HumAIn Podcast

Beyond Spreadsheets: How Ambient AI is Reshaping Financial Planning with Runway’s CEO Siqi Chen 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Secure RAG Systems: A DeepTech Exploration with Protecto’s COO, Protik Mukhopadhyay 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Scaling AI For Enterprise: Inflection AI’s Roadmap to Human-Centered Solutions with CEO Sean White 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

AI Strategy Unveiled: Former IBM Chief AI Officer on Enterprise AI Success 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Beyond ChatGPT: Unlocking Enterprise Value in the Age of Generative AI 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Ethical AI in Action: How Plainsight is Transforming Business Intelligence 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

The Next Frontier in AI: Multi-Agent Frameworks and the Path to AGI 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Javier Luraschi: AI as Your Co-pilot: Hal9's CEO on Reshaping Enterprise Workflows 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: 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/ Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Max Cho is the CEO and co-founder of Coverage Cat, a startup revolutionizing the insurance industry through data-driven solutions. With a diverse background in technology and finance, Max has held key positions at industry giants including Google, Two Sigma, and Microsoft. His expertise spans software reliability, quantitative analysis, and consumer-focused product development. Driven by personal experiences with insurance complexities, Max founded Coverage Cat to simplify the insurance buying process and empower consumers with transparent, optimized insurance options. His unique blend of technical knowledge and entrepreneurial spirit positions him at the forefront of innovation in the InsurTech sector. 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-insurancehttps://www.coveragecat.com/carrier-comparison Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

The Data Dilemma: How Blind Insight is Revolutionizing Secure Analytics for Enterprises ft. Jackie Peters and Nick Sullivan 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 Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Patrick Obeid: How AI Simplifies ESG Reporting and Data Infrastructure w/ ESG Flo [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/ Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Max Galka: How AI Transforms Decision-making on the Blockchain [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/ Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Steven Banerjee: How Machine Intelligence, NLP and AI is changing Health Care [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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...

[Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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 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 (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...

[Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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...

Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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 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: (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...

Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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. Please support this podcast by checking out our sponsors: Episode Links: 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 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: (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...

Thor Ernstsson: How To Use Data Science for Stronger Relationships [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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 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) – 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...

Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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. Please support this podcast by checking out our sponsors: Episode Links: Stephen Miller’s LinkedIn: https://www.linkedin.com/in/sdavidmiller/ Stephen Miller’s Twitter: https://twitter.com/sdavidmiller Stephen Miller’s Website: http://sdavidmiller.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 (01:42) – 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...

Nell Watson: How To Teach AI Human Values [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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...