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

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...

Ryan McDonald: How To Position People at the Center of AI Native Solutions [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Ryan McDonald is the Chief Scientist at ASAPP working on NLP and ML research focusing on CX and enterprise. He is also an Associate researcher in the NLP group at Athens University of Economics and Business. Ryan was a Research Scientist in the Language Team at Google for 15 years where he helped build state-of-the-art NLP and ML technologies and pushed them to production. He managed research and production teams in New York and London that were responsible for a number of innovations used in Translate, Assistant, Cloud and Search. He was the first NLP research scientist in both New York and London, and helped grow those groups into world-class research organizations. Prior to that, he did his Ph.D. in NLP at the University of Pennsylvania. Episode Links: Ryan McDonald’s LinkedIn: https://www.linkedin.com/in/ryanmcd/ Ryan McDonald’s Twitter: https://twitter.com/asapp Ryan McDonald’s Website: http://www.ryanmcd.com CX: The Human Factor Report: https://ai.asapp.com/LP-2021-09-CX-The-Human-Factor_Landing-Page.html Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (3:00)- The kinds of problems that deploying AI runs into for enterprise is more about scalability. Instead of having a single user of the technology, we have hundreds of users of the technology and how can we deliver a unique experience and an excellent experience for each of those users and this necessitates questions around adopting machine learning and natural language processing models to new domains. (10:49)- And this is exactly the technology we're building out. How can we sort of regularize that? How can we look at the conversation and the issue that the customer's happening? That's sort of embodied in the dialogue, up to a point in time and then allow AI to make recommendations to the agent; Here is a workflow that we think you should use and all the steps you need to follow in order to solve this issue (28:33)- So we design everything and that's why it's critical to design these things from the bottom up with AI in mind. All of our artificial intelligence has been designed to serve those latency needs. So to kind of give you a couple of examples, the first is automatic speech recognition. So a huge number of calls that come into call centers are still voice, they're not digital. It's not people call contacting over chat. It's people calling in on their phone. (30:41)- So we've focused on building out something called SRU, which is an architecture where we can take super high, accurate AI models and then distill them into these faster architectures, which allows us to get into these millisecond range. So we can get responses back to agents and milliseconds, and that really is going to affect how much they use those suggestions at the end of the day. (32:38)- Beyond what's happening in the conversation and see everything, all the information and all the actions...

Humphrey Chen: How AI Can Revolutionize the Way We Consume Video [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Humphrey Chen is the CEO and Co-Founder of CLIPr. He has a BS in Management Science from MIT. His work in tech specializes in the use of technology to make people and companies more productive. Please support this podcast by checking out our sponsors: Episode Links: Humphrey Chen’s LinkedIn: https://www.linkedin.com/in/humphreychen/ Humphrey Chen’s Twitter: https://twitter.com/humphreyc?s=20 Humphrey Chen’s Website: https://aws.amazon.com/es/rekognition/?blog-cards.sort-by=item.additionalFields.createdDate&blog-cards.sort-order=desc Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (01:36) – CLIPr operating premise is that not all minutes of video content are equally relevant to everyone. So it uses machine learning to fully index that video and make it fully searchable. (05:02) – Watching a whole video can be inefficient when a participant only wants to watch specific sections. CLIPr team's speeds up and accelerates more efficient automations to be helpful for both consumers and enterprises. (06:42) – The tools that CLIPr provides are a way to guarantee target audience engagement rates to be really informative. CLIPr focuses on this video insight when it comes to engagement and interaction around the video itself in a category called video analysis and management. (08:04) – CLIPr aims to hand out the tools to efficiently find content that matters, bookmark it, share it, react to it, comment on it. (08:27) – The tools and the skills required to edit a video are completely opposite from the skills and tools required for editing inside of a document. CLIPr bridges the two effectively, by building a video-based document type. (11:57) – There has not been as much disruption around video. Some use cases that have been thought out include recording customer meetings; customers’ feedback, integrations with a CRM record, and also, provide a score over time around the actual probability of closing a sale based on the relative perception for the customer reaction. (14:20) – AI, additionally with the hospitals and the medical universities and researchers alike are still using antiquated technology and they're not extracting insights from these video moments. CLIPr is also useful in telemedicine. For surgeons, CLIPr means high value, highly visual, high-impact in a short time. (24:26) – Machine learning, in general, it's all about the data and about engagement and interaction and training new models around the data. So, machine learning allows people to create things and bring solutions. Technology is actually going to find meaningful problems to solve more effectively and more efficiently. (28:21) – The purpose of services is to build businesses and to augment either with the stable technology or the experimental technology for what will be the future of AI, of natural language processing of emotion, detection of...

Dave Bechberger: How Connected Data Impacts Our Daily Interactions [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Dave Berchberger is a Senior Graph Architect at Amazon Web Services (AWS). He is known for his expertise in distributed data architecture being a thought leader in graph databases, and the co-author of Graph Databases in Action by Manning Publications. Dave uses his 20+ yrs experience working on and managing teams delivering full-stack software solutions to take a holistic approach to solve complex data problems. Episode Links: Dave Bechberger’s LinkedIn: https://www.linkedin.com/in/davebechberger/ Dave Bechberger’s Twitter: https://twitter.com/bechbd?s=20 Dave Bechberger’s Website: https://www.manning.com/books/graph-databases-in-action?a_aid=bechberger Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (01:29) – Corporate environments need to be able to help solve certain types of problems that traditional relational databases or other data technologies are not very good at solving. The new approach is to build out high-performance data platforms on top of a mix of technologies, focused around solving them with graphic, graph database technologies. (02:53) – Graphs are the mathematical construct of a graph. It's really about networks, connected data of different people connected to other people or things of that nature. It's about building out networks and using those connections to be able to answer specific types of questions and draw insight and information out of that data that isn't necessarily available from other technologies. (06:49) – Fraud is another canonical use case, because it is all about figuring out connections and patterns within data, to be able to discern whether this activity is fraudulent or not. (08:32) – Other technologies don't do a great job linking together entities in such a way that those links and those connections are also treated as first-class citizens inside that data. Graphs bring those connections in your data up to being “first-class citizens”. (09:29) – With a graph, those connections are brought up and given first class status in the languages and queries that you run. It's called traversing them, to be able to move across them, to be able to drive insight from how those connections are made and how those connections basically connect this network of data together. (12:38) – Using Graphs makes developers able to not only process data in a real-time transactional mode, but being able to use those along with something like graph type analytics, and then use that in conjunction with AI and ML technologies to augment data back into your graph in order to provide a better real-time user experience. (14:32) – Any enterprise build or any consumer service build is really about creating a better, faster and easier to use experience for your customers. Those are really the driving forces behind any kind of business initiative. Graphs is one of those technologies. (16:38) –...

Alex Beard: How to Solve for the Global Education Crisis caused by The Pandemic [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Alex Beard is the Senior Director at Teach For All , and author of the book Natural Born Learners. After starting out as an English teacher in a London comprehensive, He completed an MA at the Institute of Education before joining Teach For All. His book, “Natural Born Learners”, is a user's guide to transforming learning in the twenty-first century, taking readers on a global tour into the future of education, from Silicon Valley to Seoul, Helsinki to Hounslow. Episode Links: Alex Beard’s LinkedIn: https://www.linkedin.com/in/alex-beard-08901915/ Alex Beard’s Twitter: https://twitter.com/alexfbeard?s=20 Alex Beard’s Website: https://www.alexbeard.org/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (01:43) –The methods used to teach would probably be familiar to Socrates two and a half thousand years ago in ancient Greece. Few things have been done differently inside the classroom. The gap between what is possible, and what was currently true in the classroom is at the heart of our education crisis. (03:03) – The pandemic has widened the educational divide. The pandemic has exacerbated the crisis and intensified some of these questions about the future of education. (06:30) – Education must consider access and quality. But with schools shut down, access becomes an infrastructure through the internet and that's a relatively technical solution. (07:38) – If you're not going to school, quality of education is knowledge received sitting in your bedroom via your laptop, which has completely disrupted our idea of what a quality education is. (08:19) – The vast majority of primary and middle school kids are just not equipped with self motivation yet, so quality has to mean something about human to human engagement. Learning, for most people, is better when it's social. (13:40) – Practitioners have had to develop new pedagogies, new ways of learning, how to engage kids through the medium of technology. You need to know how to engage a student. (15:16) – We might be strengthening bonds between teachers and parents, as a result of the pandemic to support early learning, virtually, and that involves engaging parents more actively in supporting their kids to learn. (18:48) – Our intelligence is unlimited, and it's teachers in schools that cultivate that potential. We need to be more explicit about the different roles that teachers play, and set up our system to enable teachers as subject specialists who help kids to do better. (21:12) – Teachers need to be experts in tech, at least to understand how they can use the latest tools to outsource bits of their practice to save themselves time. (30:22) – AI is sort of an adversary to help us enhance our own creativity. The dangers are more connected to the intentions. It all comes down to human choices if you deploy technology and in certain ways...

Ivy Lu: How To Organize Data Science Teams and Data Science Projects for Startups [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Ivy Lu is the head of data science and machine learning at Oxygen. Ivy's onboarding marked the launch of Oxygen’s banking platform. She has bachelor's degree in Geographical Information System from Peking University, a Ph.D in Earth Systems and Geoinformation Science and a Master's degree in Geographic Information Science and Cartography both from George Mason University. Episode Links: Ivy Lu’s LinkedIn: https://www.linkedin.com/in/ivy9lu/ Ivy Lu’s Twitter: https://twitter.com/oxygenbanking Ivy Lu’s Website: https://www.blog.oxygen.us/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (01:42) – I joined Capital One as a data scientist after my graduation from George Mason University with a PhD in Geographic Information Science. After I moved to the west coast, I joined Apple. So, at Apple, I work on an anti-fraud team where we fight against all kinds of fraud and abuse within the whole Apple ecosystem to bring trust and safety to the Apple customers. Both experiments helped me prepare for my new challenge at Oxygen as a FinTech company. So, that's my career , how I passed from the traditional banking industry to a large technology company. And now I'm at the spin hat company Oxygen. (04:05) – A collaboration challenge, since you are the only one and only data scientist on the team, basically, you are collaborating with so many different teams and departments: from operations to marketing customer support or product features. So, you need to collaborate with every single one in the different departments and understand their needs, understand their pain. That also comes related to the first challenge. Collaboration comes with prioritization. (06:57) – Data science teams should be positioned as the foundation and the cross team within the whole organization. So for each line of the business, data scientists should have domain knowledge about the problem that they are trying to deal with (09:20) – I collaborate with our fraud team to set up a lot of protections in the core sets. We collaborate with different fraud vendors on how to set up all the parameters, all the controls in place in the fraud vendors for our sign up status. After the sign up flow is pretty under control, I built a preliminary machine learning model for the fraudsters, to detect fraudsters after sign up for the behaviors they show with our card. (14:48) – I see these days, as data scientists it may require different skills than before. Nowadays, maybe, coding skills are not required anymore with such a good tool for data scientists and for machine learning engineers. But, ultimately, I still think the important thing is the study section background on the machine learning algorithm, the deep understanding of the machine learning algorithms. Also what's important is the deep understanding of the problem they're solving. (17:41) –...

Asra Nadeem: How the future of media will be enhanced by generative design [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Asra Nadeem is the Co-Founder of Opus AI, a streaming platform powered by proprietary tech that turns plain text into movies and playable 3D worlds in real-time. She is the first female Pakistani venture capitalist. She has a BA in Economics, and has a Masters in Film/TV/Theater and English Literature from Beaconhouse National University. Please support this podcast by checking out our sponsors: Episode Links: Asra Nadeem’s LinkedIn: https://www.linkedin.com/in/bretgreenstein/ Asra Nadeem’s Twitter: https://twitter.com/AsraNadeem?s=20 Asra Nadeem’s Website: https://opus.ai/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (01:55) –Nadeem’s background and her thesis: There is not any kind of freedom without financial freedom, and technology is a great enabler for that. (07:13) – Through a platform that grants access to some of the most brilliant minds in the world for free, anyone can learn and interact now. (09:02) – ”Naseeb” revolutionized the traditional marriage arrangements in Pakistan, by allowing younger generations to create connections online and get married. (11:26) – Formal education has mainly three purposes: learning something, networking and better job opportunities. Those three things are available through technology. (13:54) – The Big Names in the tech industry don't request a college degree to work for them, only the skills. It's a different world that is crafting narratives and stories, building stories for the creative industry, and this is a space that's a massive opportunity that has not been tapped into yet. (14:59) – Opus.ai, an engine that takes any literary text and converts it into a movie. So you have a code without having to know how to code. It can be that tool to enable digital natives who may not have any coding experience in order to democratize content creation. (23:29) – The technological progress or the leaps and bounds of automation make generative design come of age. Using AI to boost creativity makes anything possible and accessible. (26:01) – New types of film will be generated and created. And creativity generates, potentially, new jobs. There is no match for human creativity. And this inherent desire to explore new places or explore new worlds, that's something that's very uniquely human and not replicable by a machine. (32:14) – Network effects are built into platforms, who want to get users in front of as many people, because that's how they drive ad revenues or eyeballs. Figure out trends that your product market fit, and then that platform creator fit that's working for you. (38:14) – The current conditions are opportunities to reinvent, to try new technology and to show that you as a human, can be part of a new wave. We're continuing to move forward into a world that could be without code, could be no code, low code. Build your creative...

[Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Steven Shillingford is President and CEO of DeepSee.ai, a Knowledge Process Automation (KPA) platform to mine unstructured data, operationalize AI-powered insights, and automate results into real-time action for the enterprise. He is the creator of the Knowledge Process Automation industry category, delivering AI-powered automation and productivity via easy to deploy, cloud-based business flows for critical business operations in the Capital Markets and Insurance verticals. He has led several startup enterprises, building cloud-scale platforms and helped found a successful cybersecurity platform for big data analytics supporting network surveillance systems for a range of verticals, from intelligence agencies to Fortune 500 companies. Please support this podcast by checking out our sponsors: Episode Links: Steven Shillingford’s LinkedIn: linkedin.com/in/steve-shillingford Steven Shillingford’s Website: https://deepsee.ai/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (02:31) – Innovation Cycles used to be about features, but now consumers and enterprises look for innovation around processes (06:53) – Using AI to surface the information that is most useful through a configurable tool bias towards action. (13:52) – NLP to support different tools for different types of business problems inside the enterprise (16:29) – A hybrid approach where people need interaction to lead us to “enhanced accelerated productivity” (22:26) – Reducing processing time to offload a non-human optimized work to the machine, keeping Computers working on behalf of the humans (23:42) – Operationalize data science and the innovation that comes from AI around outcomes to achieve knowledge, reduce cost, mitigate risk and improve customer satisfaction, not only in capital markets or insurance, but across a number of industries (26:53) – A platform that matches unstructured data in different business models, but same processes. Automation of checkpoints by a machine using the Deepsee platform as in capital markets (30:27) – Helping research get faster results. Streamlining paper processes to innovate in new therapeutics, new vaccines, medical supplements and medications, as well as the technology used for blockchain (33:50) – More than document digitization it’s document and data analysis, preserving data provenance across all actions to build trust through transparency and achieve wide-scale adoption. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Oliver Schabenberger: How Data, Analytics, Decisions and Intelligence Are Connected [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Oliver Schabenberger is the Chief Innovation Officer at SingleStore. He is a former academician and seasoned technology executive with more than 25 years of global experience in data management, advanced analytics, and AI. Oliver formerly served as COO and CTO of SAS, where he led the design, development, and go-to market effort of massively scalable analytic tools and solutions and helped organizations become more data-driven. Previously, Oliver led the Analytic Server R&D Division at SAS, with responsibilities for multi-threaded and distributed analytic server architecture, event stream processing, cognitive analytics, deep learning, and artificial intelligence. He has contributed thousands of lines of code to cutting-edge projects at SAS, including, SAS Cloud Analytic Services, the engine behind SAS Viya, the next-generation SAS architecture for the open, unified, simple, and powerful cloud. He has a PHD from Virginia Polytechnic Institute and State University Please support this podcast by checking out our sponsors: Episode Links: Oliver Schabenberger’s LinkedIn: https://www.linkedin.com/in/oschabenberger/ Oliver Schabenberger’s Twitter: https://twitter.com/oschabenberger?s=20 Oliver Schabenberger’s Website: https://www.singlestore.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction (01:38) – From forestry to statistics to Software development to advance analytics (04:07) – To understand the data is not only to build a mental model, but a probabilistic model of how the data came about, and once that model is accepted, as a good abstraction, then it is used to ask questions about the world. (05:39) – Many of the assumptions into our established models and established thinking about industries and supply chains had to be questioned because of unforeseen events like the pandemic. Scenario modeling is not just making a prediction, it must also guide the decisions and the need to provide the right abstractions. (07:19) – There is an approach steeped in mathematical statistics and probability theory. And a more computationally-driven approach which shows how computer science, as a discipline, changed its focus from focus on compute, to focus on data. (10:34) – There are transactional systems, analytics systems, machine learning and data science, all somewhat based on existing technology purpose-built for a certain use case, and what we're seeing is the use cases coming together. These worlds need to come together through a data foundation where the workloads can all converge. Silos and empires that need to be connected. (16:15) – The explosion of neural network technology over the last 15 years due to the availability of big compute and cloud computing has allowed to solve much deeper problems, and we need larger amounts of data to train those models. (16:33) – Modern AI, data-driven AI...

[Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Brad Schneider is the Founder and CEO of NoMad Data. He was previously the CEO of Adaptive Management. Throughout his career, Brad has focused on using alternative data to improve decision making and prediction. Brad has been a Portfolio Manager at Tiger Management, and Managing Director at Jericho Capital, a $2bn AUM TMT-focused hedge fund. Prior to Jericho, Brad also worked at Palo Alto Investors as an equity analyst and was a co-founder and head of product development for InfoLenz, a predictive analytics company. Brad holds a Bachelor of Science degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and is a CFA charterholder. Please support this podcast by checking out our sponsors: -Work Patterns: https://www.workpatterns.com -Imagine Golf: https://www.imaginegolf.com -Art of Manliness: https://www.artofmanliness.com/podcast/ -Keep Optimising: https://keepoptimising.com Episode Links: Brad Schneider’s LinkedIn: https://www.linkedin.com/in/bradschneider/ Brad Schneider’s Twitter: https://twitter.com/bschneider222?s=20 Brad Schneider’s Website: https://www.nomad-data.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 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 (01:16) –A tech guy who started in the analytics space and moved to the world of investment, which led him back to the field of data (02:33) – Building software over the years helped him, as the user of data, to more easily interact with that data and find ways to connect the use case to the dataset. (03:57) – NoMad Data's goal is at a high level to be the search engine for these datasets, making it a lot easier for people in the AI space, for researchers, for computer science, for marketers, for strategy professionals, consultants, investors, help them connect those everyday business problems that they have to real datasets. (05:33) – Data that is more frequently purchased include credit transaction data and customs data, which allows to see trade flows (06:48) – Data sets are so powerful, but they're also so broad.Customs data set help to understand a single company on the aspect of one company or region and economic competitive wins and losses for factories. And because they're so broad it's very hard to describe on a webpage what this dataset can be used for. (08:07) – The build vs. buy dilemma: it really depends on your timeline and the availability of the data you need. Even if the data we collected was a hundred percent accurate, it would become very challenging, because we wouldn’t have enough data points to even make a simple linear regression model. So, in a lot of cases, it's better to buy. (10:25) – Getting that data from where it started, whoever is creating it or whoever you're purchasing it from, and getting it somewhere that you can write that first query has historically been a bottleneck. Some services like Snowflake are creating these marketplaces where people are putting the data in...

Ashutosh Garg: How To Leverage AI To Recognize And Improve Diversity In Hiring [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Ashutosh Garg works with startups across the enterprise stack. He is particularly excited about how machine learning and deep learning are reinventing existing software categories and creating new consumer experiences. Ashutosh has invested in AI-enabled business applications (such as marketing technology and HR technology), data platforms, data center infrastructure, security & privacy, as well as online video. Before joining Foundation Capital in 2008, Ashutosh was the general manager for Microsoft’s online-advertising business and led field marketing for the software businesses. Previously, Ashutosh worked at McKinsey & Company, helping technology companies scale their go-to-market efforts. Earlier in his career, Ashutosh founded TringTring.com, one of the first search engines in Asia, set up Unilever’s Nepal operations, and led the marketing and pre-sales teams at Cadence Design Systems. Ashutosh has a bachelor’s degree from the Indian Institute of Technology (IIT) in New Delhi and an MBA from the Indian Institute of Management at Bangalore, where he received the President’s Gold Medal. Episode Links: Ashutosh Garg’s LinkedIn: https://www.linkedin.com/in/ashugargvc/ Ashutosh Garg’s Twitter: https://twitter.com/ashugarg?s=20 Ashutosh Garg’s Website: https://foundationcapital.com/member/ashu-garg/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 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 (01:31) –Eightfold.ai was created in 2016 as a talent intelligence platform that is being used by the leading enterprises across the globe to hire, engage, and retain a diverse workforce. (04:21) – Large enterprises’ number one challenge is people. They are not able to hire fast enough. Enterprises should think about diversity, about their own biases, to understand what talent exists. We added exits to bring the right people on board and that is where data and AI comes into play. (05:43) – We can't keep looking for people who have done the work. We have to look at the people who can do the work, and that is a fundamental shift in the mindset. (09:00) – We need to reach out to the people who may not have had all the privileges that we have and support them. We have to look at people beyond what we perceive for their face color, age. (10:14) – Machines have the ability to forget and ignore. We have our biases because of the lack of knowledge. Knowledge and moving out of biases can really help us solve this problem when hiring candidates. (11:59) – There has to be an audit process to ensure that your algorithms are not going crazy and that they are doing the right thing. Let's use them to help humans do a better job. (13:53) – It's all about humans. These systems are designed to come in and replace humans. In that case, not only are you taking the snitch system correctly, you're teasing that: I really don't need to worry about humans, and that has to...

#148- Robert Stevenson: Why The Future Hospitality Guest Experience is Mobile [Audio] Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS Robert Stevenson is the Chief Executive Officer at INTELITY. He is a business and technology executive with 20 years’ of rich experience across a wide array of disciplines. Robert specializes in the productization, strategy and market delivery of new technologies. In addition to undergraduate studies in Design and Computer Science, Robert holds an MBA from the Schulich School of Business at York University and the Kellogg School of Management at Northwestern University, including work at the Hong Kong University of Science & Technology. Episode Links: Robert Stevenson’s LinkedIn: linkedin.com/in/robertstevenson Robert Stevenson’s Twitter: https://twitter.com/intelity?lang=en Robert Stevenson’s Website: https://intelity.com/ Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS 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. (01:45) – Hospitality Tech has been reluctant to embrace the latest and greatest technologies. (03:28) – INTELITY is a mobile platform being built to modernize the guest experience. (05:36) – INTELITY customer segment and customer ecosystem and market is that 80% who are not major hotel brands. (08:09) – INTELITY has been conceived as a B2B2C. (12:41) – How the pandemic stroke Hospitality industry but leveraged a long-expected change. (13:53) – Mobile experience and automation to improve the market. (14:53) – Using AI and data to drive revenue. (18:31) – Using AI and data to predict customers behavior and offer a better service. (19:59) –Automate the experience to elevate the guest and improve the travel P&L for the hospitality space. (21:17) – The voice space in hospitality has been slow to customize and adapt these tools. (23:54) – Mobile technology has led the way, but major changes will emerge in mobile computing devices. (27:56) – The power of the devices will continue to get stronger, better and more demanded. (28:38) – The trend will be to see new hotel apps rolling out to promote contactless experiences because of COVID. (29:55) – The hospitality industry needs AI and Machine Learning to adapt to customer needs. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

How Platforms Leverage The Extended AI Community To Address Misinformation with Claire Leibowicz Shownotes Links https://www.linkedin.com/in/claire-leibowicz-17156a65/ https://twitter.com/CLeibowicz https://www.partnershiponai.org/manipulated-media-detection-requires-more-than-tools-community-insights-on-whats-needed/ https://medium.com/partnership-on-ai/a-field-guide-to-making-ai-art-responsibly-f7f4a5066ee https://arxiv.org/abs/2011.12758 https://medium.com/swlh/it-matters-how-platforms-label-manipulated-media-here-are-12-principles-designers-should-follow-438b76546078 About HumAIn Podcast The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy