HumAIn Podcast - Artificial Intelligence, Data Science, Developer Tools, and Technical Education David Yakobovitch

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


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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  
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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 resea

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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 
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

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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