Episodios

  • The Data Dilemma: How Blind Insight is Revolutionizing Secure Analytics for Enterprises ft. Jackie Peters and Nick Sullivan

    The Data Dilemma: How Blind Insight is Revolutionizing Secure Analytics for Enterprises ft. Jackie Peters and Nick Sullivan
    Jul 14 2024

    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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    27 m
  • Patrick Obeid: How AI Simplifies ESG Reporting and Data Infrastructure w/ ESG Flo

    Patrick Obeid: How AI Simplifies ESG Reporting and Data Infrastructure w/ ESG Flo
    Jul 1 2024

    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/



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    39 m
  • Max Galka: How AI Transforms Decision-making on the Blockchain

    Max Galka: How AI Transforms Decision-making on the Blockchain
    Feb 23 2024

    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/ 



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    31 m
  • Steven Banerjee: How Machine Intelligence, NLP and AI is changing Health Care

    Steven Banerjee: How Machine Intelligence, NLP and AI is changing Health Care
    Sep 21 2022
    Steven Banerjee: How Machine Intelligence, NLP and AI is changing Health Care  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSteven Banerjee is the CEO of NExTNet Inc. NExTNet is a Silicon Valley based technology startup pioneering natural language based Explainable AI platform to accelerate drug discovery and development. Steven is also the founder of Mekonos, a Silicon Valley based biotechnology company backed by world-class Institutional investors (pre-Series B) — pioneering proprietary cell and gene-engineering platforms to advance personalized medicine. He also advises Lumen Energy, a company that uses a radically simplified approach to deploy commercial solar. Lumen Energy makes it easy for building owners to get clean energy.  Please support this podcast by checking out our sponsors:Episode Links:  Steven Banerjee LinkedIn: https://www.linkedin.com/in/steven-banerjee/ Steven Banerjee Website: https://www.nextnetinc.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (05:20)- So I am a mechanical engineer by training. And I started my graduate research in semiconductor technologies with applications in biotech almost more than a decade ago, in the early 2010s. I was a Doctoral Fellow at IBM labs here in San Jose, California. And then I also ended up writing some successful federal grants with a gene sequencing pioneer at Stanford, and Ron Davis, before I went, ended up going to UC Berkeley for grad school research, and then I became a visiting researcher.  (09:28)- An average cost of bringing a drug to market is around $2.6 billion. It takes around 10 to 15 years, like from the earliest days of discovery, to launching into the market. And unfortunately, more than 96% of all drug R&D actually fails . This is a really bad social model. This creates this enormous burden on our society and our healthcare spending as well. One of the reasons I started NextNet was when I was running Mekonos, I kept on seeing a lot of our customers had this tremendous pain point of, where you go, there's all this demand and subject matter experts, as scientists, they're actually working with very little of the available biomedical evidence out there. And a lot of the times that actually leads to false discoveries. (13:40)- And so there are tools, they're all this plethora of bioinformatics tools and software and databases out there that are plagued with program bugs. They mostly lack documentation or have very complicated documentation and best, very technical UI’s. And for an average scientist or an average person in this industry, you really need to have a fairly deep grasp or a sophisticated understanding of database schemas and SQL querying and statistical modeling and coding and data science.  (22:36)- So, a transformer is potentially one of the greatest breakthroughs that has happened in NLP recently. It's basically a neural net architecture that was incorporated into NLP models by Google Brain researchers that came along in 2017 and 2018. And before transformers, your state of the art models and NLP basically were like, LSTM, like long term memories are the widely used architecture. (27:24)- So Sapiens is, our goal here is to really make biomedical data accessible and useful for scientific inquiry, using this platform, so that, your average person and industry, let's say a wet lab or dry lab scientist, or a VP of R&D or CSO, or let's say a director of research can ask and answer complex biological questions. And a better frame hypothesis to understand is very complex, multifactorial diseases. And a lot of the insights that Sapiens is extracting from all this, with publicly available data sources are proprietary to the company. And then you can map and upload your own internal data, and begin to really contextualize all that information, by uploading onto the Sapiens.  (31:34)- We are definitely looking for early adopters. This includes biotech companies, pharma, academic research labs, that would like ...
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    31 m
  • Steven Shwartz: How AI Will Impact Society Over the Next Ten Years

    Steven Shwartz: How AI Will Impact Society Over the Next Ten Years
    Jun 12 2022
    [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSteve 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 it's fascinating. It's being used in almost every area of business, of government, of the nonprofit world. It is fascinating how much application there is.  (27:06)- And they're not really going to make sense if you drill down into them. So what's going to be the implication of that. Is it only going to be useful if there's all kinds of search engine optimization where you don't really care If what you're right makes sense. We're going to generate a lot of crap using GPT three and put it out there for search engine optimization purposes.(31:19)- And I think there's a lot of opportunity for companies that are helping develop software and services to help companies build non-biased explainable systems. And then you have a whole issue around when you build a machine learning system, it deteriorates over time. So it might only work for a couple of days and then start to go downhill. It might work for weeks, but you have to monitor those systems and go back and ...
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    34 m
  • Gianluca Mauro: How To Educate Future Managers To The AI Era

    Gianluca Mauro: How To Educate Future Managers To The AI Era
    May 22 2022
    [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSGianluca Mauro is the CEO of AI Academy, which he founded with the mission of helping people understand what artificial intelligence is and its place in their organizations and their career. Gianluca is the author of the book "Zero to AI - A nontechnical, hype-free guide to prospering in AI era" Over the years, Gianluca and his team have done both technical consulting and training workshops, working with companies like P&G, Merck, Brunello Cucinelli, Daikin, Fater, Bayer, and EIT Innoenergy Gianluca teaches Artificial Intelligence to people without a tech background, without any code or math. Why? Because he believes, the future of artificial intelligence is in the hands of people who can find use cases in their organizations, and then define and run AI projects. Please support this podcast by checking out our sponsors:Episode Links:  Gianluca Mauro LinkedIn: https://www.linkedin.com/in/gianlucamauro/ Gianluca Mauro Twitter: https://twitter.com/gianlucahmd Gianluca Mauro Website: https://ai-academy.com Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (04:15)-Sometimes it's not a concept that people are familiar with. It sounds weird to anybody who works in tech. But, a lot of companies, in these industries, are still struggling with the cloud. So, when you go to these companies and start talking about this technology, they are excited. They're like, this sounds amazing, but you have to keep into account the reality of where they are, they're not in a place where they can invest in hiring a full-blown data science team, because then nobody knows how to interact with them. (09:29)- So, having the right governance for how to use the data, how to keep it in the right shape, and making sure that the quality is what we need, and then actually bring into the laptops of the data scientists that they can make tests and run experiments and make graphs. So, I always like to say it doesn't really matter how good your technology is. How good is your data warehouse or whatever kind of stock you use if using that data is not easy. If using that data it's not straightforward for a data scientist. (17:32)- And in the same way, if we want to use AI for marketing, you need to give tools to the marketers that understand the problem to use AI on their data for their problems. When I talk about sales, well, I understand sales data set and takes me a lot of time to understand the logics of sales, have a sales team of the data that its Sales team works with to a sales team who really understands this data, the right tools to, they don't have to be able to do everything but the list to get started, well, then they know much better than me the data.  (18:17)- So, it's kind of a paradox, because the most important thing of the app is the recommender system. But the reason why that works is not because of the tech, but because of how the UX feeds the tech. And if you think about this, think about this concept, well, then your UX designers, they need to understand this, they need to understand what it means to feed an algorithm with the right data.  (23:40)- And so we have seen cases where these things went wrong. And I may start from the stuff that everybody knows about, the elections in 2016, fake news and all this stuff up until more niche, let's say topics that maybe not a lot of people aren't aware of. But that actually had a strong impact on people. An example is AI in hiring. There was a very interesting research made by MIT Technology Review about how a lot of companies that sell software for hiring and leverage AI are actually biased. (31:01)- And it has been amazing, honestly, because then you'll have people coming from all sorts of backgrounds. I give them the tools and the foundational knowledge that they need to talk about these topics in a way that is productive and they bring the wrong perspectives. They bring their own experience. And I had to say, I've been amazed ...
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    35 m
  • Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence

    Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence
    Apr 3 2022
    Ben Zweig: How Data Science and Labor Economics Connects to Workforce Intelligence  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBen 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 workforce of a company. But what's cool about this is that when an employee is stored in an HR database, that information is mirrored in the public domain. (21:22)- So, we really have to create a taxonomy that updates that changes with an evolving occupational landscape and the changing economy. We also really need to infer the activities that people do, because those are the building blocks of a job, or the job is a bundle of activities. So, we really need to understand that when one person says lawyer and another person says, attorney, those are probably the same occupation, but when one person says Product Manager in Facebook versus a Product Manager at JPMorgan, those might be totally different occupations. (30:21)- So, what are the HR tech companies that are really dominating, and then it gets even specific, who's dominating the self-driving car market, how benefits help retention of women in the workforce, that's something that we've seen some changes in the ...
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    27 m
  • Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything

    Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything
    Feb 19 2022
    Edo Liberty: How Vector Data Is Changing The Way We Recommend Everything  [Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSEdo 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 just launched a version 2.0 that contains all sorts of filtering capabilities and cost reduction measures and you name it.    (21:22)- And so I'm a great believer in knowing your own data and knowing your own customers and training your own models. It doesn't mean that you have to train them from scratch. It doesn't mean you don't have to use the right tools. You don't have to reinvent the wheel, but I'm not a big believer in completely pre-trained, plucked off of a random place in the internet models. I do want to say that there are great models for just feature engineering for objects that don't change so much. So we have language models like BERT that transform text and create great embeddings and they're a good starting point. (31:01)- So I think you'll see two things. First of all, with Pinecone specifically, we're focused on really only two things; making it easy to use and get value out of Pinecone and making it cheaper. That's it! I mean that, those ...
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    33 m