117 episodes

David Yakobovitch explores AI for consumers through fireside conversations with industry thought leaders on HumAIn. From Chief Data Scientists and AI Advisors, to Leaders who advance AI for All, the HumAIn Podcast is the channel to release new AI products, to learn about industry trends, and to bridge the gap between humans and machines in the Fourth Industrial Revolution.

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

    • Technology
    • 4.9 • 41 Ratings

David Yakobovitch explores AI for consumers through fireside conversations with industry thought leaders on HumAIn. From Chief Data Scientists and AI Advisors, to Leaders who advance AI for All, the HumAIn Podcast is the channel to release new AI products, to learn about industry trends, and to bridge the gap between humans and machines in the Fourth Industrial Revolution.

    Steven Shwartz: How AI Will Impact Society Over the Next Ten Years

    Steven Shwartz: How AI Will Impact Society Over the Next Ten Years

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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

    • 34 min
    Gianluca Mauro: How To Educate Future Managers To The AI Era

    Gianluca Mauro: How To Educate Future Managers To The AI Era

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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

    • 34 min
    Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence

    Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence

    Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence  
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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 
    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,

    • 27 min
    Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything

    Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything

    Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything  
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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. 
    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 re

    • 33 min
    Thor Ernstsson: How To Use Data Science for Stronger Relationships

    Thor Ernstsson: How To Use Data Science for Stronger Relationships

    Thor Ernstsson: How To Use Data Science for Stronger Relationships  
    [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 
    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 o

    • 34 min
    Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems

    Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems

    Stephen Miller: How To Leverage Mobile Phones And 3D Data To Build Robust Computer Vision Systems
    [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. 
    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 recog

    • 34 min

Customer Reviews

4.9 out of 5
41 Ratings

41 Ratings

LisaIsHereForIt ,

Incredible insights on the future of AI 💥

HumAIn has quickly become a favorite in my feed! I'm consistently impressed by the engaging conversations, insightful approach, and pioneering guests. Amazing work, David -- I truly learn something every time I listen!

Clarisse Gomez ,

Awesome Podcast!!!

David, host of the HumAIn Podcast, highlight all aspects of artificial intelligence, data science, developer tools, and technical educatio‪n‬and more in this can’t miss podcast! The host and expert guests offer insightful advice and information that is helpful to anyone that listens!

Jason Strand ,

Solid Data AI Thought Leadership

I work in technology education and always pick up an actionable insight when I listen to the HumAIn podcast. Great guests in an interview format expertly curated by David Yakobovitch. Highly recommend.

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