshop has a tool called Content Aware Crop. If you try to rotate something or change the dimensions, it fills in the background for you. Netflix has one thing related to user interface, a simple snapshot that shows you the video that you are actually about to watch or the movie. Firedrop.io is able to process videos and use large amounts of data to basically output advertisements.

(19:37) – The de facto tool that everyone was using 10-15 years ago, was called OmniGraffle. Sketch is being displaced right now, but Sketch again was the de facto tool for UI and UX designers for a long time.It allowed you to do pixel level manipulation. Figma allows you to have a collaborative experience. Adobe used to have a tool called Fireworks and they adopted it to call it IXD. They're essentially SAS solutions.

(24:22) – Those tools are just going to become increasingly joined with Slack but I'm not necessarily predicting that it will specifically have Slack integrations.

(26:02) – Sketch didn't go to the cloud fast enough and they allowed other entrants to the market beat them to it.

(27:22) – There's an entire industry now that's building tools and what they do is they provide analytics that are input to product managers or to user experience designers. Some of these tools will eventually begin to pull all their data together and put AI on top of it and actually be able to suggest user interfaces based on all the data that it's been looking at. 

(32:53) – There are absolutely low code options for people who either don't know anything about coding. But we're still really far away from the day where we don't need developers because an AI will be doing it.

(36:22) – For people who all they're doing is taking one thing and moving it to another set of colors or a different font, or basically doing some of that unpleasant work, that's going to be mechanized within 10 years. Those people need to up-level their skills, so that they're doing something more complex that a computer can't do today and may not be able to do for some time.

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Chris Bishop has a degree in German Literature from Bennington College. He started music after getting out of school.He ended up in the jingle business, writing music for television. Then he became intrigued by the web and taught himself to be a web producer and worked at a couple of seminal interactive agencies in New York. He was hired by IBM into their fledgling corporate internet programs division.

He is a TEDx speaker, ex-IBMer, former NYC studio cat, future workplace consultant, and a firm believer in the power of focusing on the fringe. Based on his own nonlinear, multimodal career path  he’s developed a workshop called “How to succeed at jobs that don’t exist yet” designed to excite and empower today's learners as they navigate the global borderless workplace.His session provides insight into how to deliver business results and pursue successful careers leveraging emerging technologies including quantum information science, AI, data science, fintech, cryptoassets, blockchain, augmented/virtual reality, genomic editing, and robotics.

Episode Links:  

Chris Bishop’s LinkedIn: https://www.linkedin.com/in/christopherbishop123/ 

Chris Bishop’s Twitter: @chrisbishop

Chris Bishop’s Website: https://improvisingcareers.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(04:38) – The U.S Bureau of Labor and Statistics says today's learners will have 8-10 jobs by the time they're 38. They're going to use technology that doesn't exist today. I connected with a gentleman from LinkedIn Learning and he said, I think your content would be valuable to the LinkedIn Learning audience and here we are.

(06:18) – People can work from home or from wherever on the train or in a Starbucks and be more productive, because they're more in control of their time. Data science is going to have lots of opportunities to take these learnings, as you said about education.  The opportunity again, for data science to rethink how information is shared and distributed represents a huge opportunity. 

(08:36) – The idea is that humans have been creating devices to make work simpler and faster and easier for literally thousands of years. There's lots of history and precedents for the kinds of tools that led to humans manipulating data, that is what we do today with algorithms and using artificial intelligence and machine learning. So it is part of a long arc that goes back thousands of years and is going to continue for thousands of years.

(11:38) – An interesting example to share is the New York Stock Exchange. That space is basically a catering hall now, because there are algorithms that are doing most of the trading. There are certainly people in there doing work, but back to your comment about math, algorithms can make assessments and recommendations, buy and sell way faster than a human can. So that's the model, it is like, let's use tools that will help us move faster, work better, work more efficiently and improve productivity.

(12:24) –We are also seeing AI being used to help radiologists examine X-rays.  A lot of data science is being put into the unfortunately scrubbed mission today, but hopefully we'll see the SpaceX launch. That's going to open up incredible opportunities for data scientists, not just around NASA and ancillary businesses. 

(14:41) – Everything is generating data now and the idea is that data is empowering. It can also be disabling. And there are certainly conversations about privacy and confidentiality. At the end of the day, the ability to capture data and represent it accurately is a good thing. Using tools like AI and machine learning, we can take that data and make sense out of it and rationalize it, not only to live more comfortably, but also to drive innovative business models.

(16:17) –  Interesting new careers, jobs and certainly in data science are emerging at the intersection of unlikely or historically disconnected disciplines. So by that, an example I cite is Nanopharmacy. So they're now creating ingestible bots that can carry Pharmacology at the atomic or molecular level, to the affected area, to the tumor or to the wound or to the area where the medicine is needed. All that kind of science that's going on now in these crazy times is going to be expanded. it is  going to set models and precedents for how medicine is created and delivered, how healthcare and biomedicine is created going forward

(19:05) – My toolkit is me reflecting on how I navigated these careers and trying to codify them into these future career tools. I call them voice antennas and mesh. Technology is a source of information about future tech and culture. So that's the antenna piece. And then the third piece is mesh, which I like to describe as a three-dimensional data visualization of your network. 

(23:17) – First of all, get into a disciplinary vertical that you're interested in, a topic area that you're passionate about because then you'll be successful if you're interested in it and then find ways to step back and provide more strategic higher level business perspective, and respect the fact that you are knowledgeable, more than you think about how say a business is run and some it is not for everybody. I would encourage data scientists again, as this is such a rapidly evolving and morphing field to think about how to move up into say a management role or a strategy role, to not be afraid to contribute ideas about solutions for innovative products and services that a company might take on to drive their business model. 

(26:11) – There's lots of sources of information and the bad news is there's lots of really good sources of information. So, managing, parsing and doing triage on the tsunami of info is the challenge. The implication is that these are topic areas you're interested in. The broader implication is, it represents focus areas for a data science career.

(28:40) – Learning is key. I heard it stated by some writer recently that we have to stop thinking of education as an event that happened in time. Education is something that goes on your whole life. It never ends, especially in this environment. In the second decade of the 21st century learning is a non-stop process. Just like networking.  it is the old adage described to showbiz, but true in every business. Now it is not what you know, it is who you know, so building your mesh is critical. 

(33:13) – My general advice, certainly to all careerists, but definitely to data scientists has always been and served me well, is, chase the maelstrom, find the chaos, go for the mayhem. So go where they don't know what it is yet. And then you can be involved, you can have a creative role, you can do something interesting and innovative and be employed gainfully and be remunerated.

(34:48) –  I went into this web thing and it served me well. It was an emerging technology that people didn't quite know what to do with it. And people from all different kinds of disciplines and backgrounds were getting into it. So fast forward to 2020, the areas where I've encouraged data scientists to focus on, are things like certainly AR and VR. In the education space and in the medical space and then even in financial services, I would encourage them to investigate crypto assets, blockchain, and bitcoin. These are all going to be big opportunities, certainly 3D printing. Biotech, certainly education, almost everything you can think of is being transformed by technology and the implications are on data science. 

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Merav Yuravlivker is the Co-founder and CEO of Data Society, which builds and delivers tailored data science academies to Fortune 500 companies, government agencies, and international organizations. From assessing your current staff capacity to implementing data-driven culture, they can unleash the workforce’s potential to solve your organization’s toughest problems and prepare for the future.

Episode Links:  

Merav Yuravlivker’s LinkedIn: https://www.linkedin.com/in/meravyuravlivker/ 

Merav Yuravlivker’s Twitter: @Merav_Yurav

Merav Yuravlivker’s Website: https://datasociety.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:34) – Data Society is a data science training and consulting firm. And we work with government agencies as well as large organizations and corporate clients to help them understand their data, to solve problems. So whether that is through customizing training programs, to their use cases, to train up their workforce, to understand data, or whether that is building customized software and algorithms to help them make predictions about trends that they are seeing, we are there to provide solutions

(03:01) – What has been truly amazing is just the way that our team has handled the transition from more in-person training to more live streaming. Since we switched to live streaming, we have a lot of students from South America who are joining us now, and it has been really wonderful to see that additional impact that has had and the different points of view that they are bringing to the table.

(05:11) – This is really going to shift the way that people think about education. can we really provide support for each other at a time when people are still trying to work out what support they want. now we are chunking it into smaller portions over longer periods of time to make sure that we are maximizing that learning and that retention.

(06:39) – Data is the only way that we are going to get through this successfully and make sure that we prevent it in the future. So it is really important for us to understand that data that we are collecting about this pandemic is truly for the benefit of the entire population. While there is a lot of politics that seems to be involved in this pandemic, it is important to understand that data is apolitical and it is important to use it in order to inform our decisions.

(11:01) – There is a lot of that misconception going around. And in fact, we did a study last year of data scientists and asked them what their biggest pain points were in their workforce, and what we found is that they had a lot of difficulty communicating insights to their managers and to their staff outside of their data science teams, because there is not a common data vocabulary. 

(11:44) – Another misconception that a lot of people have is that data science is magic. You push a button and all of a sudden, you know exactly what is going on, and I am sure you could also speak to how much time data collection and data cleaning actually takes. Usually it is 80% of any data project and a lot of the data scientists that we surveyed said that there was a lot of frustration on the part of their bosses because they do not understand exactly how time-consuming it is to collect that amount of data and then to collect it accurately and make sure that it is clean and ready for processing.

(14:11) –  There are some very valid concerns that have come up, people do not want to be tracked by a company without getting certain assurances about how their data will be used. 

(16:22) –  What if we could connect with data inventories from grocery stores and then build an app to be able to share that information with shoppers so that they can check the supplies before they go. And that way they will only have to make one trip because the other concern is that the more trips you make outside, the more exposure you have to COVID. So our aim is to reduce that, so you only have to go out one time to get the essential products that you need. And what we found out very quickly is that groceries had their hands full already. And a lot of them do not have up-to-date inventory APIs, for example, that we could tap into. So we ended up partnering with another local Washington D.C company called OurStreets, and they have built an app called OurStreets Supplies, which helps people find out what is in stock at a grocery store near them. 

(20:54) – Furloughed workers are workers that are still technically employed by companies, but are not receiving paychecks. And what is really unique in this situation is previously when employees were furloughed, they were not eligible for unemployment insurance, but because many companies are anticipating this to be a short crunch as opposed to a long lasting effect, they do not want to lose some of their employees by letting them go too soon. 

(22:11) – My company is working on helping prepare those individuals to re-enter the workforce with very highly prized data analytics skills. Bring that industry knowledge that they already have and have taken years to learn and then pair it with that data analytics skill set to create something completely new and help them become more agile in this environment.

(28:45) – Even though the levels of productivity might be the same, there are a lot of intangibles that are very hard to measure that encourage innovation and collaboration that really only occurs in an office space. There is going to be a big shift towards data literacy. And what I mean by that is an understanding of how to ask the right questions of data, understanding what the terminology means, what the potential means and feeling comfortable to manipulate data, visualize data to a certain extent. We are going to see some little robots that are running around on sidewalks, delivering our pizzas inside and stuff like that. So I think we are going to see that type of shift and we are going to see a lot more jobs in that type of automated, like automated behavior.  

(37:36) – It is becoming more imperative now more than ever for companies to make that shift to become more data informed. If you are not starting to plan for this data economy that we are in, it will be like competing in a race when you are in a rowboat and your competitors are in motorboats. You will get there eventually, maybe, but you are probably going to spring a lot of leaks and you are definitely not going to be ahead of that pack. A lot of that has to do with the ability for an organization to be agile and to empower its workforce, to think independently, to ask the right questions and to be able to solve challenges effectively.

(43:01) – Take an inventory of where you are currently. So assess what data tools do you have? How is your data stored? How is it stored securely? And then thinking through your workforce; Who are your powerhouses? Who are your people that really are leveraging data and how well is it understood in terms of data governance and data policies? 

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Matthew O’Kane leads Cognizant’s AI & Analytics practice across Europe.  His team helps clients modernize their data and transform their business using AI. Matthew brings close to two decades of experience in data and analytics, gained across the financial service industry and consulting. Prior to joining Cognizant, he led analytics practices at Accenture, EY and Detica. 

Over this period, he has delivered multiple large-scale AI/machine learning implementations, helped clients transition analytics and data to the cloud and collaborated with MIT on new prescriptive machine learning algorithms.  Matthew is passionate about the potential for AI and analytics to transform clients’ businesses across functional areas and the customer experience.

Episode Links:  

Matt O’Kane’s LinkedIn: https://www.linkedin.com/in/matthewokane/ 

Matt O’Kane’s Twitter: @MatthewOkane

Matt O’Kane’s Website: http://www.infosecurity-magazine.com/view/13065/comment-connecting-the-dots-on-insider-fraud/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:14) – I finished a math and stats degree. Got interested in statistics, joined banking and realized there was tons of data I could play around with and apply predictive models to. But almost 20 years ago, I never realized how important AI in Analytics will become as it is today. I joined Cognizant a year and a half ago to really drive what the next level is around analytics and AI, how clients are really scaling AI across the companies and it's a big engineering effort now. Hence why we've got a big team of people who do all the things you need to get started on around AI.

(04:03) –  I still go back to the underlying machine learning algorithms that have been around for a long time. Some of the models have gotten more sophisticated and computing power has come along and cloud computing power has come along too, to help us actually power these more and more.

(06:04) –  We're obviously going to enter a large recession. The type of AI and the type of work you can do within the ISP will change dramatically. Things like revenue generating opportunities for AI are going to be less on the priority list for at least the next year, and it's probably going to be more on cost reduction

(07:39) – If we say it's moving from revenue generating opportunities to cost optimization opportunities, most organizations are gonna see a big shift towards automation, around AI, and we've seen a lot of clients are working at the moment looking to apply AI in new areas they probably hadn't thought about. Automation and the fact that automation means less jobs in a recession and it takes away human effort, we have to square up for what is going to be the reality of the moment.

(09:39) –I don't think privacy is going to go away. It still seems to be top of priority, we're just trying to solve privacy problems by Webex and by remote working and by email rather than face-to-face but it's still a big issue and coming out of this if you're going to apply more data and AI to your business, the privacy aspect goes up and is always going to be top of the agenda.

(11:03) – There are still fairly distinct areas where humans are good and certain tasks where machine learning is good at a task, so it's really about taking another look at every process you have and re-imagining it within this new digital AI world. This is certainly a crisis that has created significant demand in some areas and a drop in demand in other areas. That's how it's going to play out going forward so we need to be shifting humans to the right areas.

(12:41) – Typically if you send an engineer out to solve a problem they're not the expert; there's only about five experts in the entire company. But by taking some of the knowledge from those five experts and turn them into some models you can infuse the insight and the knowledge from the five SMEs into the day-to-day work that the engineers are doing and they can use augmented reality to actually see something. 

(14:39) –  It allows a human to essentially take what's in their brain and turn it into a model, it allows your experts in the organization, your best claims handler, your best salesperson, your best engineers to take what they have and their understanding and turn them into a set of rules. This is called data programming and these rules can then be turned into a neural network model. AI is very good at processing all the massive data, but it doesn't have the intuition that's held inside of an expert's hat.

(17:38) – It turns around to the ethical AI Space as well as the fact that if the research you're doing and what you're developing isn't open and people can't go in to get help and look at it and look at your code and understand how it works. What my team does is take the complex research and a client problem and try to fit the two together and that's usually the hardest thing to do, getting something that impacts clients business.

(19:20) – It's not just about algorithms and code. We have to convince the executives in our company to change their business or some new deep learning could do to the actual outcomes.

(20:31) – The UK government has been doing a lot of research on AI, they've used that to develop a set of ethical AI pieces, a good set of standards. Now we're working with the UK government infusing ethical AI into every single machine learning model or project that they run. 

(23:36) – From the data scientist all the way through to the product engineer if the business where we're actually applying the AI is making different decisions, that responsibility has gone all the way through the organization. 

(25:33) – Data is always biased if you look at that data without realizing COVID etc was happening. There's always something behind data and there's something generating that data.

(27:35) – A lot of execs in companies, people that are budget holders can control where AI is used and how they can accelerate and improve business results.

(28:39) – A lot of companies have worked out how to operate remotely, and that's a very good time to open up about ideas, about how you could be scaling AI in the organization, how you can really get going and change things so now is the time to have that conversation.

(29:47) – It’s important getting the right data platform before you can do AI.  A lot of clients that are going back and saying we need to solve our data, modernize our data, create the right governance model around it usually move on to the cloud. That's what most clients are doing, enabling it and then really scaling AI.

(31:37) – They've really got to reduce costs, reduced errors, all these things that are dragging their business down, if we can really help in that area we can really speed up growth in the local companies.

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Nikita Shamgunov co-founded MemSQL and has served as CTO since inception. Prior to co-founding the company, Nikita worked on core infrastructure systems at Facebook. He served as a senior database engineer at Microsoft SQL Server for more than half a decade. Nikita holds a bachelor’s, master’s and doctorate in computer science, has been awarded several patents and was a world medalist in ACM programming contests.

Episode Links:  

Nikita Shamgunov's LinkedIn: https://www.linkedin.com/in/nikitashamgunov/ 

Nikita Shamgunov's Twitter: @NikitaShamgunov

Nikita Shamgunov's Website: https://www.singlestore.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:37) – People who have the levers of power are rolling out initiatives, rolling out shutdowns and thinking about these big disruptive changes. Andrew Cuomos's updates, always starts his update with a lot of statistics, demonstrating and showing how those statistics are influencing the decisions of what we’re going to go about next.The issue is how can we use data and how can we use location-based data? Because everybody's now carrying a smartphone to really identify and control the epidemic.

(03:13) – MemSQL works with a handful of customers to enable social tracing scenarios, estimate the migration patterns that people are having by commuting to work or by going from state to state or taking airplane flights. How can we anticipate where the next outbreak is going to be the most pronounced? Can we really push the numbers down and keep them low? And that requires very good social tracing and contact tracing techniques.

(06:27) – The telecommunication operators do have the data, but not necessarily the technology. And that's where MemSQL is partnering with some of the key telecommunication providers here in the United States and overseas, to enable contact tracing and social tracing by combining the data sets the telecommunication providers have, by the nature of their business, and MemSQL technology to store process and give the full 360 information for social tracing for migration patterns, and for various decision supports that eventually flows back into the politicians, the decision decision-makers, to control the spread of the pandemic. 

(09:08) – There's just so many applications to contact tracing, and COVID certainly highlights. At least one use case there is to control the spread of the pandemic, the effectiveness of that is absolutely unparalleled. This is not going to be the last pandemic. We're going to see more of that and the well developed techniques, technologies that you can just turn on with a flip of the switch will be available and ready for us moving forward. There are certainly plenty of applications for contact tracing, various security applications, terrorists, criminal activities, all of those things. And suddenly it edges at the border of what's that place where we’re giving the authorities too much power that could be the invasion into privacy. 

(14:36) – It's a part of social responsibility. In my preferred and ideal world, those contact tracing apps are just pushed on you by the device providers, by Apple and Google. And of course it's a consent. So you can reject it or you can accept it. And that would be my preference, but I think it goes into the same category as wearing the mask. Downloading a contact tracing app is a very straightforward thing for you to do, so you basically do it and forget about it. 

(16:30) – We live in the post COVID world and we'll be working from home quite a bit. We're going to get so good at understanding and controlling this pandemic through a combination of rules and guidelines such as 60 to part, wearing a mask, installing a contact tracing App on your phone. Something that is simple to follow and something that society accepts. And then we're going to get very sophisticated in tools that give us very good insight about what to do and what not to do. And if something is working or something is not working.

(19:01) –  There is public data and there's data that is guarded by whoever owns that data. And for public data, we need to have open techniques for securing and anonymizing that data. So you either lock the data down and doesn't give access to anyone. And they are responsible for the security and safety of that data, that the bad guys won’t go and break into it.

(23:04) – When you think about data management, a typical solution includes the ability to capture, store and process data. The right place to store and process large volumes of data are in the cloud and the way it works under the hood. You can assemble sophisticated systems. And those would allow you to, like I said, store that data, analyze, process that data, transform these data and build applications. That fundamentally delivers you beautiful user experience, they give you interesting insights or they crunch data under the hood and they present you with some sort of decision support for whatever you want to do with that data. They generate insights. MemSQL is that modern data management solution or a database that lets you store an unlimited amount of data and lets you build applications that are data-centric. 

(27:22) – There's a bit of a race right now in the markets to become the number one hybrid cloud provider and all the public clouds participate in the rate and the race. We're decisively hybrid, and you can consume MemSQL using Helios, which is our managed service by going onto our portal, clicking on the Helios button, and then a few clicks later, you're able to consume our data management technology in the cloud, but we are also offering Helios Hybrid Cloud, which is in a way, do it yourself cloud.

(31:24) – The right choice for your solution really depends on the scenario. Think about what technology gives you today and what technology is going to give you tomorrow in the short, medium and long-term. Understand what you need to solve for today, but also really think about what you need to solve for tomorrow and marry that with where the technology is moving towards in general, and use that as a guiding star from making the choices for data management or really anything else.

(33:52) – A lot could be accomplished through technology. And in order to do that, in order to deliver that value, you need technology and you need people. Then you need people who know how to use that technology.  There's plenty of work for information workers, for talented individuals, for data scientists and smart politician with call for help to the frontline medical workers, but also call for help to the information workers. 

(36:59) – We're late to the party. What happened in California and specifically in San Francisco, San Francisco was one of the first places to impose a shutdown and the numbers speak for themselves. So it was done in a timely fashion. And we had one of the fewest cases compared to the rest of the country. The government is also incredibly resistant to the local government opening up.

(39:48) – The big tech, Apple, Google, Microsoft, and Facebook, I think have tremendous amounts of power and a tremendous ability to help both with the technology. And there's just the vast reach of that technology, and the checkbook. The small tech, in my opinion, should be volunteering more.

(41:30) – If we just never go back to the office, once the social capital is spent, It's not super clear to me if this is going to continue working just as well as it used to before. So that's why I'm looking forward to reopening. 

(44:44) – It's a defining moment for startups. That's where the borders are redrawn. And those who emerge from this, the strongest, will benefit for years and years after as a trust test, like COVID is bringing to the industry, that's the lens that we view our market. there's certainly a lot more fantastic people on the market that we can hire that bring those opportunities together. And because startups are nimble by nature and the decision makers are few, let startups actually seize those opportunities. 

(46:54) – Look at this as a stress test. I know that stress tests are good, if you survive them and you emerge stronger after it, that's really the focus for us. And that's what I wish the rest of the tech industry was going through as well. 

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Nikolas Badminton is the Chief Futurist at Futurist.com. He’s a world-renowned futurist keynote speaker, consultant, author, media producer, and executive advisor that has spoken to, and worked with, over 300 of the world’s most impactful organizations and governments. He helps shape the visions that shape impactful organizations, trillion-dollar companies, progressive governments, and 200+ billion dollar investment funds.

Episode Links:  

Nikolas Badminton’s' LinkedIn: https://www.linkedin.com/in/futuristnikolasbadminton/ 

Nikolas Badminton’s Twitter: @NikolasFuturist

Nikolas Badminton’s Website: https://nikolasbadminton.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:37) – about the age of 10, I started programming computers and I flunked out of school. I eventually ended up in a program called Applied Psychology and computing at Bournemouth University. I got a Bachelor of Science in that degree. I also went into linguistics and artificial intelligence and using artificial intelligence to do a grammar checking and grammatical investigations. Then I dropped into the data world, massive data infrastructures using analytics, behavioral targeting of customers using data, then I started to be hired to speak about artificial intelligence, and we really got into talking about the human ethics and the hybridity of humans and the machines. 

(05:04) – Something that can act as a human, move as a human, perceives, creates its own philosophy, creates some purpose… we are a long way from that. I questioned people that are trying to give that to machines. We can't work out what it truly means for ourselves beyond a metaphysical and a discussionary, a philosophical bent.

(06:59) – This is about humans. This is ultimately about a hybridity between humans and technology. They're not robotics that are independent from who we are, that are suddenly trying to take over the world. There's actual practical applications that are going to help us solve big problems.

(09:19) – Artificial intelligence just doesn't wander off and becomes useful. It needs a lot of training. It needs a lot of guidance and a lot of that practical expertise. It might be able to start identifying patterns that we may not see as readily or as easy as AI, but our practical wisdom needs to be injected into the overall solution. 

(12:06) – COVID is a black elephant. The elephant in the room and the black swan. If you've got a black elephant, it's that black swan that's been in the room for over a hundred years that everyone knows, that there's a risk of it out rearing its head and causing a huge calamity, but we've just conveniently pushed it to the side and decided that the likelihood of that happening is a lot lower than we really want to pay attention to.

(15:44) – If you've had that level of a focus and investment in artificial intelligence, in weapons systems, imagine if that reality of Skynet becoming Sentient becomes an actual reality. And maybe it's seeding the black elephant with some really heinous code or training that's been done by someone that's got a grudge.

(17:18) –  Using machine learning and data and analytics to make predictions using other practical solutions or a way from the normal ideas of technology. Climate change is the best example of a black elephant in the room.

(23:55) – In America, culture is freedom. And anytime you tell me that you're taking my freedom away, I'm going to say, well, you know what? screw that. And that's the mess that America's got itself into. Singapore is very small. It can be contained and they've got ironclad rules around that. America's very big. And the idea of freedom isn't a bad idea. And democracy isn't a bad idea. This virus doesn't care about democracy. It doesn't care about freedom. It doesn't even care to infect humans. It just does it.

(26:42) – This is going to go far and wide. It's our response to it, our ability to treat the virus, our ability to have healthcare that can help people that have it get over it in the more extreme cases, for us to take things seriously and to stay at home. We can see cases for years of COVID-19.

(30:05) – If you don't shake hands and you stand at distance, if you're in the same room as someone or in the same open space, you've still got those mirror neurons firing. You still got that attraction, whether they're friends or lovers or potentials in either of those cases. And that's a pretty good step towards keeping social cohesion. Humans love to be around others. They just like the sense of human touch. And obviously, we're going to get back to that world.

(33:35) –  World leaders are clamoring for hope. They're trying to calm everyone down that there is some light at the end of the tunnel. I'm hopeful that we're going to get that. There's very smart people in the world working together. Artificial Intelligence is playing its role. Analytics is playing, so big data and data science is playing as well. These practical uses of artificial intelligence are really why we're here and why we're talking about this in this podcast and beyond. 

(36:06) –  We've got to remember who's behind these solutions as humans, and even with the best machine learning and data sets, it's humans that are shaping the future and we're going to continue to shape the future.

(38:10) –  This is not the absolute future of work. The absolute future of work is a fundamental reprogramming of how the industrial world works and gets out of the way for a true digital evolution of biology, communications, transportation, and energy.

(41:51) –  I don't mind the idea of robots. What I don't really like about the idea of robotics is that we're trying to get them to do things that are so human, that it is driving us backwards in terms of progress. Robotics have got a huge role to play in the world. We need to stop chasing human style robotics that are suddenly going to walk like us and talk like us and just get back to basics on robotics that just do one or two things really well and without our intervention. 

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Aswin Rao leads Target’s global Artificial Intelligence team responsible for products involving Demand Forecasting, Inventory Planning & Control, Price Optimization, Personalized Recommendations, Search, and Marketing Science. He’s also an Adjunct Professor in Applied Mathematics (ICME) at Stanford University where along with research and teaching in Reinforcement Learning, He directs the Mathematical and Computational Finance program.

His career has been to create or boost business profitability through advanced Mathematics & Engineering by recruiting and mentoring rare talents, foster a vibrant team culture, focus on the right business problems to solve, and meet challenging goals through diligent prioritization. His educational background is in Algorithms Theory and Abstract Algebra. His teaching experience spans topics across Pure as well as Applied Mathematics, Programming, Finance, Supply-Chain, Entrepreneurship. His current research and teaching focus is A.I. for Sequential Optimal Decisioning under Uncertainty (particularly Reinforcement Learning algorithms).

James Jeude’s as an executive carries a record of growth and success, bringing Cognizant a 10x growth in data & analytics services revenue in the decade. He was on the management team, leading distinct P&L practices, and driving thought leadership and public perception. Creating all-country all-industry best practices for his clients gives him a perspective any company can use in an era where consumer experiences in one industry carry over into expectations for an unrelated industry.

Episode Links:  

Ashwin Rao’s LinkedIn: https://www.linkedin.com/in/ashwin2rao/ 

James Jeude’s LinkedIn: https://www.linkedin.com/in/james-jeude/ 

Ashwin Rao’s Twitter: @Ashwinraoarni

James Jeude’s Twitter: @JamesJeude

Ashwin Rao’s Website: https://www.collabera.com/ 

James Jeude’s Website:https://www.cognizant.com/  

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:35) – Ashwin Rao is the Executive Vice President at Collabera,  a $750 million IT services and staffing company. We are a high growth, innovative ID services and solutions provider, headquartered in Basking Ridge, about 16,000 people globally and 60 offices around the world. Tech 2025 focuses on experiential events and discussions to try to start conversations inside of companies. In that group, Jeude manages the consulting and strategy work that follows. He’s also an adjunct professor at New York University, an engineer by training, and a speaker and author on workforce topics.

(02:39) – Our entire business is dedicated to helping clients meet their needs for everything from precision staffing of individuals to bulk staffing and solving business function problems. So we had two challenges. One, we had to rethink how Collabera operates in a work-from-home model. And two, we had to redesign our offerings for clients facing new challenges. This is so much more than just disaster recovery. No one had a plan to empty every office, everywhere.

(03:21) – We believe that our workforce is the very heart of business. Businesses are the very heart of what keeps society running. And the topic itself is really part of the solution to our challenges, not merely a distraction.

(04:10) – We see the virtual workforce model having five key parts: the places we work, the way we work, what we work on, demand management and the transition while undertaking this virtual workforce model change.

(06:38) – Everyone thinks of when they hear virtual is working from home. You can't just assume, “well, we're all on a computer anyway. It doesn't matter where the computer is”. There's more to it than that. So we wanted to think about the places we work and when we come back after COVID-19, we probably still need some way to connect as people. 

(07:50) – Second point is the way we work. And I'm telling you, it should be completely rethought. If we can work virtually and remotely, the entire structure of teams and deliverables should be rethought. 97% of our companies now use Agile for software, but almost no one uses it outside. We have applied agile pod techniques to much of the virtual workforce model we use. Working in an age of AI and automation should become bigger, not smaller. By that, I mean that vendors, partners and teams should be given larger chunks of work with broader outcomes to help distribute the risk and reward of managing uncertainty and choosing the right approach in innovation. 

(08:57) –  As society tries to reboot, get supply chains moving again, some will start, some will stop. Now it might be tempting to reduce capacity when demand is down, but if it bounces back, you've got lost revenue.  If you have capacity that's higher than demand then you have wasted resources. And that is the eternal question.

(09:58) – How to manage transition is a key element. And how you make good use of the idle moments as capacity stays in place waiting for demand to return. For companies that conduct knowledge, work and value added business processing, we believe in training, ideally training teams to a common goal. 

(12:33) – The identified five key elements of flexibility: One is location flexibility. Two is skills independence. Three is team upskilling. Four is platform independence. Fifth and last is team collaboration. 

(15:38) – Agile explains how a corporate team gets down into initiatives and then into epics and then daily tasks called stories. Agile method encourages and demands that teams cooperate closely, commit to local problem solving when possible, have frequent feedback up the chain and flow research and testing results among other teams. In a non-programming environment, these same principles apply.

(17:43) – In a virtual workforce model, we can actually mitigate that equation a little bit. We can. In fact, we can mitigate it a lot. The extremes of this model can be dampened down by having variable resources that are applied to augment the fixed capacity. If demand occasionally rises above capacity, use trusted partners or flex teams to add capacity. If the demand drops below capacity, do not. We recommend dropping your capacity to match demand, because you might get it bouncing back sooner than you think. Educate the team, redesign processes, cross skill upskill, bill collateral, bill documentation and work on internal projects.

(21:54) – Technology and good process design can make even healthcare delivery a candidate for a new workforce model. If it works in healthcare, it might very well work for the offices and functions of the listeners you have. 

(25:09) – From virtual workforce to virtual digital talent, to virtual contact center to a data visualization, all these services have been given a COVID accelerated response offering, in terms of how we can work in such an environment.

(26:24) – COVID task force in these issues, this has to be top-down and cascaded to business units. Do not leave it to each worker to decide how chill or how manic they're going to be at home.

(27:51) – We strongly advise companies to begin now to look at the obligation aside from work from home and not cost on the adrenaline that came from managing this crisis so far, we have done it so far and we will continue to do it. Think back and see what we need to do for planning a better work-from-home environment.

(28:49) – Your clients and your employees will come out of this with new expectations and they don't match your old methods. Without adopting the principles that we're talking about or something similar, both the revenue side, that is the customer expectations, and the cost side, that is your employees and their expectations, are going to change dramatically.

(29:55) – Our clients are demanding how the future is going to be and how we, as their partner, can help them take it to the next level. So we are excited while this opportunity came about, because this is not a great issue out there, but we are excited that companies are thinking differently and we have a role to play here by being agile and by helping them get to that model pretty soon.

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Jerry Ting is the CEO and Co-Founder at Evisort Inc. He is a former Board Member at Harvard Law Entrepreneurship Project and Harvard Association for Law & Business and was an Account Executive at Yelp.

Episode Links:  

Jerry Ting’s LinkedIn: https://www.linkedin.com/in/jerryting/ 

Jerry Ting’s Twitter:  @JerryHTing

Jerry Ting’s Website: https://www.evisort.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:36) – Law is a super fascinating industry in the sense that it's one of the last ones to typically adopt technology. Nothing with automation, artificial intelligence, business intelligence. But then you go into the law firm environment or the legal environment, and then we step back 10 to 15 years in technology. Legal tech is one of those morphous terms that emerged recently, but it's a new wave of technology that addresses the question of how to make lawyers more efficient.

(04:23) – There's a really big market opportunity to both modernize and also look forward, bringing in automation and artificial intelligence to help an industry that provides a lot of value, but hasn't adopted technology in the way that financial counterparts have.

(06:14) – Law firms bill on an hourly basis. If you bring in tools that save 80% of time, that might not necessarily be all good for a law firm for an in-house counsel, for a lawyer at Microsoft, for a lawyer at, and name any big firm, they're driven by traditional business KPIs. Being more efficient, being able to help close deals quicker, removing roadblocks for sales and procurement. These are good things for in-house counsel. So we focus on in-house corporate counsel. 

(09:21) – It's actually easier to change technology than it is to change people's minds. We think we can provide legal services, whether it's tech-enabled or with alternative billing models. There is a large opportunity for disruption in the law firm space. 

(10:54) – Microsoft is an investor. And the Evisort part of why that's exciting is that almost 80% of our customers use SharePoint or Microsoft teams to store contracts in one way or another. One of the main use cases is taking data that already exists in the cloud and activating it using machine learning and AI. 

(11:45) – One is for helping accelerate deals, helping accelerate how quickly a sales team can close contracts. We can provide a layer of automation to review contracts for proof. The other one is vendor management. Being able to see across a billion dollar supply chain, software license agreements to be paid, to be cancelled, to automatically renew, all in a calendar format and visualizing it. And the third one is one that encompasses both of the previous, which is bringing data to lights. 

(12:21) – A centralized enterprise repository where, regardless of where your contracts are stored, sales contracts could be in Salesforce. Employment contracts could be in Workday. Vendor contracts could be in SAP Ariba, but one centralized place where management can go and find and run a report and gather insights about their contracts across the entire enterprise.

(13:18) – Our AI technology does a couple of things. We can take a scan of the contract that we've never seen before, convert it to a Word file and pull out over 50 different data points, including who the contract is with, when does it expire and what are the key legal terms. We can do that all today. From a content analysis perspective based on benchmark data, how to optimize this contract is the next level of intelligence. 

(15:44) – We understand what the customers need and then, we go to our research team and we already have models that we built that we'll test with. And most of them are deep learning models, a lot of research being done on natural language processing on computer vision. We test it on the existing models that we have. And then, if the accuracy is not where we need it to be, we start to tune that model and then add additional features.

(18:57) – We've invested a significant portion of our R&D budget in building out a proprietary dataset that now spans hundreds of thousands of labeled data points. And the modeling then follows that. But without a large enough data, you might be building a model for the wrong subset of data. It might be under a fitted model. We're creating training data that customers may not have ordered yet, but we know that as a phase two and a transformation project they may need.

(21:40) – Historically, contract management and AI vendors have focused on the things to do after you sign a contract. We recently announced a full collaboration platform from generating a contract, to negotiating it, to getting it approved, all assisted by AI. That's now available to all of our clients. We are the first company to go end-to-end from the creation of a contract all the way through renewal, all AI assistants all in one platform. 

(25:55) – There's a big difference between SAS companies and AI companies. Our idea is to combine the two. Combine deep AI analytics that were traditionally meant for large enterprises working with consultants. Democratize the AI that's easily digestible and verticalized for business function and then wrap it in a SAS platform so that anybody can use it. AI companies mature, they're going to build more end-to-end SAS platforms. And, it is going to be hard for the SAS platforms to build the AI capabilities. And that over time to merge into end-to-end SAS and AI platforms. 

(25:12) – The Bay area is world-class for scaling companies. The leaders and go-to-market and marketing and sales and customer success, product management, the go-to-market team in the environment that we have in the Bay area is hard to compete with, including New York. But New York is actually one of the main bases for customers. I try to get the best of all three regions, deep research out of universities in Boston, meeting with clients in New York, and then also running my office here in California.

(28:02) – To be a Forbes’ 30 under 30 has given us some credibility and some recognition for the work that we're doing. We were never doing this as a hobby, we always believed in the vision and our ability to execute and then being named to the Forbes list was a validation for the efforts that we had so far. And then shortly after Microsoft and Vertex and other VCs invested $15 million. The 30 under 30 was a way for us to go out to our colleagues, peers and say, take a chance at Evisort and join us. We're here working on something cool, something meaningful and something impactful.

(31:09) – What's happening a lot with verticalized AI applications right now is it's removing some of the tedious parts of a person's job, but it's actually making that person more effective in doing what they were supposed to do in the first place. I don't think AI is going to replace people's jobs. It's actually going to replace the points that people didn't want to do in the first place, so they can spend more of their time doing the strategic work.

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Daniel Pianko is the Co-Founder and Managing Director of Achieve Partners. Pianko also serves as Managing Director of University Ventures. With nearly two decades of experience in the education industry, Pianko has built a reputation as a trusted education adviser and innovator in student finance, medical education, and postsecondary education. 

A frequent commentator on higher education, Pianko’s insights have been featured in national media outlets including The Wall Street Journal, CNBC, TechCrunch, Inside Higher Ed, and The Chronicle of Higher Education. He began his career in investment banking at Goldman Sachs, and quickly became intrigued by the potential of leveraging private capital to establish the next generation of socially beneficial education companies. 

After leaving Goldman Sachs, he invested in, founded, advised and managed a number of education-related businesses. He also established a student loan fund, served as chief of staff for the public/private investments in the Philadelphia School District, and worked as a hedge fund analyst. Daniel Pianko graduated magna cum laude from Columbia University, and holds an M.B.A. and M.A. in Education from Stanford University.

Episode Links:  

Daniel Pianko’s LinkedIn: https://www.linkedin.com/in/daniel-pianko-947223/ 

Daniel Pianko’s Twitter:  @danielpianko

Daniel Pianko’s Website: https://www.achievepartners.com/  https://www.universityventures.com/  

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:46) – COVID is going to do a massive experiment in taking millions of learners online in the space of a week. Online education in the US will get to maybe 50% of people getting their content online. It’ll be a second massive evolution revolution in learning at all levels, as those online environments will become even more robust, even more like a replacement for the in-person. In-person education is going to go away.

(05:17) – Almost no Ed Tech platform has their own video interface, but Zoom is never going to build out the ecosystem that's required to actually run an online school. Packback uses an AI system to basically put it up. It uses AI to allow professors to grade online discussion, because you're not actually looking to grade very detailed work. 

(08:25) – You're seeing technology bring massive consolidation. And that is happening in education because an online learning environment has to scale, and scale is a different beast in the online world. We're going to have to move these things online and it's gonna reward scale in a way people are not ready for in the traditional education consumer market.

(12:15) – People don’t quite realize how important schools, K-12 schools, physical schools are. They don't have digital connectivity. I would strongly encourage schools to look at organizations like K-12 and other online environments to figure out how to solve these equity issues. Especially, if it means getting technology in the hands of these kids.  It's a failure of leadership that we can't get these devices and the internet connectivity in the hands of our students, and I know it's hard, but that's no excuse.

(16:32) – It's important that we differentiate between the aversion of online education that people are experiencing this week versus a real online education, because online education shouldn't have to be synchronous. 

(18:29) – Predictive analytics is not quite AI. We're able to dramatically open the funnel rethinking the entire classroom experience, technology experience, that led to a predictive analytics revolution in education, in medical school education. And now we admit students or we're starting to admit students based on their success in the MSMS. You can transform equity issues through technology and through predictive analytics and through AI.

(23:30) – Adaptive learning has been the buzzword in education broadly, for the better part of 25 years. And even before then, some really great work was done down by very famous education professors who basically said there are different ways people learn. I'm not a technologist, but what is important for tech hardcore, techies, to understand is learning is still one of those fundamentally human endeavors. We have failed. And the reason why is because the technologists and the educators aren't connected enough.

(25:45) – We're not where online education or AI driven education is totally worthless and meaningless. We're at this kind of in-between stage where the most successful interventions are going to be those where the technologist and the education folks can come together and say, here are the areas where we can deliver a high quality program that radically improves the product and it's going to be high-performance.

(28:09) – Adaptive tests are a perfect example where technology works really well. Psychometricians can basically prove it. That's a better model for testing because it levels out where you're going to end up and allows you to drive a better outcome. While the actual instructional component will stay fairly human centric for the foreseeable future, a lot of these back office, I don't call the admissions office back office, but the non-straight academic functionality will become much more consumer-friendly and tech-driven and where AI can have a massive impact.

(33:02) – People learn differently in different components. Sometimes I actually really prefer online learning. I'm actually not a believer that COVID is going to radically change human existence. I don't think technology fundamentally changes that. I do believe that the vast majority of humans want human to human interaction.

(38:26) – The skills gap is massive and it is not going away. There are vast areas where the connectivity between education and employment has broken down. And we see a future where a series of intermediaries develop,  intermediaries that solve the education friction and the employment friction.

(45:39) – Software is eating the world and it's changing how everybody operates. But at the end of the day, things around education and workforce are very human-driven. And there's a push to automate the job search and education processes.

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Daniel Whitenack is a Ph.D. trained data scientist working with Pachyderm. Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, Spark Summit, PyCon, GopherCon, JuliaCon, and more), teaches data science/engineering with Purdue University and Ardan Labs , maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.

Episode Links:  

Daniel Whitenack’s LinkedIn: https://www.linkedin.com/in/danielwhitenack/ 

Daniel Whitenack’s Twitter: @dwhitena

Daniel Whitenack’s Website: https://datadan.io/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:13) – Being online is pretty normal for myself and my team. I am fairly often on calls with people all across the U.S. but also in Singapore, and India, and Africa and all over mostly via zoom.  

(02:55) – Our India teammates went fully remote from their office cause they're all programmers and software engineers and that sort of thing so they're all working from home. 

(03:56) – What's really boosted NLP in the last couple of years are these large scale language models, so oftentimes what you'll have in an AI model and that's processing text is you'll have a series either one or a series of encoders for text classification. What's really been interesting is these sort of large scale language models that have been trained like GPT-2 and BERT and ELMo, and there's a bunch of other ones. They're trained on a massive set of data, even sometimes for multiple languages, such that you really can apply that model to a wide range of tasks by just fine tuning to one of these tasks like translation or sentiment analysis, or text classification with a much smaller amount of data than was required before. That led to this explosion and application of AI and NLP

(06:12) – The size of the models has increased a lot and they're processing a lot of data. These word embeddings or these representations of texts that are learned in the model encode a lot about language in general so it's been shown in a couple of studies that you can backtrack out of these embeddings, the actual traditional syntax structure of texts that linguists are familiar with like grammars and such and so in these embeddings is encoded a lot of information. 

(08:07) – Transfer learning depends a lot on that sort of parent model that you transfer from and there are sort of very multilingual models out there some including up to a hundred and 104 hundred nine languages maybe. There's actually 7,117 languages currently being spoken in the world. if we think about a multilingual model that has like 104 languages in it and it's Embeddings that it's language model supports, that's a drop in the bucket and some tasks like speech to text, or text to speech especially in NLP platforms only support maybe 10 to 20 languages and so there's a long way to go in terms of NLP for the world's languages. 

(11:29) – I'm really hoping that what we start to see in 2020 is a an acceleration of this technology through the long tail of languages because with 7,000 languages if we tackle like one language every six months or 12 months or something like that it's going to take us a long time to support things like translation or speech to text in 7,000 languages, so I'm hoping that we see some sort of rapid adaptation technology come about in 2020 that will let us tackle, 40, 50, a hundred languages more at a time.

(13:46) – Teams that are starting to leverage that those existing resources, which really haven't been tapped into I don't think because they're archived in weird ways they're not in the sort of formats that like AI people typically are used to working in, so we're just at the tipping point where we can really jump in and utilize a lot of that data in creative ways. 

(15:17) –  There are certain languages that maybe aren't being used in the same way that they were before. There's other languages that would be used digitally, they're just not supported yet and there's economic concerns and literacy concerns and all of these things all wrapped up and so we have a lot of data around all of those things.

(18:09) – For chatbots in general, I would say that there's less support for those than there is for a general technology like Google Translate or machine translation. So it's fewer languages than that, but you can do, again, some creative things to bridge the gap, like doing some of this transfer, learning and other things to build custom components under the hood to support new languages. whoever does crack the nut of rapidly.

(22:38) – Imagine going into a new language community with a virtual assistant, imagine if that virtual assistant had the ability to query a natural language, that could enable there's still other pieces of that puzzle, like document search and that sort of thing but this is a big step in the right direction. 

(26:40) –  There's a lot of disruption and that's definitely true and there's a lot of people experiencing real suffering out there but at the same time there also some new opportunities that are arising. 

(36:15) – Our show is really focused on as you might have guessed the practicalities of being an AI developer these days and not only for those that are currently AI developers, but those that would like to be AI developers so we dig into a bunch of the different technology

(38:03) – Reinforcement learning and generative adversarial networks scans both of those technologies get a lot of hype because of some of the things that they power like deep fakes and other things we haven't really entered into a season where reinforcement learning and GANs are really powering a lot of enterprise applications the way that deep learning models have actually penetrated.

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Gideon Mendels is co-founder and the CEO of CometML. Gideon is an experienced data scientist and entrepreneur. He worked on Deep Learning research at Google and Columbia University and previously co-founded Groupwize.

Episode Links:  

Gideon Mendels’ LinkedIn: https://www.linkedin.com/in/gideon-mendels/ 

Gideon Mendels’ Twitter: @comet_ai 

Gideon Mendels’ Website: https://www.comet.ml/site/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:06) – Some people call Israel the start-up nation. New York's the new Mecca and it's the Mecca of technology.

(02:46) –  To build a better model, especially if you're inheriting an existing one you try to figure out what people did already. You don't want to reinvent the wheel. You want to see what works, what doesn't. Where's the exact data set.

(04:27) – We eventually collected most of the information, but we started from scratch because we wanted to make sure we're not basing our assumptions in something that might have been inaccurate. 

(04:54) – We found another approach that was much simpler than what we had in production. When you don't have the right processes and tools, it's really hard to bring ROI on these efforts.

(05:29) – We have this amazing stack of tools, anything from testing, monitoring, orchestration, CI/CD, Versioning, you name it. And there's a lot of, sometimes, maybe too many, but then you go to machine learning teams and both of them are still using a combination of scripts, notebooks, and emails. And that's a fallback. There's definitely a better way to do this. It is exciting that developer tools and machine learning are helping these bigger companies to build reliable missionary models.

(06:16) – Comet is a meta machine learning platform designed to help these machine learning or AI practitioners and their teams to build machinery models for real world application. The platform allows these teams to automatically track and manage their dataset, their code, experiments, models, as we solve problems around reproducibility, visibility, efficiency, and loss of institutional knowledge.

(07:31) – Some engineers think Machine Learning is basically software engineering. But in machine learning, code is just one small piece of the puzzle. You have data, you have experimentation, you have results, you have models and models in production. But at the end of the day, these are different processes. And for that, we need different tools and different methodology. 

(08:57) –  Our approach has always been to be agnostic to what tool to use. We work with any type of machine in the library, whether it's the common ones, Perch, TensorFlow, scikit-learn, but even if you have something that's completely custom that you built in your garage or in your organization, you can still use Comet.

(09:32) – Pick the best tool for the job but still have one platform where you can see everything, you can compare your results, you can share them, you can collaborate. So very similar from that perspective to what GitHub did for code, we're doing for machine learning.

(11:36) – Python has definitely been the most dominant language on the machine learning side of things. We still see quite a lot of R users. Mostly those with a more traditional statistics background, but we also see people training models in things like Java.

(12:31) – You can see the emergence of low-code or no-code solutions. Those will become more and more popular as we go, as well. 

(13:46) – Deepfakes, like with every new technology, are an amazing technology that is used and can be used for really great things. There's no question that people can abuse it. There are some similarities to hate speech in the sense that we will need to use machine learning to detect them. But we would need to make sure we set some kind of policy.

(16:16) – We have major enterprise customers, multiple Fortune 100's across industry. We have some big tech companies, finance, automotives, media companies, biotech, retail, even manufacturing. We do have dedicated models and the platform to look at, computer vision problems, looking at your model predictions and debugging them same from natural language processing, tabulary data and audio. But we're not limited to a certain use case.

(18:37) – We recently announced a partnership with Uber AI Labs which developed a really unique product or library called Ludwig. Ludwig is a no-code machine learning library. You kind of define the specification of the models without coding anything. And then you can train your model based on that. And Comet is the built-in experimentation management tool for that. 

(21:31) – For ancestry, one of the key things is they have Comet as the central place for their team to track their machinery and experiments and debug them. One of the biggest challenges in machine learning is debugging these models.  It's about figuring out where your model predicts the wrong results.

(22:39) – One of the biggest value propositions in Comet is that they can look into the results of the model and track predictions over time and better understand what's going on and how to drive the research process forward. You look at the results. You decide that this model is not doing any good. Just you click a small stop button. It's very simple, but it's very valuable if you're trying to move quickly. 

(24:14) – Transfer learning falls under the subfield of meta machine learning; using machine learning to improve machine learning.The idea with transfer learning is by using a model or a training and a much bigger data set, you can get much better results than with your smaller one. This has two advantages: the ability to get a better result in your data set, but also saving a lot of costs.

(29:11) – The predictor is an early stopping mechanism. We try to predict where your model is going, and then once things look like they're not going anywhere, or the model has converged or that the line essentially flattens in a way, you stop the model. You reiterate and try to figure out the next step. And that's essentially the research process. We can actually automate this process, and you get to move 30% faster.

(32:48) – Product side, instead of trying to solve all the problems in this space, to build one end to end solution that does everything. If you have one platform that replaces AWS, New Relic, GitHub, Jenkins, all the tools in the world, one product with one login, that's something that's very hard to do.

(35:07) – Machine learning is essentially becoming another tool for engineering. Things will definitely converge. And if you look into undergraduate programs, for example, machine learning and AI have become part of the core curriculum.

(38:09) – If you're trying to classify some examples you can go back and do the data labeling process and get more data from this class and drive the research process for it. In production time, you won't be surprised anymore because you already looked into all these edge cases and solved them in training. These are the two main approaches people in the industry are taking.

(43:16) – It's very important not to get married with a single library, use the best research that's out there. 

(44:11) – Overlap and collaboration between academia and industry. That convergence of being able to support both ways is very exciting. More companies are being able to get real business value from machine learning and AI.

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Mike Robins is an author, speaker, coach, and podcaster who delivers keynotes and seminars (in-person and virtually) to groups of all kinds throughout the world. He’s written five books, which have been translated into 15 different languages. His latest, We're All in This Together, has just been released. Before starting his business in 2001, Mike played baseball at Stanford University and then with the Kansas City Royals organization. After baseball and prior to starting his consulting business, he worked for two tech companies in online ad sales during the "dot-com boom" of the late 1990s.

Episode Links:  

Mike Robins’ LinkedIn: https://www.linkedin.com/in/mrobbins/ 

Mike Robins’ Twitter:   mikedrobbins

Mike Robins’ Website: https://mike-robbins.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

– Twitter:  https://twitter.com/dyakobovitch

– Instagram: https://www.instagram.com/humainpodcast/

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/

– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:50) – It's definitely called upon a lot of us to dig deep and think, what can I contribute to the world? There's a desire for us to connect, even though we're all separated by time and space more than ever these days.

(03:05) – In some ways the rules have changed very quickly through this pandemic, we're all getting a chance to connect with and see each other's humanity as people are trying to do work from the dining room table and their kids are off to the side.

(06:52) – There's a lot of companies, especially tech companies who've been needing to embrace video and other platforms in order to communicate. There are a number of companies that we work with for which that is the primary way they communicate anyway, prior to COVID. That's going to allow us to work in a more effective and productive way, but also humanize the technology if you will, because that's been one of the challenges over the years and continues to be. 

(09:04) –  Everybody is panicked in the sports industry because they don't know what's going to happen. They're just hoping and waiting for things to get back to normal, but it's impacting a lot of people's lives and a lot of people's jobs in the short term.

(11:47) – The technological capabilities of schools are often dependent upon the socioeconomics of that school or that community. Not everybody has access to the same technology and so just as that exists in school in person, it also exists online. The education experience, just like the meeting experience or the conversation experience, is different when it's done virtually than when it's done in person.

(13:57) – We want an education just like in business, that can be innovative and creative, adaptive and adjusted to the moment.

(16:31) – if you had the best players you would have the best team. If you have the most talented players, you should have the best team. But that was not the case at all and I learned it many times in sports. 

(18:00) – It was the intangible qualities that allowed us somehow, some way to put our little egos aside and be interested in each other's success, wanting to win as a team more than simply just succeed as an individual to cultivate an environment where people can work together in a way that actually brings out the best in everybody.

(21:30) – To be good at anything and ultimately sustain that success and grow, you got to master yourself. We're talking a lot more about mindfulness, both in our schools and in businesses, because we're all dealing with similar macro experiences, even though we have our own little world. Much of our success or failure has to do with mindset and approach. We need some talent, but the same is true in business.

(28:44) – We actually have a lot more common ground with each other than we think we do. We're separated in a way we've never been forced to be physically separated before, and we're simultaneously connected to each other in this global experience all at the same time.

(32:30) – Maslow's Hierarchy, the third place on the pyramid once we get past the physiological and safety needs for human beings is a need to belong. The ultimate  goal is to get to a place where you create an environment where everyone irrespective of their background, their race, their seniority, their age, their gender, their all of that everyone feels like they belong and that's not easy. 

(35:40) – Authenticity and vulnerability is the iceberg. And it's about lowering the waterline on the iceberg so that we share a little more authentically and vulnerably how we're feeling and we really ask other people how are you and not just the corny hey, how are you? How's it going? What's up? Reach out a little bit to offer some support.

(39:26) – Have some compassion for ourselves in the midst of all this, and also have some grace and compassion for other people. There are a bunch of companies and apps and pieces of technology that maybe we didn't realize were super important that are now becoming very important in a different way because of what's going on. 

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Rishal Hurbans is the Business Solutions Manager at Entelect where he is responsible for business development, strategic planning, ideating, and designing and developing solutions for local and international clients; whilst actively nurturing knowledge, skills, and culture within the company, community, and industry. He has a passion for business mechanics and strategy, growing people and teams, design thinking, artificial intelligence, and philosophy.

Rishal is the author of Grokking Artificial Intelligence Algorithms with Manning Publications, aimed at demystifying AI algorithms for technologists by teaching the approaches through practical problem solving and visual explanations: 

Episode Links:  

Rishal Hurbans’ LinkedIn: https://www.linkedin.com/in/rishalhurbans/ 

Rishal Hurbans’ Twitter:  @RishalHurbans

Rishal Hurbans’ Website: https://rhurbans.com/  http://bit.ly/gaia-book 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:49) – “Grokking Artificial Intelligence Algorithms'' consists of 10 chapters that explore different AI approaches. It's part of Manning Publications, MEAP, which means Manning Early Access Program. And the benefit of that is we get feedback from readers as we release chapters, which allows us to refine and create a better book at the end of the day. Once all the chapters have been released and we get some feedback, the book would be then printed and finalized. 

(03:22) – The term Grok or Grokking is to gain a deep understanding about something, but through intuition and through some sort of feeling about it, demystifying these algorithms that are sometimes underappreciated. Including the modern hyped concepts like machine learning and neural networks, to actually help the reader understand why it works and how it's useful to the day to day.

(05:16) – A lot of Funding has gone into creating this kind of skills and capabilities in different organizations. There's a lot of solutions and proof of concepts that have been bolts that work in theory or work in a ring-fence environment, but perform poorly in production or don't provide the value that was originally envisioned. A lot of effort is going into understanding now, what are the critical aspects to what we're doing with this technology. How do we understand it better? And how do we target it or direct it in a better way as opposed to running a bunch of experiments and see what works.

(07:03) –  It's not a lack of engineering or a lack of know-how in actual execution. In any technology that we've built, especially software, at the end of the day, it comes down to solving a real world problem, whether that's a business problem or, whatever the case might be. Usually it comes down to a business problem that you're solving. 

(07:49) – It's not actually addressing the problems in a meaningful way because we just tried everything. Also, partly it's because people have been trying the hype buzzwords, because they're a good idea. And you feel like if you're not doing it, you're doing something wrong. From a global decision-making perspective, the stakeholders involved there, the different people involved, they need to have a better understanding of what problems the technology is solving, as opposed to just simply using it, to implementing it for the sake of it.

(09:40) – The focus on the different algorithms is driven by a theme or concept I mentioned just a bit earlier. So instead of trying any new technique that you come across, I wanted to highlight the advantages of some of the underappreciated algorithms. The goal was basically to expand a technologist or a developer's mind in terms of what the possibilities are when being faced with a problem. There's no silver bullet and here are the advantages and disadvantages of the different approaches. 

(12:35) – Specifically with search, it's mainly exhaustive, you had to try every possibility to find a good solution, whereas, more modern approaches try to estimate a good solution. A person would have to know what questions to ask. What modern approaches and machine learning and deep learning try to do is learn from examples and learn from previously made decisions to figure out the questions.

(14:07) – Modern algorithms are geared towards different problems that we're trying to solve now, but computing has definitely made it possible for things like artificial neural networks to become more prominent.

(16:11) – Large amount of data that's been collected through connecting the world, the actual value that's hidden within that data and the kind of advancements in computing have allowed us to leverage these algorithms. And as I said, old algorithms that can now do some really powerful and useful things. 

(17:26) – The implant search is also sometimes referred to as adversarial search. It's essentially used for two player games like chess, and the whole concept is centered around an agent predicting the future. So if I'm an agent. And I see a certain state of a chess board. I would make a move and then simulate every move that my opponent could make and score that. Games like Dota and StarCraft, they're using something completely different. So they're leveraging reinforcement learning and deep learning. 

(19:03) – You're not working on a two dimensional space where you're moving pieces a few blocks at a time you're working in a very fluid environment. It's almost simulating reality. Detailing every single piece of information and representing that as a state and then trying to predict every possible future for that state becomes very difficult to do with traditional adversarial search approaches.

(19:52) – They try to let an agent learn from experiencing the game. What a deep mind, open AI and similar organizations have done is basically allowed an agent to play itself many times and figure out what short-term actions and mid-term actions may result in long-term rewards. I'd like people to be more pragmatic because the more pragmatic you are, the more effective you are at solving what's important.

(22:49) – Technology, including data science, including software engineering or mobile development or whatever facet of technology we're working in, I see it as a tool or a vehicle to deliver value or solve a problem. There's a difference between a successful project and a successful solution. There's this deep focus on what tools and libraries and technologies and programming languages, and what are you using as opposed to, why are you using it? What are you trying to achieve with it? And that's not just a problem in data science. It's a general theme, but we're getting better as we go.

(25:57) – A big misunderstanding is the glamour in bolding, something with machine learning or AI algorithms about 60, 70% fried, depending on the surveys, you look at 60 to 70% of  data scientists work is usually understanding, cleaning, preparing, enriching, augmenting that data before it becomes useful. And even after you do all that work, you don't actually know if that data is going to solve your problem or not.

(26:58) – Every solution should contain some sort of data science or AI element to it. And that's not really the case. So unless there is a clear use case, not that fits the use of some sort of either classification or reinforcement learning or optimization algorithms. Unless there's a real use case for that, it shouldn't just be taken into consideration. You should think critically about how you can build a minimum solution that solves the problem in the best way. 

(29:19) – I would have spent a technical perspective and a growth perspective specifically in the area of AI and machine learning, I would have made a bigger effort to figure out why math is useful in these concepts. Do not give up on that and perhaps try and seek material or people or mentors or someone that can explain to you in a more human way, how these mathematical principles work, but more importantly, why they're important.

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Eric Adams is a former State Senator, and current Brooklyn Borough President running to be the next Mayor of NYC. He was born in the Brownsville neighborhood of Brooklyn, went on to earn an Associate in Arts degree in data processing from the New York City College of Technology, a Bachelor of Arts degree in criminal justice from John Jay College of Criminal Justice, and a Master of Public Administration degree from Marist College. 

Eric graduated from the New York City Police Academy in 1984 as one of the highest-ranked students in his class. After initially serving with the New York City Transit Police Department, he was transferred to the New York City Police Department (NYPD) with the merging of the city’s police forces.

Episode Links:  

Eric Adams’ LinkedIn: https://www.linkedin.com/company/eric-adams-for-mayor/ 

Eric Adams’ Twitter:   @ericadamsfornyc

Eric Adams’ Website: https://ericadams2021.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators  

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– Facebook: https://www.facebook.com/HumainPodcast/

– HumAIn Website Articles: https://www.humainpodcast.com/blog/

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:29) – We were able historically to get away with the dysfunctionalities of cities. In the next 20 years, as we evolve into computer learning and artificial intelligence, we have to change how we run cities so we can keep pace with that.

(02:16) – The real fact that we're not addressing COVID-19 in real time with real data and real on the ground response is really exposing. Our cities across America and in general, specifically, here in New York, are not prepared to see how you run cities in the 21st Century.

(03:40) – We have a disproportionate negative impact on certain communities. When you look at the term of essential employees, over 70% of these central employees are black and brown people. When we see the decrease or the increase we are talking about specific populations, over 60% of the people who died from Coronavirus are black and brown.

(06:37) –  Free food for all New Yorkers is open to people who are in need of a meal who can't travel far to their community.

(07:27) – We have a large number of people in this city who are seniors. It is our responsibility to teach our seniors how to be introduced into the technology.

(09:11) – Our influence really impacts the entire globe. And here in the city, we're in a fishbowl in that we all live together. Our technology, the technologies that we use must be part of preparing our future employee pool and how we run this city in an effective way.

(11:22) – The population that was less likely to use technology, our senior population, are compelled to embrace the technology that's available. 

(13:30) – Government officials need to make sure students have the devices and the technology that they can remain engaged.

(16:21) – The more we build out using the free wifi, and it should be a right in all communities, the more we learn where our gaps are. And it's important to do a GIS mapping of the entire cities.

(18:25) – It's not a one day strike. It is imperative that as we go through this crisis, we're thinking about rebuilding in the meantime. How do we look at this new norm that we are going to embrace?

(21:11) – The New York City Employee Retention Grant program is a great program because many jobs are being impacted, they want to lose employees. And if you hold onto your employees through this program for a particular period of time, you are able to take the benefits of this program. 

(22:39) –  We should do a 90 day moratorium on rent as well, as long as it's matched together with the moratorium on mortgage payments. 

(26:53) – What we must do is continue to get the information out into the crevices of all of our communities. 

(29:26) – We need to try to provide personal protection equipment to all essential employees. We need to make sure that any employee that's considered an essential employee, that they have some form of healthcare package

(31:55) – You don't have to break your traditional bonds of coming together as a family, we just have to be more creative in doing so. We are a resilient community, city and country, we've had hard times before and all we have to do is come together. Show a level of compassion, commitment, and dedication, not only to each other, but to ourselves. 

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Gabriele Columbro is the Founder and current Executive Director, FINOS at Linux Foundation and Member at Forbes Finance Council. Columbro is an open source leader and technologist at heart, having spent more than 10 years building thriving communities and delivering business value through open source. He thrives in working with open source communities to drive disruptive innovation, whether it’s for an early stage tech startup, a Fortune 500 firm or a non profit organization. Gabriele brings expertise in executive and technical leadership, ranging from FinTech to enterprise collaboration, from developer platforms to SaaS ARR business models. 

Previously Director of Product Management at Alfresco, as Executive Director, Gabriele built the Symphony Software Foundation from the ground up, with the vision of creating a trusted arena for Wall Street to accelerate the digital transformation, engaging in a new model of open source FinTech innovation, backed by the largest global investments banks like Goldman Sachs, JPMorgan Chase, Morgan Stanley, Citibank, Deutsche Banks, Nomura, Wells Fargo, UBS, Credit Suisse. Gabriele is also a PMC Member for the Apache Software Foundation and an advisor for Bankex.com.

Episode Links:  

Gabriele Columbro’s LinkedIn: https://www.linkedin.com/in/columbro/ 

Gabriele Columbro’s Twitter: @mindthegabz

Gabriele Columbro’s Website: http://mindthegab.com/ http://mindthegab.com/

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter: https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:01) – There are some major shifts happening in the industry and all the arrows pointing to open source as a brand new way forward for this industry. There are systemic reasons why we're seeing the rise of open source, same financial services margins. Revenues of nowhere nearly where they were 10 years ago in this industry, the cost of regulation keeps rising. 

(04:20) – So there is not an infinite amount of money to be thrown at every single technology problem in the industry. And open source certainly has had a history of reducing technology costs when using TCO. That's one of the main driving reasons for financial institutions looking at open source collaboration. Open source provides a much larger, much broader talent pool, and allows every individual to continue fostering its own portfolio. Open source doesn't equal free, there's a lot to be saved, but also a lot of money to be made on open source.

(09:07) – This generation has grown up with social tools and a really different way of even interacting with each other. The new generation of developers that we see coming up comes with being born and bred in GitHub.

(11:19) – Open source is not charity. There's an element of conscience, of openness. Everyone, and most corporations participate in open source right now. And even our foundation, they do it with a business goal. So it's not per se charity. it's not just talent acquisition, it’s certainly a lot of talent retention as well. 

(13:04) –The rise of open source out there and the rise of non-profit open source foundations is because open source is not easy. Especially if you're a large corporate who's seeking to collaborate either with its competitors or with its customers and ecosystem at large through open source. 

(13:51) – Code is certainly important. And the quality of the open source code is higher. Everyone feels a bit more accountable for what they put out there than necessarily what you do behind the firewall. But that's just the tip of the iceberg. 

(10:05) –There's an element of internal and external policies. Regulated industries are very understandably risk averse, and very much careful about what degree of collaboration they have with their competitors. That's why foundations like ours provide a very structured governance framework, conflict of interest policies, antitrust policies, making sure that it's clear that through transparency, you can achieve a very productive level of collaboration without any compliance concerns.

(145:39) – Policies are one element. You mentioned standards. The world of open standards and the world of open source had historically been very different, but they are more and more colliding because they reinforce each other. When you add the open source reference implementation to an existing standard, that drastically speeds up the rate of adoption, and certainly the rate of compatibility, cross compatibility through the standard.

(16:25) – The generational cultural aspects include a lot to learn before you can be effective and productive in an open source community, the same way you do it in an internal project. You need to relinquish control in favor of influence. And that's a big step for hierarchical organizations, large corporate hierarchy organizations. But there's also an element of code of conduct and behavior. Open source communities that are driven by meritocracy, or even by the more contribution, the more sweat equity you put into, the more influence you have.

(19:03) – Government is one of the models that we're using for modeling the collaboration in our community. Governance and code governance and corporate governance. All of our governance is public and transparent, which leads us to traceability. Every decision is traceable and is auditable

(24:05) – There is an intention and a goal for the industry to better model the data in a collaborative way to be clear, not only to collaborate on the code itself, on the visual modeler and on the language, but on the models themselves. Create this common modeling tool and common set of data models in the hope that with common data models, we can start building on top of it common tools and common ideally AI and ML and intelligence around it. 

(30:56) – It is understandable how institutions who have not only such a regulatory nature, but very sensitive information about their customers would think twice before sharing information with some of their competitors, whether that's because it's a unique differentiator or even just because of fear of breaking some regulation. There’s lack of data standardization, and we’re identifying more technical solutions to enable shedding in a safe way. Big tech through open sourcing is enabling some of these better collaborations happening also in financial services. 

(34:46) – Open source can be a means for financial institutions to really have an alternative to the usual maker by decision. Its transparent nature, which is a talent pool expanding nature, goes back to traceability. That's really a good driver for financial institutions and fintechs to collaborate in the open. Rather than having to train and specialize people in every single system that you're going to have to go and regulate, you can build a broader talent pool if the implementation and the process is dealt with in the open.

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Lorna Davis has served as President of multinational consumer goods companies for 20+ years, in Danone, Kraft and Mondelez. Lorna has been a key leader in Danone’s purpose journey and is a Global Ambassador for the B Corp movement. In 2017, she served as CEO and Chairwoman of Danone Wave (now Danone North America), where she established that $6 Billion entity as a Public Benefit Corporation and achieved B Corp status in 2018, making it the largest B Corp in the world. 

Lorna is a member of the Social Mission Board of Seventh Generation, the Integrity Board of Sir Kensington (both Seventh Generation and Sir Kensington are owned by Unilever)the Advisory Board of Radical Impact and the Board of the Stone Barns Center for Food and Agriculture.

She has lived and led businesses in 7 countries including the UK, France and the USA and served on the Global board of Electrolux for 6 years.

Lorna was also based in Shanghai, China for 6 years where she was the CEO of the merged Danone and Kraft business.

Episode Links:  

Lorna Davies' LinkedIn: https://www.linkedin.com/in/lorna-davis-3366ab14/ 

Lorna Davies' Twitter: @lorna_davis10

Lorna Davies' Website: https://www.lornadavis.net/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators 

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ 

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:37) – When people ask me how I'm doing i noticed that the answer is only relevant for this moment. I'm variable like everybody else and I'm trying to just take it one moment at a time really. 

(04:17) – What we will also come out of this with is a really good understanding of ourselves, which will be very important for the next phase of the world. We'll be more self aware and hopefully more compassionate and more loving leaders in the future.

(06:31) – Work out how to calibrate, how to be supportive, but now how not to be helpful. Because being helpful is a pain in the neck, nobody wants to be helped. But how can we really provide support for each other at a time when people are still trying to work out what support they want. 

(09:24) – Very interesting to see how businesses are pivoting. I'm loving the innovation that's coming out of this and I'm also loving the new relationships, new collaboration, new interdependence that's coming from this.

(13:26) –We're going to see things that we have never seen before. And we're also going to see a complete reshaping of traditional blocks of time. This sort of neat disruption of the day is challenging for some people. These fixed boundaries between these periods of our lives have dissolved perhaps forever. We'll be easily able to segue away from laying on the couch, reading a book to getting up, to do a yoga class, to doing an hour of work, to going to learn the tuba. There'll be fun. 

(17:27) – The inclusion of people who are shyer than others. With that hand raising function, people who would otherwise struggle to fight their way into a conversation can put their hand up. These are all things that will enhance intimacy and connection that I hope we hold on to when we go back to more in person meetings.

(21:50) –  It was unthinkable before that you and I might build this kind of relationship and never meet. And people have old fashioned ideas about how people need to be face-to-face to really build a relationship. I don't think that that's true.

(30:25) – The big question is if you really want to solve the climate challenge, countries need to work together and they need to have a line of legislation on carbon reduction. They obviously need to sign up to agreements and they need to have a shared view that the world has a problem that the world has to solve together. 

(33:22) – There will be stories that as human activity slows down, natural activity will rectify itself or come back to life. And hopefully we will fall back in love with the world, fall back in love with nature, fall back in love with the universe really. And that'll give us a new sensibility. It is a better, more grounded place to act from when you are in love with other humans and in love with nature than when you're frightened, angry, defensive, and think that your money is going to save you, which is kind of what, has been predominant in parts of the world recently.

(35:58) – The water will deliver us into common ground, or intercommon waters, and then we'll be able to find our ground. Everybody knows that it's chaotic. Nobody can pretend that it isn't. So it is, so let that be.

(41:50) – Women's ability to deal with ambiguity and complexity and interconnectedness is better than men's. This time of ambiguity, complexity, multitasking is the time for them to really step up. 

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About HumAIn Podcast:

The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.

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Fernando Gomez Baquero is the Director of Runway and Spinouts at Jacobs Technion-Cornell Institute and Founder at Besstech LLC. He’s an innovation economist, nanomaterials engineer and entrepreneur who mentors companies on diverse topics such as IoT, digital innovations, new materials for transportation, creating better electric vehicles, improving wind and solar power, using social networks for gratefulness, and and more.

Episode Links:  

Fernando Gomez Baquero’s LinkedIn: https://www.linkedin.com/in/fernandogomezbaquero/ 

Fernando Gomez Baquero’s Twitter:  @FerGomezBaquero

Fernando Gomez Baquero’s Website: http://www.fernandogomezbaquero.com/index.html 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:37) – The most reasonable thing to do initially was to fairly quickly move every single class to online, which we did pretty fast andthe good thing is that we were already prepared for that. Most of our classes were already streaming and we already had a lot of experience doing that. 

(04:17) – We live in a good time that we definitely can move a lot of things to virtual and we are able to shift to that pretty fast. And I hope that everybody knows that by doing that, we can deliver not exactly the same content and continue to work that way. So, this is really a test of the future of work.

(05:48) – Cornell Tech was created as an economic development story or as an economic development driver for the city of New York. Why don't we basically get the best of both worlds? revitalize an area that hasn't been used for a while, which is the Southern side of Roosevelt Island. And then we use that space to bring a campus of a university that is going to focus a lot of engineering and scientific resources to create the companies in the future. And that's the purpose of the campus, focusing on entrepreneurship and creating new companies.

(08:09) – We no longer see entrepreneurship and academia as a binary thing. We don't see it as, you need to do your masters program. And then when you finish, you do entrepreneurship and you build a company in the country. What we see is while you're in the academic environment, you can be doing your degree. You can be working towards your degree, but at the same time, you should be creating a company. And we are more than capable of not only giving you the space to do that, but training you to do that with the people that have done that. So the people that come to Cornell Tech are people that want to have that academic and entrepreneurship experience at the same time, which is a lot of work.

(09:43) – If you take a look at that set of degrees, it is  just the right combination of skills to build the company. And so once you take those people that are, one of them is a computer scientist, one of them is an engineer, one of them is an MBA, one of them is a lawyer and you put them together in teams, you build a very early stage, very good company.

(10:58) – It really depends on where you are in your life right now, what you want to do. If you want to be an entrepreneur our goal is that we will have a program for you. If you are working in a company right now, you'd be working as a program manager or a project manager for a while, and you really want to have that experience of saying, I can give myself a year to improve my skills, know something better. And at the same time, have that experience of building an early stage company.

(13:46) – We give them all of the support that they can get. And as Nanit would really focus on computer vision, we have companies working on genomics on computational biology, on computer vision for construction and infrastructure on a better simulation technologies for spaces. On big data on other types of devices. It's really a wide range of applications. 

(17:01) – Tech transfer is something that has been done in universities for many years. And that the dynamic of tech transfer has really changed for decades. And that dynamic is, you are a researcher, inside of a university system, creating knowledge, that knowledge belongs to the university. And then the university is trying to find on the outside ways of commercializing that research. 

(18:29) – People who are creating the knowledge are the best vehicles for commercializing that knowledge. We trust that you're the one that can make this into a billion dollar company. And what you need is for us to help you succeed, to give you the training that you need, to give you the tools that you need, to give you the resources, to give you the connections, to give you the environment that you need.

(21:11) – We have a couple of our postdocs that immediately switched their companies to say, we can develop better financing strategies for what needs to be done with COVID. We have some other ones that are saying we definitely need to work a lot on finding a test for immune response to COVID. So now we have all of these people working on the health tech side.

(23:15) – We're enabling communication in a different way, but we're also enabling leadership in a different way. We have people working on the future of work this way. We have people that are really building interesting tools for the gig economy.

(25:01) –  There's very few segments of the population that are actually doing artificial intelligence. There's some that are, for the most part, who we're trying to teach our companies. And most of them are either doing some type of some interesting application of machine learning. Perhaps it could be some interesting signal processing or hubs or data mining in a particular way, or using tools like natural language processing and computer vision.

(27:15) – We're still in a very primitive way on how we see machines and interact with them. We have just scratched the surface of how it is that we can improve our interaction with robots. 

(30:43) – We have many tools right now. These technologies, these tools, and just are  great opportunities to use all of that toolset for a very big problem. 

(33:07) – We have people that were product managers and they definitely don't want to be product managers anymore. They want to be entrepreneurs. They want to be CEOs, so this is just a segment of people. We have some that have been product managers and they want to continue to be product managers, but they want to raise their skill level. Now you can be an entrepreneur, you can be a product manager, you can be a CTO, you can be other things. And this really what we want, to open up possibilities for a career. 

(36:23) – Studio is really, the most innovative part of Cornell Tech. The core idea is that you can practice entrepreneurship while you are in academia, but practicing the real way, meaning that you could be driven to entrepreneurship and you can have that experience of being an entrepreneur at the same time that you are in academia.

(39:15) – There are a lot of tools out there that you could use. Figma, Trello, Slack. There's a lot of inducing communication. WhatsApp actually is huge in a lot of parts of the world for communicating with businesses too. So for sure use the tool that makes more sense for the community that you're trying to get to.

(42:25) – We have amazingly smart people oriented towards the common good that are putting a lot of effort into finding solutions. So that is some positive news. We don't want to downplay how complicated the situation is. It is an opportunity for all of us to to create things that are important for society, things that are good for society. And we are shifting a lot of resources to solve this problem.

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Saleema Vellani is an award-winning serial entrepreneur, keynote speaker, a professor, and the author of Innovation Starts With “I”. At the age of 21, Saleema co-founded and launched Brazil’s largest and #1 language school to finance an orphanage and social development programs, which has taught several thousands of students to date. Shortly after, she co-founded and ran a leading online translation agency in Italy to help companies expand their digital presence globally, while generating hundreds of jobs in the gig economy. 

The business was acquired in 2012. For over 12 years, Saleema has led 100+ international organizations, nonprofits, and Fortune 500 companies to their next stage of growth and innovation. As an intrapreneur, Saleema has been co-leading award-winning, groundbreaking research with the World Bank on solving food insecurity in conflict-affected countries through climate-smart technologies since 2016. Given her experience with running businesses online, in 2013, Saleema led startup education programs for Upwork (formerly Elance) to train Washington DC-based business owners on how to hire and manage remote teams.

Currently, Saleema is the Founder and CEO of Ripple Impact, which helps entrepreneurs increase their influence and impact through accelerating the growth of their platforms and businesses. She also teaches Design Thinking and Entrepreneurship at Johns Hopkins University and is a frequent guest lecturer at business schools.

Episode Links:  

Saleema Vellani’s LinkedIn: https://www.linkedin.com/in/saleemavellani/ 

Saleema Vellani’s Twitter: @Innovazing

Saleema Vellani’s Website: https://saleemavellani.com/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

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– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:53) –  I was embracing a lot of the principles and the actual design thinking process. It is about iterations and cycles, but it was really about understanding a problem and that's something that we talk about customer development, understanding who are potential customers and what's the problem we need to solve. We realized we we needed to really carve our own niche and focus on what was working 

(05:16) – The skill of being able to think like a designer doesn't mean everyone needs to becomes a design thinking expert or an innovation expert, but just the skill of being able to connect dots that seem unrelated and that's also referred to as associative thinking, there's different theories around this, but really trying to connect things I that already exist in new ways, that ability to think that way is one of the skills that's going to be really important for the future of work.

(05:51) – We're in the middle of this re-skilling revolution right now as stated by the world economic forum. Embedding that in the culture of an organization is becoming increasingly important.

(06:35) – Innovation starts with I, and the mindset and developing that innovative way of thinking and being able to only share creativity and really just knowing yourself, know your sweet spot, what do you do? Or what can you offer to the world? Understanding who are the stakeholders that you need to really understand when you're solving a problem and then making your impact on the world through that and so that with more and more with technical fields.

(07:03) – Showing empathy. Understanding yourself and who you are, and that ability to make things more humane. Understanding humans really starts by understanding yourself.

(09:17) – Thinking about what data is available, but the design thinking mindset can be applied and data scientists, being able to question that and using design thinking principles, whether it's starting from empathy to really framing the problem and that's one of the hardest parts of design thinking is being able to frame the problem correctly, because oftentimes we're thinking about the solutions without really understanding the problem.

(11:26) – We're entering the fourth industrial revolution as we talked about we're in this re-skilling revolution and a lot of businesses are stalling and they're falling behind, or sometimes it's hard to even see that you're stalling when you're so focused inside of the business and not on the business to develop that awareness until it's too late and you've been replaced or you've been automated.

(18:15) – Resilience is really important for everyone to have and when it comes to innovation, entrepreneurship, design thinking. The time where you're hitting a dip, you're going rock bottom and you're not sure whether you should go, you should keep working at it, what to do and it's almost like crisis mode and that's happened to me. Resilience is important because you have to be okay with failure and more and more companies are trying to adopt this culture where failure is and it starts by having a psychologically safe environment.

(22:48) – The Coronavirus has actually enabled us to be more human and really understand what's going on in the world and developing that global awareness, which is another insight that I got through my book interviews is really understanding what's going on with different cultures.

(26:12) – With design thinking, it's important to understand the experience that humans or your customers go through and on the backend there is lot of the coding, a lot of that's already being automated a lot of things are being replaced,

(28:04) – That ability to think in that way, like a designer, even just enough so that you can humanize the code or humanized data science, that's going to be increasingly important. 

(29:46) – Constant learning, the ability to just constantly be in learning mode and going to conferences, absorbing content. Try to get at least one nugget per day and learn something new and make that part of your routine that's really important to stay up to date with the trends cause it's so easy to just become obsolete in today's economy. 

(32:12) – This rise of entrepreneurship is like everyone wants to be an entrepreneur, a lot of people are trying to participate in the gig economy, being entrepreneurs and even the concept of an entrepreneur has evolved so much. There's Instagram influencers, social entrepreneurs, focusing on the feeling and the impact that's important, as well as figuring out how to collaborate with other people.

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Hefin Rhys is a Senior Scientist (flow cytometry) at UCB. He completed his PhD at the William Harvey Research Institute in Queen Mary University of London in 2017, and graduated from my MPharmacol degree from the University of Bath in 2013. His main academic interests are conventional, imaging and small particle flow cytometry, data science and machine learning. 

Episode Links:  

Hefin Rhys’ LinkedIn: https://www.linkedin.com/in/hefin-rhys/ 

Hefin Rhys’ Twitter:  @HRJ21

Hefin Rhys’ Website: https://www.manning.com/books/machine-learning-with-r-the-tidyverse-and-mlr 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(01:44) – My view is not that of someone who is an expert on this virus, but it's clearly something that's very serious and that we need to take seriously and treat with respect. So as much as the virulence of the virus itself is concerning, I particularly consider how viral misinformation and misinformed practices have gone along with it.

(08:24) – As a pharmacologist, my PhD was in immunology. The traditional analysis methods that we had been using and that other people in biological fields were using started to not quite suit our needs, not quite answer our questions. In biological life sciences the level of maths left them. I started to teach statistics, R and machine learning during my PhD. Manning wanted a book that was not for computer scientists necessarily, but more for people who were an expert in their own area but who could use and benefit from machine learning, who could benefit from understanding and learning machine learning to make predictions and extract meaningful insights from the data that they have.

(14:57) – The answer to the question of whether somebody should learn R or Python is yes, people should use either or both. Python would probably have been a more convenient choice for a lot of people for machine learning. Carat or MLR in R, which were kind of an answer to scikit-learn and create this common interface so that you learn how to use that package and then substituting in a variety of different machine learning techniques and algorithms is extremely simple. Tidyverse is a collection of data science packages, a set of packages that are designed to make common data science tasks extremely easy, clean and reproducible.

(22:21) – There's basically no reason for Python and R to compete, we can incorporate code from both languages.

(24:11) – R has a phenomenal community of people. You need only to tweet a question or ask for opinions, and hashtag our stats and you get a ton of really nice supportive answers back and a huge amount of support on github or stackoverflow. 

(25:41) – Submitting a package to CRAN, the Comprehensive R Archive Network, is not a difficult process at all, if you write your package well. But writing a package for it to be submitted on to CRAN has to meet certain criteria. The documentation has to be of a certain quality in data in a certain way. The script files have to be laid out and documented in a certain way. So the whole CRAN submission process selects for good quality packages. 

(27:30) – People that are asking the really important questions, whether to do with business or science or health or whatever, the people that know how to ask and are asking those important questions are the ones that should be able to harness and implement statistics, data science, and machine learning to get those answers. I don't think that machine learning should be the purview only of mathematicians and computer scientists.

(28:13) – As long as you teach people how to do things properly, that they have enough of an understanding of how the techniques work and what they do and what they don't do, then, absolutely, we can democratize machine learning. We can absolutely teach people to be able to use these techniques, to extract the answers or make the predictions that they're looking for in their field of expertise.

(29:18) – The MLR package, which stands for machine learning in R. It provides a unified interface to a huge number of, not only actual machine learning algorithms, but also processes and functions like missing value, imputation, hyperparameter tuning, validation techniques. Where MLR particularly shines is, It makes it extremely simple to validate your models, MLR works very nicely with parallelization. MLR helps achieve that because you can do some extremely complicated validation pre-processing with very small amounts of code. 

(34:49) – Caret has functions that you can use to split your data into train test validation sets. And it has the ability for you to perform data pre-processing steps like missing value, imputation and things like that. MLR has become more popular recently. Caret has been the mainstay.

(38:15) – Tidy Models are a set of packages that come from the Tidyverse. And in a similar way in which MLR is trying to create a uniform interface to machine learning, Tidy models are packages that are trying to create a unified approach to modeling in general. So that includes, and it's probably more widely used, as linear modeling. 

(41:53) – I really do think that Machine Learning with R, the tidyverse, and mlr is an excellent book. And it sounds very braggy of me and I don't mean to be, because although I wrote the content, a huge number of people other than me have made the book very good. So I do think that people will learn a lot and get a lot from it.

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Christoph Lütge studied business informatics and philosophy in Braunschweig, Paris, Göttingen and Berlin. He was a visiting scholar at the University of Pittsburgh (1997) and research fellow at the University of California, San Diego (1998). After taking his PhD in philosophy in 1999, Lütge held a position as assistant professor at the Chair for Philosophy and Economics of the University of Munich (LMU) from 1999 to 2007, where he also took his habilitation in 2005. He was acting professor at Witten/Herdecke University (2007-2008) and at Braunschweig University of Technology (2008-2010). 

Since 2010, he holds the Peter Löscher Chair in Business Ethics at the Technical University of Munich. In 2019, Lütge was appointed director of the new TUM Institute for Ethics in Artificial Intelligence. He has held visiting positions in Venice (2003), Kyoto (2015), Taipei (2015), at Harvard (2019) and the University of Stockholm (2020). In 2020, he was appointed Distinguished Visiting Professor of the University of Tokyo. His main areas of interest are ethics of AI, ethics of digitization, business ethics, foundations of ethics as well as philosophy of the social sciences and economics. 

His major publications include "Business Ethics: An Economically Informed Perspective" (Oxford University Press, 2021, with Matthias Uhl), "An Introduction to Ethics in Robotics and AI“ (Springer, 2021, with coauthors) and "The Ethics of Competition” (Elgar, 2019; Japanese edition with Keio University Press, 2020).

He has been a member of the Ethics Commission on Automated and Connected Driving of the German Federal Ministry of Transport and Digital Infrastructure (2016-17), as well as of the European AI Ethics initiative AI4People (2018-). He has also done consulting work for the Singapore Economic Development Board and the Canadian Transport Commission.

Episode Links:  

Christoph Lütge’s LinkedIn: https://www.linkedin.com/in/christophluetge/ 

Christoph Lütge’s Twitter: @chluetge 

Christoph Lütge’s Website: https://www.gov.tum.de/en/wirtschaftsethik/start/ 

Podcast Details: 

Podcast website: https://www.humainpodcast.com/ 

Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 

Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 

RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 

YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag 

YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos 

Support and Social Media:  

– Check out the sponsors above, it’s the best way to support this podcast

– Support on Patreon: https://www.patreon.com/humain/creators   

– Twitter:  https://twitter.com/dyakobovitch 

– Instagram: https://www.instagram.com/humainpodcast/ 

– LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  

– Facebook: https://www.facebook.com/HumainPodcast/ 

– HumAIn Website Articles: https://www.humainpodcast.com/blog/ 

Outline: 

Here’s the timestamps for the episode: 

(00:00) – Introduction

(02:25) –  On the Future Forum we developed the idea of forming a kind of global network of centers for AI ethics. And at the end of this forum, we launched a concrete project, the global AI Consortium, which we are now taking forward in order to form a kind of global alliance of centers working in this field.

(04:06) – It's not just an academic thing. It's not just a traditional research Institute where you do research behind closed doors, basically intimate. You have to work with both industries, with civil society and with politics, and that's the only way to take these issues forward. 

(06:54) – More of these systems are more visible to the public, and that's why there's also this discussion about AI and the ethical as well as governance aspects of it.  Certainly the trend is now, and has been already for years, obviously, the machine learning and deep learning aspect of AI, which some of the more conservative countries still refuse to call real AI. So for a long time, the idea has been that there will be something more robot-like systems that are out there in the world and doing certain things. But  this is the major trend. And of course, the implementation into special vehicles, and probably also in the field of health. I would say these are the most important trends for the near future.

(09:33) – AI systems can both speed up a lot of processes, as well as create entirely new ones, or let's say connect data. They will provide a lot of new input for doctors. And so we are, and will be more and more, at a point where we can say, it's not responsible anymore not to use AI.

(12:10) – We have these different levels of autonomous, striving automated, highly automated driving and fully automated driving. So what we are witnessing now is a progression on these levels. We need to get beyond that level where it's actually where the company is liable during the time that the car was in control, but not the driver.

(15:33) – We need to have robust software which must be able to drive on the difficult, maybe not most extreme conditions, that's if we want to drive under any conditions that will be difficult. And of course, that car must be able to deal with, let's say, rain, with hale, with snow, at least light snow, maybe. And that can pose a number of difficulties, also different ones around the globe.

(17:05) – We presented our first guidelines for ethics of AI in late 2018 in the European parliament. And we came up with these five ethical principles for AI. So, which are beneficence-maleficence, justice-autonomy. And while these four are quite standard for ethics, the fifth one is quite interesting: the explainability criteria. Then we presented another paper on AI governance issues just recently last November, this was about how companies and States can interact on deriving rules and governance rules for these systems.

(20:48) – There are a few people who have the expertise in ethics actually. I'm one of the few ones in there and it will be quite interesting to see how this process works out, because, ultimately, we will need to develop international standards for these AVs.

(23:03) – Ethics is quite a fuzzy term. It has lots of connotations and, for some people, it's about personal morality and that's not really what we mean. We are aiming at standards or guidelines, rules which are not always legal ones, which might be so. So we found it also better to use the term responsible AI. Not just the typical research academic conference, but one where we plan to interact with other stakeholders from industry, from civil society, from politics as well.

(24:37) – We invite the abstracts on many areas of AI and ethics in a general sense to visit our webpage to find a lot of potential topics, whether it will be AI in the healthcare sector, AI and the STGs, AI policy, AI and diversity and education, and many others.

(25:47) – Engineering curriculum should be enriched with elements from humanities and social sciences, not least of which it will be ethics. But now with a focus on AI, it becomes clearer that working on AI will not be enough to just look at it from a purely technical point of view. It needs to generate the necessary trust. Otherwise people would just not use these systems. And this is something that engineers should be familiar with, engineers and computer scientists, and people from technology.

(28:23) – One of the key challenges will be how we manage to some extent, standardize explainability. Every step within the system must be transparent and it must be clear, you must be able to track it down. Of course, there's no way to do that, if you are familiar with the technology. So we need to find some kind of middle way. And there is this research field of explainable AI in computer science, and the challenge will be to implement systems.