[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAs the Head of AI, Labor, and the Economy, Katya Klinova directs the strategy and execution of the AI, Labor, and the Economy Research Programs at the Partnership on AI, focusing on studying the mechanisms for steering AI progress towards greater equality of opportunity and improving the working conditions along the AI supply chain. In this role, she oversees multiple programs including the AI and Shared Prosperity Initiative.Katya holds an MPA in International Development from Harvard University (USA), a B.Sc. cum laude in Applied Mathematics and Computer Science from Rostov State University (Russia), and a Joint M.Sc. in Networks and Data Science from University of Reading (UK), Aristotle University of Thessaloniki (Greece), and Universidad Carlos III de Madrid (Spain), where she was a Mundus Scholar.B is a technology and facilitation expert who is passionate about creating social change through empowering everyone to participate in technological and social governance. B is a Congressional Innovation Fellow serving in the US Senate advising policy makers on technology policy.B received a Bachelor of Science in Economics from the University of Texas at Dallas, and was selected as an MIT-Harvard Assembly Fellow for the 2019 Ethics and Governance in Artificial Intelligence Initiative cohort.Episode Links:  Katya Klinova’s LinkedIn: https://www.linkedin.com/in/katyaklinova/ B. Cavello’s LinkedIn: https://www.linkedin.com/in/bcavello/ Katya Klinova’s Twitter: @klinovakatyaB. Cavello’s Twitter: @b_cavelloKatya Klinova’s Website: https://www.partnershiponai.org/ B. Cavello’s Website: https://bcavello.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(01:55) – AI and technological change have been contributing to the polarization of labor market skill bias. What we saw as the pandemic is that people with college degrees, people who have the opportunity to work remotely have been hit economically much less comparatively with people who are not able to work remotely. And that's disproportionately people who did not have access to higher education and college degrees.(04:52) –  We see a lot of formal sector jobs  falling away as a result of precautions taken to manage the virus. But as a result of this, we also see a proliferation in oftentimes lower wage on-demand or gig work playing out. There are many, several silver linings to take from this trend that we're seeing playing out, but there are also a lot of highly disruptive technologies in the space of robotics and information technology, especially in the AI space, that could lead to possible exciting futures, but they could also lead to some less ideal outcomes.(08:14) –  Some people might have found out that they're just as productive working from home, and they save time commuting. So some companies might have discovered that they're saving a lot of money on office space. So they might choose, even if it's not because of healthcare considerations, they might choose to stay remote. And that might become more of a norm.(10:41) – We see a whole new level of disparity across the board. The office, the workplace is in some ways a leveler, in that everyone has access to the same coffee machine, the same conference room, the same equipment, but as more of our work is distributed, that might not be the case.(13:19) –  I also want to shine a spotlight on the role that we human beings are playing in the process of facilitating the development of these technologies. And while we recognize that, we're building incredibly fabulously capable machines, really continuing to interrogate to what end and to whose benefit those are being built. Taking a more active stance in the future of work debate, and being more deliberate about choosing the direction of technological change when it comes to AI and other technologies as well is what is missing.(18:41) – We need to be realistic about our ability to quickly enough upskill everyone globally to keep pace with the technological advancement and think about how do we lower the barrier to entry, lower the barrier that's needed in terms of skill requirements for people to be able to use these technologies to their economic advantage and extract economic value from that and be able to use it for their earning opportunities. I'm genuinely curious to what extent certain jobs that are considered as low skilled or high-skilled, which we recognize as the flawed language of economics, where we're really what we're referring to is educational attainment and how much pre-training someone has.(28:24) – The benchmark that we hold our technology against is not these questions of what would make a worker's job easier or their output better. But rather this question of, is it going to be able to perform at the level of a human? Can we make a technology that will make a person,  that will then be able to do whatever a person can do? And there's this sort of fetishization in the AI sphere. And it comes from a really beautiful, fascinating space. The scifi nerd in me does really wonder, Oh man, what would it be like to create other ways of thought, what would it be like to develop these thinking machines.(31:04) – We have something like 8 billion humans, those humans now more than ever are in need of gainful jobs. And if we think of technological progress as the type of technological change that helps society prosper and overcome its economic condition, the last thing that we need to do is to be building machines that do what humans can already do better than them. And creates competition for those humans.(40:25) –  I work in the AI space because  that can be a thing that does bring about incredible opportunity and prosperity and new horizons of understanding and collaboration that we haven't even seen before. And that's really exciting to me. So, I wanted to just clarify that this stance is not one that says we shouldn't have AI. We shouldn't go down this road. We shouldn't build these technologies, but rather, that this technology isn't moving on its own, it's moving because our hands are doing the work, at least for the time being(44:01) – There's a lot of talk in conversations about structural issues and structural change. At the end of the day, these structures are built by us as people, the humans in the AI loop, and we have the power to shift it. And we also have the power to do things that we couldn't do before.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSFelipe Longé is the CTO of Play Magnus and the CEO of Solve Oslo. His mission is to empower talented people in creating empathic user experiences utilizing product development strategies and startup methodologies.He’s always been absorbing competency from all disciplines during his 10+ years of experience with software and product development. He believes that the larger picture can only be understood and engineered if there's a deep empathy for the end-user. The user-centric focus combined with an understanding of technological opportunities, lead to better decisions and sustainable strategies for digital businesses.Episode Links:  Felipe Longe’s LinkedIn: https://www.linkedin.com/in/flonge/ Felipe Longe’s Twitter:   @felongeFelipe Longe’s Website: https://welcome.ai/  Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(04:01) –  Magnus Carlsen himself and his father wanted to enter the digital space and do something with this brand building. We built apps because they are a good way to spread your brand, to create awareness and also to spread joy. (09:54) – We were very thorough with it, making sure that the app feels personal. When you open the app, it feels like you're going to actually play Magnus on it. It's a totally different experience from displaying random chess against AI.(11:41) –  It's actually AI against AI at some points, because they try to memorize as much as possible within the branches that they play. Maybe it's a merger between machine, man and machine. We'll see a huge revolution in how sports is executed based on what machines can learn about it. And not only humans. (21:37) – When you repeat something a lot, you are creating shortcuts, you're wiring your brain to do that specific task perfectly. You do this over and over, and it just becomes embedded in your software, so to say.(24:59) – Freedom for most people gives responsibility, which is good. This will be the way to work moving forward. Not only because of COVID, it would be because it's more effective and it gives this type of freedom. (28:21) – We do both consulting and product development in house. One of our cool engagements has been to work with a camera that can scan your eye and recognize patterns on diabetes to people with diabetes II, and therefore, find out if you're becoming blind. (30:19) – New phones will come out. Wearables will be a huge thing moving forward. At some point we'll figure out how to come closer to the connection between a scan and psychological States of the mind.(32:53) – We're wearing the technology that we previously had in a living room, and in our offices. Most people will have smartwatches and bluetooth devices all over their body. And of course the smartphone, but that will shrink, or at least become thinner and thinner up to a point where it's maybe a bracelet or something and you can bend. Nanotechnology will at some point become cheap to use and the manipulation of genes, it's all there, but there are so many sciences just expanding exponentially.(36:14) – We need to become more connected on these things, especially on med tech. At some point it would just become global,  pure global collaborations.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJared Goldberg is the Head of Data Science at WeatherOptics. He owns a Bachelor of Science in Biopsychology, Cognition, and Neuroscience; Applied Statistics from University of Michigan.Episode Links:  Jared Goldberg’s LinkedIn: https://www.linkedin.com/in/jared-goldberg-427462103 Jared Goldberg’s Twitter: @weatheropticsJared Goldberg’s Website:https://www.weatheroptics.co/ https://github.com/jaredbgo 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) – WeatherOptics started as a weather blog way back when, from our founder and CEO, Scott Pecoriello, he was a weather nut growing up. And he had this blog, it was on Facebook and social media. And his snowfall accuracies were crazy good. In November of 2017, he reached out to me to bring the business into this tech side of things and into the data side of things. And we have just been gaining momentum since then.(03:15) –  Quantitative forecasting was started in the 1920s by the Norwegians. The modern era of forecasting started in the 1980s and that's where we had global forecasting models based on a more complex system of observations, but still building off these physics concepts that were used originally. Since the 1980s things have just gotten more complex. These models have gotten better. And now there's this whole wild system that no one really realizes is happening where you have all of these different inputs from all these weather gauges, like airplanes and satellites, and they're all amalgamated and interpolated into these models.(09:52) –  To some extent, everyone has this inherent understanding that the weather changes our behavior. However, we need to keep in mind that our business and where we really understand the weather better is the short term weather events. It is these sorts of impacts that obviously are not as flashy as something like a hurricane or a tornado, but we feel understanding how weather impacts daily life at these smaller scales and these less major events actually can save people and companies a lot of money and can really improve their processes.(14:11) – While some industries have been excluded or cut off, and obviously a lot of people are losing jobs, there are other industries that we are leaning on much heavier. And one of these industries is logistics. And one major application of our weather data is building useful ways to understand, not just that it is going to rain or I guess in this case, it is not just going to snow in. It's how is that going to affect your route? We are aware that weather has an impact on sales. So we consider weather data, a viable source of alternative data in terms of quantitative investing and things like that. We think these weather signals can help explain variations in other datasets that help us understand the market. (17:46) –We have a combination of meteorological expertise, as well as machine learning. We have been very thorough to truly understand how the raw weather data paired with these non weather variables, add up to these actual impacts and we feel by delivering impacts as opposed to raw weather data, we are going to allow businesses to make impactful decisions, that way they do not have to wrestle with the data itself.(23:39) – We expect that these self-driving cars will need to have an even better safeguard against these road conditions that could be disruptive to normal driving. It is those sorts of interactions between variables that we feel our impact indices would allow people to have the upper hand to understand that just because it is raining does not mean that the roads are not going to be dangerous. And perhaps these cars, these very smart and intelligent cars should know the level of danger and how prepared they need to be in order to uphold the safety of the people using them.(27:37) – Power outages can be in terms of how weather affects humans on a day-to-day level. California outages would be the perfect use case where if the emergency management companies and government groups that were preparing for these things, if they had a really accurate forecast of what was going to happen in the future, based on the weather, then they could have had a better response.(32:32) – The whole idea of our company is these impact indices and all of our forecasts allow these companies to have the heads up to say, we think something disruptive is going to happen. So you should change your behavior in order to mitigate loss.And  once a company has identified that they would like the heads up about this bad weather, and they would like to understand how weather is going to impact their day to day operations, the whole idea is we want to deliver that information in a format that makes the most sense.(34:45) – Our insight portal is for more of the non-technical audience. And this is for individuals who perhaps are managing a certain geographic area.The insight portal is our attempt at the most user-friendly nontechnical delivery of these same insights. Our most technical offerings you could argue are our APIs, which are delivering the raw weather data itself, such that we give you those impacts very granularly. And then your data science team would get a chance to play around with it and use it in the way that is best for them we are building this middle ground to deliver things like Excel templates that have this weather data aggregated up. (39:16) – We cannot blame individual events, but we do know that these large term changes can be attributed or are more evident that things are happening.So it's important to know that as the climate changes and as these big term big level changes happen, it's going to result in these small level things that are going to start affecting our lives. That's why it is just going to become increasingly important to know when those individual bad weather events are going to happen in order to prepare for these bad things and mitigate loss as we've discussed, but also we need to keep track of them. (42:48) – In some ways, the weather can pop up relatively randomly and be quite disruptive across industries.Moving forward is getting these crop indices up, testing their accuracy and deploying them across our product suite. (45:28) – This could even feed into that fire in terms of technology and improving and people realizing how important prediction is going to be. Maybe it'll just make people more excited about technology. I certainly hope so. (47:40) – If people can use weather as a framework for technology and artificial intelligence as a whole, it will allow people to understand how powerful prediction is.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSam Horodezky is the Founder of Strathearn Design. He has been dedicated to user experience (UX) for more than 20 years. During that time, he founded a company specializing in this field called Strathearn Design. With more than 15 years of management experience, he has worked with and overseen multiple teams of designers and developers, and created a wide variety of unique, focused strategies for companies that needed to improve their UX strategy.At Strathearn Design, clients are pushed to think beyond the aesthetics of their UX. Their main goal is to educate and enlighten clients about their entire business and product suite. They put their expertise to practical use, advising clients about the skills their teams possess and the quality of their product. They can also manage and repair their entire UX from the ground up, studying every detail of their business and their market.Episode Links:  Sam Horodezky’s LinkedIn: https://www.linkedin.com/in/sam-horodezky-3b19552/ Sam Horodezky’s Twitter:   @StrDesignSam Horodezky’s Website: https://www.strathearn-design.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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) –  Some of the tools that are becoming available now are specifically meant to democratize design or bring design to the masses. Wix has this thing called the ADI (Artificial Design Intelligence), and it helps create a website that is very straightforward.(04.27) –  Either machine learning or AI have to be able to generate as much as possible. We're quite there yet when it comes to design. Not as far as I can tell, but that is definitely the idea, to reduce the amount of work required of the human.(05:18) – Microsoft does do a lot of artificial intelligence, ML type stuff, whether they're actually using that or not, you can never really tell. All they have to do is put in a graphic and then some texts that can have different groups of texts and different pieces of graphics and it'll give you lots of options. I'm sure they're developing all sorts of interesting techniques to make design so that non-designers essentially can get good results.(07:32) – Logo Joy was centered around logos. It's now called Looka and this one will generate a bespoke logo. You only pay once you decide you want a high resolution image. It's not the same quality as if you were really to hire yourself a designer and get a bespoke logo. But at the same time for 50 bucks, this is giving you a lot.(10:49) –  What really is AI and what is not? What is definitely true is that you're able to take a photo or a video and then transform it into something that looks totally different than it actually can be, quite professional. It's another example of increasing the ability to have tools for users that aren't really designers.(14:04) – There's a lot of interesting tools out there, but they seem like they're more kind of experiments than they are things that are genuinely going to change how we do work. Photoshop has a tool called Content Aware Crop. If you try to rotate something or change the dimensions, it fills in the background for you. Netflix has one thing related to user interface, a simple snapshot that shows you the video that you are actually about to watch or the movie. Firedrop.io is able to process videos and use large amounts of data to basically output advertisements.(19:37) – The de facto tool that everyone was using 10-15 years ago, was called OmniGraffle. Sketch is being displaced right now, but Sketch again was the de facto tool for UI and UX designers for a long time.It allowed you to do pixel level manipulation. Figma allows you to have a collaborative experience. Adobe used to have a tool called Fireworks and they adopted it to call it IXD. They're essentially SAS solutions.(24:22) – Those tools are just going to become increasingly joined with Slack but I'm not necessarily predicting that it will specifically have Slack integrations.(26:02) – Sketch didn't go to the cloud fast enough and they allowed other entrants to the market beat them to it.(27:22) – There's an entire industry now that's building tools and what they do is they provide analytics that are input to product managers or to user experience designers. Some of these tools will eventually begin to pull all their data together and put AI on top of it and actually be able to suggest user interfaces based on all the data that it's been looking at. (32:53) – There are absolutely low code options for people who either don't know anything about coding. But we're still really far away from the day where we don't need developers because an AI will be doing it.(36:22) – For people who all they're doing is taking one thing and moving it to another set of colors or a different font, or basically doing some of that unpleasant work, that's going to be mechanized within 10 years. Those people need to up-level their skills, so that they're doing something more complex that a computer can't do today and may not be able to do for some time.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSChris Bishop has a degree in German Literature from Bennington College. He started music after getting out of school.He ended up in the jingle business, writing music for television. Then he became intrigued by the web and taught himself to be a web producer and worked at a couple of seminal interactive agencies in New York. He was hired by IBM into their fledgling corporate internet programs division.He is a TEDx speaker, ex-IBMer, former NYC studio cat, future workplace consultant, and a firm believer in the power of focusing on the fringe. Based on his own nonlinear, multimodal career path  he’s developed a workshop called “How to succeed at jobs that don’t exist yet” designed to excite and empower today's learners as they navigate the global borderless workplace.His session provides insight into how to deliver business results and pursue successful careers leveraging emerging technologies including quantum information science, AI, data science, fintech, cryptoassets, blockchain, augmented/virtual reality, genomic editing, and robotics.Episode Links:  Chris Bishop’s LinkedIn: https://www.linkedin.com/in/christopherbishop123/ Chris Bishop’s Twitter: @chrisbishopChris Bishop’s Website: https://improvisingcareers.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(04:38) – The U.S Bureau of Labor and Statistics says today's learners will have 8-10 jobs by the time they're 38. They're going to use technology that doesn't exist today. I connected with a gentleman from LinkedIn Learning and he said, I think your content would be valuable to the LinkedIn Learning audience and here we are.(06:18) – People can work from home or from wherever on the train or in a Starbucks and be more productive, because they're more in control of their time. Data science is going to have lots of opportunities to take these learnings, as you said about education.  The opportunity again, for data science to rethink how information is shared and distributed represents a huge opportunity. (08:36) – The idea is that humans have been creating devices to make work simpler and faster and easier for literally thousands of years. There's lots of history and precedents for the kinds of tools that led to humans manipulating data, that is what we do today with algorithms and using artificial intelligence and machine learning. So it is part of a long arc that goes back thousands of years and is going to continue for thousands of years.(11:38) – An interesting example to share is the New York Stock Exchange. That space is basically a catering hall now, because there are algorithms that are doing most of the trading. There are certainly people in there doing work, but back to your comment about math, algorithms can make assessments and recommendations, buy and sell way faster than a human can. So that's the model, it is like, let's use tools that will help us move faster, work better, work more efficiently and improve productivity.(12:24) –We are also seeing AI being used to help radiologists examine X-rays.  A lot of data science is being put into the unfortunately scrubbed mission today, but hopefully we'll see the SpaceX launch. That's going to open up incredible opportunities for data scientists, not just around NASA and ancillary businesses. (14:41) – Everything is generating data now and the idea is that data is empowering. It can also be disabling. And there are certainly conversations about privacy and confidentiality. At the end of the day, the ability to capture data and represent it accurately is a good thing. Using tools like AI and machine learning, we can take that data and make sense out of it and rationalize it, not only to live more comfortably, but also to drive innovative business models.(16:17) –  Interesting new careers, jobs and certainly in data science are emerging at the intersection of unlikely or historically disconnected disciplines. So by that, an example I cite is Nanopharmacy. So they're now creating ingestible bots that can carry Pharmacology at the atomic or molecular level, to the affected area, to the tumor or to the wound or to the area where the medicine is needed. All that kind of science that's going on now in these crazy times is going to be expanded. it is  going to set models and precedents for how medicine is created and delivered, how healthcare and biomedicine is created going forward(19:05) – My toolkit is me reflecting on how I navigated these careers and trying to codify them into these future career tools. I call them voice antennas and mesh. Technology is a source of information about future tech and culture. So that's the antenna piece. And then the third piece is mesh, which I like to describe as a three-dimensional data visualization of your network. (23:17) – First of all, get into a disciplinary vertical that you're interested in, a topic area that you're passionate about because then you'll be successful if you're interested in it and then find ways to step back and provide more strategic higher level business perspective, and respect the fact that you are knowledgeable, more than you think about how say a business is run and some it is not for everybody. I would encourage data scientists again, as this is such a rapidly evolving and morphing field to think about how to move up into say a management role or a strategy role, to not be afraid to contribute ideas about solutions for innovative products and services that a company might take on to drive their business model. (26:11) – There's lots of sources of information and the bad news is there's lots of really good sources of information. So, managing, parsing and doing triage on the tsunami of info is the challenge. The implication is that these are topic areas you're interested in. The broader implication is, it represents focus areas for a data science career.(28:40) – Learning is key. I heard it stated by some writer recently that we have to stop thinking of education as an event that happened in time. Education is something that goes on your whole life. It never ends, especially in this environment. In the second decade of the 21st century learning is a non-stop process. Just like networking.  it is the old adage described to showbiz, but true in every business. Now it is not what you know, it is who you know, so building your mesh is critical. (33:13) – My general advice, certainly to all careerists, but definitely to data scientists has always been and served me well, is, chase the maelstrom, find the chaos, go for the mayhem. So go where they don't know what it is yet. And then you can be involved, you can have a creative role, you can do something interesting and innovative and be employed gainfully and be remunerated.(34:48) –  I went into this web thing and it served me well. It was an emerging technology that people didn't quite know what to do with it. And people from all different kinds of disciplines and backgrounds were getting into it. So fast forward to 2020, the areas where I've encouraged data scientists to focus on, are things like certainly AR and VR. In the education space and in the medical space and then even in financial services, I would encourage them to investigate crypto assets, blockchain, and bitcoin. These are all going to be big opportunities, certainly 3D printing. Biotech, certainly education, almost everything you can think of is being transformed by technology and the implications are on data science. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSMerav Yuravlivker is the Co-founder and CEO of Data Society, which builds and delivers tailored data science academies to Fortune 500 companies, government agencies, and international organizations. From assessing your current staff capacity to implementing data-driven culture, they can unleash the workforce’s potential to solve your organization’s toughest problems and prepare for the future.Episode Links:  Merav Yuravlivker’s LinkedIn: https://www.linkedin.com/in/meravyuravlivker/ Merav Yuravlivker’s Twitter: @Merav_YuravMerav Yuravlivker’s Website: https://datasociety.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(01:34) – Data Society is a data science training and consulting firm. And we work with government agencies as well as large organizations and corporate clients to help them understand their data, to solve problems. So whether that is through customizing training programs, to their use cases, to train up their workforce, to understand data, or whether that is building customized software and algorithms to help them make predictions about trends that they are seeing, we are there to provide solutions(03:01) – What has been truly amazing is just the way that our team has handled the transition from more in-person training to more live streaming. Since we switched to live streaming, we have a lot of students from South America who are joining us now, and it has been really wonderful to see that additional impact that has had and the different points of view that they are bringing to the table.(05:11) – This is really going to shift the way that people think about education. can we really provide support for each other at a time when people are still trying to work out what support they want. now we are chunking it into smaller portions over longer periods of time to make sure that we are maximizing that learning and that retention.(06:39) – Data is the only way that we are going to get through this successfully and make sure that we prevent it in the future. So it is really important for us to understand that data that we are collecting about this pandemic is truly for the benefit of the entire population. While there is a lot of politics that seems to be involved in this pandemic, it is important to understand that data is apolitical and it is important to use it in order to inform our decisions.(11:01) – There is a lot of that misconception going around. And in fact, we did a study last year of data scientists and asked them what their biggest pain points were in their workforce, and what we found is that they had a lot of difficulty communicating insights to their managers and to their staff outside of their data science teams, because there is not a common data vocabulary. (11:44) – Another misconception that a lot of people have is that data science is magic. You push a button and all of a sudden, you know exactly what is going on, and I am sure you could also speak to how much time data collection and data cleaning actually takes. Usually it is 80% of any data project and a lot of the data scientists that we surveyed said that there was a lot of frustration on the part of their bosses because they do not understand exactly how time-consuming it is to collect that amount of data and then to collect it accurately and make sure that it is clean and ready for processing.(14:11) –  There are some very valid concerns that have come up, people do not want to be tracked by a company without getting certain assurances about how their data will be used. (16:22) –  What if we could connect with data inventories from grocery stores and then build an app to be able to share that information with shoppers so that they can check the supplies before they go. And that way they will only have to make one trip because the other concern is that the more trips you make outside, the more exposure you have to COVID. So our aim is to reduce that, so you only have to go out one time to get the essential products that you need. And what we found out very quickly is that groceries had their hands full already. And a lot of them do not have up-to-date inventory APIs, for example, that we could tap into. So we ended up partnering with another local Washington D.C company called OurStreets, and they have built an app called OurStreets Supplies, which helps people find out what is in stock at a grocery store near them. (20:54) – Furloughed workers are workers that are still technically employed by companies, but are not receiving paychecks. And what is really unique in this situation is previously when employees were furloughed, they were not eligible for unemployment insurance, but because many companies are anticipating this to be a short crunch as opposed to a long lasting effect, they do not want to lose some of their employees by letting them go too soon. (22:11) – My company is working on helping prepare those individuals to re-enter the workforce with very highly prized data analytics skills. Bring that industry knowledge that they already have and have taken years to learn and then pair it with that data analytics skill set to create something completely new and help them become more agile in this environment.(28:45) – Even though the levels of productivity might be the same, there are a lot of intangibles that are very hard to measure that encourage innovation and collaboration that really only occurs in an office space. There is going to be a big shift towards data literacy. And what I mean by that is an understanding of how to ask the right questions of data, understanding what the terminology means, what the potential means and feeling comfortable to manipulate data, visualize data to a certain extent. We are going to see some little robots that are running around on sidewalks, delivering our pizzas inside and stuff like that. So I think we are going to see that type of shift and we are going to see a lot more jobs in that type of automated, like automated behavior.  (37:36) – It is becoming more imperative now more than ever for companies to make that shift to become more data informed. If you are not starting to plan for this data economy that we are in, it will be like competing in a race when you are in a rowboat and your competitors are in motorboats. You will get there eventually, maybe, but you are probably going to spring a lot of leaks and you are definitely not going to be ahead of that pack. A lot of that has to do with the ability for an organization to be agile and to empower its workforce, to think independently, to ask the right questions and to be able to solve challenges effectively.(43:01) – Take an inventory of where you are currently. So assess what data tools do you have? How is your data stored? How is it stored securely? And then thinking through your workforce; Who are your powerhouses? Who are your people that really are leveraging data and how well is it understood in terms of data governance and data policies? Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSMatthew O’Kane leads Cognizant’s AI & Analytics practice across Europe.  His team helps clients modernize their data and transform their business using AI. Matthew brings close to two decades of experience in data and analytics, gained across the financial service industry and consulting. Prior to joining Cognizant, he led analytics practices at Accenture, EY and Detica. Over this period, he has delivered multiple large-scale AI/machine learning implementations, helped clients transition analytics and data to the cloud and collaborated with MIT on new prescriptive machine learning algorithms.  Matthew is passionate about the potential for AI and analytics to transform clients’ businesses across functional areas and the customer experience.Episode Links:  Matt O’Kane’s LinkedIn: https://www.linkedin.com/in/matthewokane/ Matt O’Kane’s Twitter: @MatthewOkaneMatt O’Kane’s Website: http://www.infosecurity-magazine.com/view/13065/comment-connecting-the-dots-on-insider-fraud/ 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(01:14) – I finished a math and stats degree. Got interested in statistics, joined banking and realized there was tons of data I could play around with and apply predictive models to. But almost 20 years ago, I never realized how important AI in Analytics will become as it is today. I joined Cognizant a year and a half ago to really drive what the next level is around analytics and AI, how clients are really scaling AI across the companies and it's a big engineering effort now. Hence why we've got a big team of people who do all the things you need to get started on around AI.(04:03) –  I still go back to the underlying machine learning algorithms that have been around for a long time. Some of the models have gotten more sophisticated and computing power has come along and cloud computing power has come along too, to help us actually power these more and more.(06:04) –  We're obviously going to enter a large recession. The type of AI and the type of work you can do within the ISP will change dramatically. Things like revenue generating opportunities for AI are going to be less on the priority list for at least the next year, and it's probably going to be more on cost reduction(07:39) – If we say it's moving from revenue generating opportunities to cost optimization opportunities, most organizations are gonna see a big shift towards automation, around AI, and we've seen a lot of clients are working at the moment looking to apply AI in new areas they probably hadn't thought about. Automation and the fact that automation means less jobs in a recession and it takes away human effort, we have to square up for what is going to be the reality of the moment.(09:39) –I don't think privacy is going to go away. It still seems to be top of priority, we're just trying to solve privacy problems by Webex and by remote working and by email rather than face-to-face but it's still a big issue and coming out of this if you're going to apply more data and AI to your business, the privacy aspect goes up and is always going to be top of the agenda.(11:03) – There are still fairly distinct areas where humans are good and certain tasks where machine learning is good at a task, so it's really about taking another look at every process you have and re-imagining it within this new digital AI world. This is certainly a crisis that has created significant demand in some areas and a drop in demand in other areas. That's how it's going to play out going forward so we need to be shifting humans to the right areas.(12:41) – Typically if you send an engineer out to solve a problem they're not the expert; there's only about five experts in the entire company. But by taking some of the knowledge from those five experts and turn them into some models you can infuse the insight and the knowledge from the five SMEs into the day-to-day work that the engineers are doing and they can use augmented reality to actually see something. (14:39) –  It allows a human to essentially take what's in their brain and turn it into a model, it allows your experts in the organization, your best claims handler, your best salesperson, your best engineers to take what they have and their understanding and turn them into a set of rules. This is called data programming and these rules can then be turned into a neural network model. AI is very good at processing all the massive data, but it doesn't have the intuition that's held inside of an expert's hat.(17:38) – It turns around to the ethical AI Space as well as the fact that if the research you're doing and what you're developing isn't open and people can't go in to get help and look at it and look at your code and understand how it works. What my team does is take the complex research and a client problem and try to fit the two together and that's usually the hardest thing to do, getting something that impacts clients business.(19:20) – It's not just about algorithms and code. We have to convince the executives in our company to change their business or some new deep learning could do to the actual outcomes.(20:31) – The UK government has been doing a lot of research on AI, they've used that to develop a set of ethical AI pieces, a good set of standards. Now we're working with the UK government infusing ethical AI into every single machine learning model or project that they run. (23:36) – From the data scientist all the way through to the product engineer if the business where we're actually applying the AI is making different decisions, that responsibility has gone all the way through the organization. (25:33) – Data is always biased if you look at that data without realizing COVID etc was happening. There's always something behind data and there's something generating that data.(27:35) – A lot of execs in companies, people that are budget holders can control where AI is used and how they can accelerate and improve business results.(28:39) – A lot of companies have worked out how to operate remotely, and that's a very good time to open up about ideas, about how you could be scaling AI in the organization, how you can really get going and change things so now is the time to have that conversation.(29:47) – It’s important getting the right data platform before you can do AI.  A lot of clients that are going back and saying we need to solve our data, modernize our data, create the right governance model around it usually move on to the cloud. That's what most clients are doing, enabling it and then really scaling AI.(31:37) – They've really got to reduce costs, reduced errors, all these things that are dragging their business down, if we can really help in that area we can really speed up growth in the local companies.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSNikita Shamgunov co-founded MemSQL and has served as CTO since inception. Prior to co-founding the company, Nikita worked on core infrastructure systems at Facebook. He served as a senior database engineer at Microsoft SQL Server for more than half a decade. Nikita holds a bachelor’s, master’s and doctorate in computer science, has been awarded several patents and was a world medalist in ACM programming contests.Episode Links:  Nikita Shamgunov's LinkedIn: https://www.linkedin.com/in/nikitashamgunov/ Nikita Shamgunov's Twitter: @NikitaShamgunovNikita Shamgunov'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(01:37) – People who have the levers of power are rolling out initiatives, rolling out shutdowns and thinking about these big disruptive changes. Andrew Cuomos's updates, always starts his update with a lot of statistics, demonstrating and showing how those statistics are influencing the decisions of what we’re going to go about next.The issue is how can we use data and how can we use location-based data? Because everybody's now carrying a smartphone to really identify and control the epidemic.(03:13) – MemSQL works with a handful of customers to enable social tracing scenarios, estimate the migration patterns that people are having by commuting to work or by going from state to state or taking airplane flights. How can we anticipate where the next outbreak is going to be the most pronounced? Can we really push the numbers down and keep them low? And that requires very good social tracing and contact tracing techniques.(06:27) – The telecommunication operators do have the data, but not necessarily the technology. And that's where MemSQL is partnering with some of the key telecommunication providers here in the United States and overseas, to enable contact tracing and social tracing by combining the data sets the telecommunication providers have, by the nature of their business, and MemSQL technology to store process and give the full 360 information for social tracing for migration patterns, and for various decision supports that eventually flows back into the politicians, the decision decision-makers, to control the spread of the pandemic. (09:08) – There's just so many applications to contact tracing, and COVID certainly highlights. At least one use case there is to control the spread of the pandemic, the effectiveness of that is absolutely unparalleled. This is not going to be the last pandemic. We're going to see more of that and the well developed techniques, technologies that you can just turn on with a flip of the switch will be available and ready for us moving forward. There are certainly plenty of applications for contact tracing, various security applications, terrorists, criminal activities, all of those things. And suddenly it edges at the border of what's that place where we’re giving the authorities too much power that could be the invasion into privacy. (14:36) – It's a part of social responsibility. In my preferred and ideal world, those contact tracing apps are just pushed on you by the device providers, by Apple and Google. And of course it's a consent. So you can reject it or you can accept it. And that would be my preference, but I think it goes into the same category as wearing the mask. Downloading a contact tracing app is a very straightforward thing for you to do, so you basically do it and forget about it. (16:30) – We live in the post COVID world and we'll be working from home quite a bit. We're going to get so good at understanding and controlling this pandemic through a combination of rules and guidelines such as 60 to part, wearing a mask, installing a contact tracing App on your phone. Something that is simple to follow and something that society accepts. And then we're going to get very sophisticated in tools that give us very good insight about what to do and what not to do. And if something is working or something is not working.(19:01) –  There is public data and there's data that is guarded by whoever owns that data. And for public data, we need to have open techniques for securing and anonymizing that data. So you either lock the data down and doesn't give access to anyone. And they are responsible for the security and safety of that data, that the bad guys won’t go and break into it.(23:04) – When you think about data management, a typical solution includes the ability to capture, store and process data. The right place to store and process large volumes of data are in the cloud and the way it works under the hood. You can assemble sophisticated systems. And those would allow you to, like I said, store that data, analyze, process that data, transform these data and build applications. That fundamentally delivers you beautiful user experience, they give you interesting insights or they crunch data under the hood and they present you with some sort of decision support for whatever you want to do with that data. They generate insights. MemSQL is that modern data management solution or a database that lets you store an unlimited amount of data and lets you build applications that are data-centric. (27:22) – There's a bit of a race right now in the markets to become the number one hybrid cloud provider and all the public clouds participate in the rate and the race. We're decisively hybrid, and you can consume MemSQL using Helios, which is our managed service by going onto our portal, clicking on the Helios button, and then a few clicks later, you're able to consume our data management technology in the cloud, but we are also offering Helios Hybrid Cloud, which is in a way, do it yourself cloud.(31:24) – The right choice for your solution really depends on the scenario. Think about what technology gives you today and what technology is going to give you tomorrow in the short, medium and long-term. Understand what you need to solve for today, but also really think about what you need to solve for tomorrow and marry that with where the technology is moving towards in general, and use that as a guiding star from making the choices for data management or really anything else.(33:52) – A lot could be accomplished through technology. And in order to do that, in order to deliver that value, you need technology and you need people. Then you need people who know how to use that technology.  There's plenty of work for information workers, for talented individuals, for data scientists and smart politician with call for help to the frontline medical workers, but also call for help to the information workers. (36:59) – We're late to the party. What happened in California and specifically in San Francisco, San Francisco was one of the first places to impose a shutdown and the numbers speak for themselves. So it was done in a timely fashion. And we had one of the fewest cases compared to the rest of the country. The government is also incredibly resistant to the local government opening up.(39:48) – The big tech, Apple, Google, Microsoft, and Facebook, I think have tremendous amounts of power and a tremendous ability to help both with the technology. And there's just the vast reach of that technology, and the checkbook. The small tech, in my opinion, should be volunteering more.(41:30) – If we just never go back to the office, once the social capital is spent, It's not super clear to me if this is going to continue working just as well as it used to before. So that's why I'm looking forward to reopening. (44:44) – It's a defining moment for startups. That's where the borders are redrawn. And those who emerge from this, the strongest, will benefit for years and years after as a trust test, like COVID is bringing to the industry, that's the lens that we view our market. there's certainly a lot more fantastic people on the market that we can hire that bring those opportunities together. And because startups are nimble by nature and the decision makers are few, let startups actually seize those opportunities. (46:54) – Look at this as a stress test. I know that stress tests are good, if you survive them and you emerge stronger after it, that's really the focus for us. And that's what I wish the rest of the tech industry was going through as well. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSNikolas Badminton is the Chief Futurist at Futurist.com. He’s a world-renowned futurist keynote speaker, consultant, author, media producer, and executive advisor that has spoken to, and worked with, over 300 of the world’s most impactful organizations and governments. He helps shape the visions that shape impactful organizations, trillion-dollar companies, progressive governments, and 200+ billion dollar investment funds.Episode Links:  Nikolas Badminton’s' LinkedIn: https://www.linkedin.com/in/futuristnikolasbadminton/ Nikolas Badminton’s Twitter: @NikolasFuturistNikolas Badminton’s Website: https://nikolasbadminton.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(01:37) – about the age of 10, I started programming computers and I flunked out of school. I eventually ended up in a program called Applied Psychology and computing at Bournemouth University. I got a Bachelor of Science in that degree. I also went into linguistics and artificial intelligence and using artificial intelligence to do a grammar checking and grammatical investigations. Then I dropped into the data world, massive data infrastructures using analytics, behavioral targeting of customers using data, then I started to be hired to speak about artificial intelligence, and we really got into talking about the human ethics and the hybridity of humans and the machines. (05:04) – Something that can act as a human, move as a human, perceives, creates its own philosophy, creates some purpose… we are a long way from that. I questioned people that are trying to give that to machines. We can't work out what it truly means for ourselves beyond a metaphysical and a discussionary, a philosophical bent.(06:59) – This is about humans. This is ultimately about a hybridity between humans and technology. They're not robotics that are independent from who we are, that are suddenly trying to take over the world. There's actual practical applications that are going to help us solve big problems.(09:19) – Artificial intelligence just doesn't wander off and becomes useful. It needs a lot of training. It needs a lot of guidance and a lot of that practical expertise. It might be able to start identifying patterns that we may not see as readily or as easy as AI, but our practical wisdom needs to be injected into the overall solution. (12:06) – COVID is a black elephant. The elephant in the room and the black swan. If you've got a black elephant, it's that black swan that's been in the room for over a hundred years that everyone knows, that there's a risk of it out rearing its head and causing a huge calamity, but we've just conveniently pushed it to the side and decided that the likelihood of that happening is a lot lower than we really want to pay attention to.(15:44) – If you've had that level of a focus and investment in artificial intelligence, in weapons systems, imagine if that reality of Skynet becoming Sentient becomes an actual reality. And maybe it's seeding the black elephant with some really heinous code or training that's been done by someone that's got a grudge.(17:18) –  Using machine learning and data and analytics to make predictions using other practical solutions or a way from the normal ideas of technology. Climate change is the best example of a black elephant in the room.(23:55) – In America, culture is freedom. And anytime you tell me that you're taking my freedom away, I'm going to say, well, you know what? screw that. And that's the mess that America's got itself into. Singapore is very small. It can be contained and they've got ironclad rules around that. America's very big. And the idea of freedom isn't a bad idea. And democracy isn't a bad idea. This virus doesn't care about democracy. It doesn't care about freedom. It doesn't even care to infect humans. It just does it.(26:42) – This is going to go far and wide. It's our response to it, our ability to treat the virus, our ability to have healthcare that can help people that have it get over it in the more extreme cases, for us to take things seriously and to stay at home. We can see cases for years of COVID-19.(30:05) – If you don't shake hands and you stand at distance, if you're in the same room as someone or in the same open space, you've still got those mirror neurons firing. You still got that attraction, whether they're friends or lovers or potentials in either of those cases. And that's a pretty good step towards keeping social cohesion. Humans love to be around others. They just like the sense of human touch. And obviously, we're going to get back to that world.(33:35) –  World leaders are clamoring for hope. They're trying to calm everyone down that there is some light at the end of the tunnel. I'm hopeful that we're going to get that. There's very smart people in the world working together. Artificial Intelligence is playing its role. Analytics is playing, so big data and data science is playing as well. These practical uses of artificial intelligence are really why we're here and why we're talking about this in this podcast and beyond. (36:06) –  We've got to remember who's behind these solutions as humans, and even with the best machine learning and data sets, it's humans that are shaping the future and we're going to continue to shape the future.(38:10) –  This is not the absolute future of work. The absolute future of work is a fundamental reprogramming of how the industrial world works and gets out of the way for a true digital evolution of biology, communications, transportation, and energy.(41:51) –  I don't mind the idea of robots. What I don't really like about the idea of robotics is that we're trying to get them to do things that are so human, that it is driving us backwards in terms of progress. Robotics have got a huge role to play in the world. We need to stop chasing human style robotics that are suddenly going to walk like us and talk like us and just get back to basics on robotics that just do one or two things really well and without our intervention. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSAswin Rao leads Target’s global Artificial Intelligence team responsible for products involving Demand Forecasting, Inventory Planning & Control, Price Optimization, Personalized Recommendations, Search, and Marketing Science. He’s also an Adjunct Professor in Applied Mathematics (ICME) at Stanford University where along with research and teaching in Reinforcement Learning, He directs the Mathematical and Computational Finance program.His career has been to create or boost business profitability through advanced Mathematics & Engineering by recruiting and mentoring rare talents, foster a vibrant team culture, focus on the right business problems to solve, and meet challenging goals through diligent prioritization. His educational background is in Algorithms Theory and Abstract Algebra. His teaching experience spans topics across Pure as well as Applied Mathematics, Programming, Finance, Supply-Chain, Entrepreneurship. His current research and teaching focus is A.I. for Sequential Optimal Decisioning under Uncertainty (particularly Reinforcement Learning algorithms).James Jeude’s as an executive carries a record of growth and success, bringing Cognizant a 10x growth in data & analytics services revenue in the decade. He was on the management team, leading distinct P&L practices, and driving thought leadership and public perception. Creating all-country all-industry best practices for his clients gives him a perspective any company can use in an era where consumer experiences in one industry carry over into expectations for an unrelated industry.Episode Links:  Ashwin Rao’s LinkedIn: https://www.linkedin.com/in/ashwin2rao/ James Jeude’s LinkedIn: https://www.linkedin.com/in/james-jeude/ Ashwin Rao’s Twitter: @AshwinraoarniJames Jeude’s Twitter: @JamesJeudeAshwin Rao’s Website: https://www.collabera.com/ James Jeude’s Website:https://www.cognizant.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(01:35) – Ashwin Rao is the Executive Vice President at Collabera,  a $750 million IT services and staffing company. We are a high growth, innovative ID services and solutions provider, headquartered in Basking Ridge, about 16,000 people globally and 60 offices around the world. Tech 2025 focuses on experiential events and discussions to try to start conversations inside of companies. In that group, Jeude manages the consulting and strategy work that follows. He’s also an adjunct professor at New York University, an engineer by training, and a speaker and author on workforce topics.(02:39) – Our entire business is dedicated to helping clients meet their needs for everything from precision staffing of individuals to bulk staffing and solving business function problems. So we had two challenges. One, we had to rethink how Collabera operates in a work-from-home model. And two, we had to redesign our offerings for clients facing new challenges. This is so much more than just disaster recovery. No one had a plan to empty every office, everywhere.(03:21) – We believe that our workforce is the very heart of business. Businesses are the very heart of what keeps society running. And the topic itself is really part of the solution to our challenges, not merely a distraction.(04:10) – We see the virtual workforce model having five key parts: the places we work, the way we work, what we work on, demand management and the transition while undertaking this virtual workforce model change.(06:38) – Everyone thinks of when they hear virtual is working from home. You can't just assume, “well, we're all on a computer anyway. It doesn't matter where the computer is”. There's more to it than that. So we wanted to think about the places we work and when we come back after COVID-19, we probably still need some way to connect as people. (07:50) – Second point is the way we work. And I'm telling you, it should be completely rethought. If we can work virtually and remotely, the entire structure of teams and deliverables should be rethought. 97% of our companies now use Agile for software, but almost no one uses it outside. We have applied agile pod techniques to much of the virtual workforce model we use. Working in an age of AI and automation should become bigger, not smaller. By that, I mean that vendors, partners and teams should be given larger chunks of work with broader outcomes to help distribute the risk and reward of managing uncertainty and choosing the right approach in innovation. (08:57) –  As society tries to reboot, get supply chains moving again, some will start, some will stop. Now it might be tempting to reduce capacity when demand is down, but if it bounces back, you've got lost revenue.  If you have capacity that's higher than demand then you have wasted resources. And that is the eternal question.(09:58) – How to manage transition is a key element. And how you make good use of the idle moments as capacity stays in place waiting for demand to return. For companies that conduct knowledge, work and value added business processing, we believe in training, ideally training teams to a common goal. (12:33) – The identified five key elements of flexibility: One is location flexibility. Two is skills independence. Three is team upskilling. Four is platform independence. Fifth and last is team collaboration. (15:38) – Agile explains how a corporate team gets down into initiatives and then into epics and then daily tasks called stories. Agile method encourages and demands that teams cooperate closely, commit to local problem solving when possible, have frequent feedback up the chain and flow research and testing results among other teams. In a non-programming environment, these same principles apply.(17:43) – In a virtual workforce model, we can actually mitigate that equation a little bit. We can. In fact, we can mitigate it a lot. The extremes of this model can be dampened down by having variable resources that are applied to augment the fixed capacity. If demand occasionally rises above capacity, use trusted partners or flex teams to add capacity. If the demand drops below capacity, do not. We recommend dropping your capacity to match demand, because you might get it bouncing back sooner than you think. Educate the team, redesign processes, cross skill upskill, bill collateral, bill documentation and work on internal projects.(21:54) – Technology and good process design can make even healthcare delivery a candidate for a new workforce model. If it works in healthcare, it might very well work for the offices and functions of the listeners you have. (25:09) – From virtual workforce to virtual digital talent, to virtual contact center to a data visualization, all these services have been given a COVID accelerated response offering, in terms of how we can work in such an environment.(26:24) – COVID task force in these issues, this has to be top-down and cascaded to business units. Do not leave it to each worker to decide how chill or how manic they're going to be at home.(27:51) – We strongly advise companies to begin now to look at the obligation aside from work from home and not cost on the adrenaline that came from managing this crisis so far, we have done it so far and we will continue to do it. Think back and see what we need to do for planning a better work-from-home environment.(28:49) – Your clients and your employees will come out of this with new expectations and they don't match your old methods. Without adopting the principles that we're talking about or something similar, both the revenue side, that is the customer expectations, and the cost side, that is your employees and their expectations, are going to change dramatically.(29:55) – Our clients are demanding how the future is going to be and how we, as their partner, can help them take it to the next level. So we are excited while this opportunity came about, because this is not a great issue out there, but we are excited that companies are thinking differently and we have a role to play here by being agile and by helping them get to that model pretty soon.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJerry Ting is the CEO and Co-Founder at Evisort Inc. He is a former Board Member at Harvard Law Entrepreneurship Project and Harvard Association for Law & Business and was an Account Executive at Yelp.Episode Links:  Jerry Ting’s LinkedIn: https://www.linkedin.com/in/jerryting/ Jerry Ting’s Twitter:  @JerryHTingJerry Ting’s Website: https://www.evisort.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(01:36) – Law is a super fascinating industry in the sense that it's one of the last ones to typically adopt technology. Nothing with automation, artificial intelligence, business intelligence. But then you go into the law firm environment or the legal environment, and then we step back 10 to 15 years in technology. Legal tech is one of those morphous terms that emerged recently, but it's a new wave of technology that addresses the question of how to make lawyers more efficient.(04:23) – There's a really big market opportunity to both modernize and also look forward, bringing in automation and artificial intelligence to help an industry that provides a lot of value, but hasn't adopted technology in the way that financial counterparts have.(06:14) – Law firms bill on an hourly basis. If you bring in tools that save 80% of time, that might not necessarily be all good for a law firm for an in-house counsel, for a lawyer at Microsoft, for a lawyer at, and name any big firm, they're driven by traditional business KPIs. Being more efficient, being able to help close deals quicker, removing roadblocks for sales and procurement. These are good things for in-house counsel. So we focus on in-house corporate counsel. (09:21) – It's actually easier to change technology than it is to change people's minds. We think we can provide legal services, whether it's tech-enabled or with alternative billing models. There is a large opportunity for disruption in the law firm space. (10:54) – Microsoft is an investor. And the Evisort part of why that's exciting is that almost 80% of our customers use SharePoint or Microsoft teams to store contracts in one way or another. One of the main use cases is taking data that already exists in the cloud and activating it using machine learning and AI. (11:45) – One is for helping accelerate deals, helping accelerate how quickly a sales team can close contracts. We can provide a layer of automation to review contracts for proof. The other one is vendor management. Being able to see across a billion dollar supply chain, software license agreements to be paid, to be cancelled, to automatically renew, all in a calendar format and visualizing it. And the third one is one that encompasses both of the previous, which is bringing data to lights. (12:21) – A centralized enterprise repository where, regardless of where your contracts are stored, sales contracts could be in Salesforce. Employment contracts could be in Workday. Vendor contracts could be in SAP Ariba, but one centralized place where management can go and find and run a report and gather insights about their contracts across the entire enterprise.(13:18) – Our AI technology does a couple of things. We can take a scan of the contract that we've never seen before, convert it to a Word file and pull out over 50 different data points, including who the contract is with, when does it expire and what are the key legal terms. We can do that all today. From a content analysis perspective based on benchmark data, how to optimize this contract is the next level of intelligence. (15:44) – We understand what the customers need and then, we go to our research team and we already have models that we built that we'll test with. And most of them are deep learning models, a lot of research being done on natural language processing on computer vision. We test it on the existing models that we have. And then, if the accuracy is not where we need it to be, we start to tune that model and then add additional features.(18:57) – We've invested a significant portion of our R&D budget in building out a proprietary dataset that now spans hundreds of thousands of labeled data points. And the modeling then follows that. But without a large enough data, you might be building a model for the wrong subset of data. It might be under a fitted model. We're creating training data that customers may not have ordered yet, but we know that as a phase two and a transformation project they may need.(21:40) – Historically, contract management and AI vendors have focused on the things to do after you sign a contract. We recently announced a full collaboration platform from generating a contract, to negotiating it, to getting it approved, all assisted by AI. That's now available to all of our clients. We are the first company to go end-to-end from the creation of a contract all the way through renewal, all AI assistants all in one platform. (25:55) – There's a big difference between SAS companies and AI companies. Our idea is to combine the two. Combine deep AI analytics that were traditionally meant for large enterprises working with consultants. Democratize the AI that's easily digestible and verticalized for business function and then wrap it in a SAS platform so that anybody can use it. AI companies mature, they're going to build more end-to-end SAS platforms. And, it is going to be hard for the SAS platforms to build the AI capabilities. And that over time to merge into end-to-end SAS and AI platforms. (25:12) – The Bay area is world-class for scaling companies. The leaders and go-to-market and marketing and sales and customer success, product management, the go-to-market team in the environment that we have in the Bay area is hard to compete with, including New York. But New York is actually one of the main bases for customers. I try to get the best of all three regions, deep research out of universities in Boston, meeting with clients in New York, and then also running my office here in California.(28:02) – To be a Forbes’ 30 under 30 has given us some credibility and some recognition for the work that we're doing. We were never doing this as a hobby, we always believed in the vision and our ability to execute and then being named to the Forbes list was a validation for the efforts that we had so far. And then shortly after Microsoft and Vertex and other VCs invested $15 million. The 30 under 30 was a way for us to go out to our colleagues, peers and say, take a chance at Evisort and join us. We're here working on something cool, something meaningful and something impactful.(31:09) – What's happening a lot with verticalized AI applications right now is it's removing some of the tedious parts of a person's job, but it's actually making that person more effective in doing what they were supposed to do in the first place. I don't think AI is going to replace people's jobs. It's actually going to replace the points that people didn't want to do in the first place, so they can spend more of their time doing the strategic work.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDaniel Pianko is the Co-Founder and Managing Director of Achieve Partners. Pianko also serves as Managing Director of University Ventures. With nearly two decades of experience in the education industry, Pianko has built a reputation as a trusted education adviser and innovator in student finance, medical education, and postsecondary education. A frequent commentator on higher education, Pianko’s insights have been featured in national media outlets including The Wall Street Journal, CNBC, TechCrunch, Inside Higher Ed, and The Chronicle of Higher Education. He began his career in investment banking at Goldman Sachs, and quickly became intrigued by the potential of leveraging private capital to establish the next generation of socially beneficial education companies. After leaving Goldman Sachs, he invested in, founded, advised and managed a number of education-related businesses. He also established a student loan fund, served as chief of staff for the public/private investments in the Philadelphia School District, and worked as a hedge fund analyst. Daniel Pianko graduated magna cum laude from Columbia University, and holds an M.B.A. and M.A. in Education from Stanford University.Episode Links:  Daniel Pianko’s LinkedIn: https://www.linkedin.com/in/daniel-pianko-947223/ Daniel Pianko’s Twitter:  @danielpiankoDaniel Pianko’s Website: https://www.achievepartners.com/  https://www.universityventures.com/  Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(01:46) – COVID is going to do a massive experiment in taking millions of learners online in the space of a week. Online education in the US will get to maybe 50% of people getting their content online. It’ll be a second massive evolution revolution in learning at all levels, as those online environments will become even more robust, even more like a replacement for the in-person. In-person education is going to go away.(05:17) – Almost no Ed Tech platform has their own video interface, but Zoom is never going to build out the ecosystem that's required to actually run an online school. Packback uses an AI system to basically put it up. It uses AI to allow professors to grade online discussion, because you're not actually looking to grade very detailed work. (08:25) – You're seeing technology bring massive consolidation. And that is happening in education because an online learning environment has to scale, and scale is a different beast in the online world. We're going to have to move these things online and it's gonna reward scale in a way people are not ready for in the traditional education consumer market.(12:15) – People don’t quite realize how important schools, K-12 schools, physical schools are. They don't have digital connectivity. I would strongly encourage schools to look at organizations like K-12 and other online environments to figure out how to solve these equity issues. Especially, if it means getting technology in the hands of these kids.  It's a failure of leadership that we can't get these devices and the internet connectivity in the hands of our students, and I know it's hard, but that's no excuse.(16:32) – It's important that we differentiate between the aversion of online education that people are experiencing this week versus a real online education, because online education shouldn't have to be synchronous. (18:29) – Predictive analytics is not quite AI. We're able to dramatically open the funnel rethinking the entire classroom experience, technology experience, that led to a predictive analytics revolution in education, in medical school education. And now we admit students or we're starting to admit students based on their success in the MSMS. You can transform equity issues through technology and through predictive analytics and through AI.(23:30) – Adaptive learning has been the buzzword in education broadly, for the better part of 25 years. And even before then, some really great work was done down by very famous education professors who basically said there are different ways people learn. I'm not a technologist, but what is important for tech hardcore, techies, to understand is learning is still one of those fundamentally human endeavors. We have failed. And the reason why is because the technologists and the educators aren't connected enough.(25:45) – We're not where online education or AI driven education is totally worthless and meaningless. We're at this kind of in-between stage where the most successful interventions are going to be those where the technologist and the education folks can come together and say, here are the areas where we can deliver a high quality program that radically improves the product and it's going to be high-performance.(28:09) – Adaptive tests are a perfect example where technology works really well. Psychometricians can basically prove it. That's a better model for testing because it levels out where you're going to end up and allows you to drive a better outcome. While the actual instructional component will stay fairly human centric for the foreseeable future, a lot of these back office, I don't call the admissions office back office, but the non-straight academic functionality will become much more consumer-friendly and tech-driven and where AI can have a massive impact.(33:02) – People learn differently in different components. Sometimes I actually really prefer online learning. I'm actually not a believer that COVID is going to radically change human existence. I don't think technology fundamentally changes that. I do believe that the vast majority of humans want human to human interaction.(38:26) – The skills gap is massive and it is not going away. There are vast areas where the connectivity between education and employment has broken down. And we see a future where a series of intermediaries develop,  intermediaries that solve the education friction and the employment friction.(45:39) – Software is eating the world and it's changing how everybody operates. But at the end of the day, things around education and workforce are very human-driven. And there's a push to automate the job search and education processes.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSDaniel Whitenack is a Ph.D. trained data scientist working with Pachyderm. Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, Spark Summit, PyCon, GopherCon, JuliaCon, and more), teaches data science/engineering with Purdue University and Ardan Labs , maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.Episode Links:  Daniel Whitenack’s LinkedIn: https://www.linkedin.com/in/danielwhitenack/ Daniel Whitenack’s Twitter: @dwhitenaDaniel Whitenack’s Website: https://datadan.io/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(02:13) – Being online is pretty normal for myself and my team. I am fairly often on calls with people all across the U.S. but also in Singapore, and India, and Africa and all over mostly via zoom.  (02:55) – Our India teammates went fully remote from their office cause they're all programmers and software engineers and that sort of thing so they're all working from home. (03:56) – What's really boosted NLP in the last couple of years are these large scale language models, so oftentimes what you'll have in an AI model and that's processing text is you'll have a series either one or a series of encoders for text classification. What's really been interesting is these sort of large scale language models that have been trained like GPT-2 and BERT and ELMo, and there's a bunch of other ones. They're trained on a massive set of data, even sometimes for multiple languages, such that you really can apply that model to a wide range of tasks by just fine tuning to one of these tasks like translation or sentiment analysis, or text classification with a much smaller amount of data than was required before. That led to this explosion and application of AI and NLP(06:12) – The size of the models has increased a lot and they're processing a lot of data. These word embeddings or these representations of texts that are learned in the model encode a lot about language in general so it's been shown in a couple of studies that you can backtrack out of these embeddings, the actual traditional syntax structure of texts that linguists are familiar with like grammars and such and so in these embeddings is encoded a lot of information. (08:07) – Transfer learning depends a lot on that sort of parent model that you transfer from and there are sort of very multilingual models out there some including up to a hundred and 104 hundred nine languages maybe. There's actually 7,117 languages currently being spoken in the world. if we think about a multilingual model that has like 104 languages in it and it's Embeddings that it's language model supports, that's a drop in the bucket and some tasks like speech to text, or text to speech especially in NLP platforms only support maybe 10 to 20 languages and so there's a long way to go in terms of NLP for the world's languages. (11:29) – I'm really hoping that what we start to see in 2020 is a an acceleration of this technology through the long tail of languages because with 7,000 languages if we tackle like one language every six months or 12 months or something like that it's going to take us a long time to support things like translation or speech to text in 7,000 languages, so I'm hoping that we see some sort of rapid adaptation technology come about in 2020 that will let us tackle, 40, 50, a hundred languages more at a time.(13:46) – Teams that are starting to leverage that those existing resources, which really haven't been tapped into I don't think because they're archived in weird ways they're not in the sort of formats that like AI people typically are used to working in, so we're just at the tipping point where we can really jump in and utilize a lot of that data in creative ways. (15:17) –  There are certain languages that maybe aren't being used in the same way that they were before. There's other languages that would be used digitally, they're just not supported yet and there's economic concerns and literacy concerns and all of these things all wrapped up and so we have a lot of data around all of those things.(18:09) – For chatbots in general, I would say that there's less support for those than there is for a general technology like Google Translate or machine translation. So it's fewer languages than that, but you can do, again, some creative things to bridge the gap, like doing some of this transfer, learning and other things to build custom components under the hood to support new languages. whoever does crack the nut of rapidly.(22:38) – Imagine going into a new language community with a virtual assistant, imagine if that virtual assistant had the ability to query a natural language, that could enable there's still other pieces of that puzzle, like document search and that sort of thing but this is a big step in the right direction. (26:40) –  There's a lot of disruption and that's definitely true and there's a lot of people experiencing real suffering out there but at the same time there also some new opportunities that are arising. (36:15) – Our show is really focused on as you might have guessed the practicalities of being an AI developer these days and not only for those that are currently AI developers, but those that would like to be AI developers so we dig into a bunch of the different technology(38:03) – Reinforcement learning and generative adversarial networks scans both of those technologies get a lot of hype because of some of the things that they power like deep fakes and other things we haven't really entered into a season where reinforcement learning and GANs are really powering a lot of enterprise applications the way that deep learning models have actually penetrated.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSGideon Mendels is co-founder and the CEO of CometML. Gideon is an experienced data scientist and entrepreneur. He worked on Deep Learning research at Google and Columbia University and previously co-founded Groupwize.Episode Links:  Gideon Mendels’ LinkedIn: https://www.linkedin.com/in/gideon-mendels/ Gideon Mendels’ Twitter: @comet_ai Gideon Mendels’ Website: https://www.comet.ml/site/ 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(02:06) – Some people call Israel the start-up nation. New York's the new Mecca and it's the Mecca of technology.(02:46) –  To build a better model, especially if you're inheriting an existing one you try to figure out what people did already. You don't want to reinvent the wheel. You want to see what works, what doesn't. Where's the exact data set.(04:27) – We eventually collected most of the information, but we started from scratch because we wanted to make sure we're not basing our assumptions in something that might have been inaccurate. (04:54) – We found another approach that was much simpler than what we had in production. When you don't have the right processes and tools, it's really hard to bring ROI on these efforts.(05:29) – We have this amazing stack of tools, anything from testing, monitoring, orchestration, CI/CD, Versioning, you name it. And there's a lot of, sometimes, maybe too many, but then you go to machine learning teams and both of them are still using a combination of scripts, notebooks, and emails. And that's a fallback. There's definitely a better way to do this. It is exciting that developer tools and machine learning are helping these bigger companies to build reliable missionary models.(06:16) – Comet is a meta machine learning platform designed to help these machine learning or AI practitioners and their teams to build machinery models for real world application. The platform allows these teams to automatically track and manage their dataset, their code, experiments, models, as we solve problems around reproducibility, visibility, efficiency, and loss of institutional knowledge.(07:31) – Some engineers think Machine Learning is basically software engineering. But in machine learning, code is just one small piece of the puzzle. You have data, you have experimentation, you have results, you have models and models in production. But at the end of the day, these are different processes. And for that, we need different tools and different methodology. (08:57) –  Our approach has always been to be agnostic to what tool to use. We work with any type of machine in the library, whether it's the common ones, Perch, TensorFlow, scikit-learn, but even if you have something that's completely custom that you built in your garage or in your organization, you can still use Comet.(09:32) – Pick the best tool for the job but still have one platform where you can see everything, you can compare your results, you can share them, you can collaborate. So very similar from that perspective to what GitHub did for code, we're doing for machine learning.(11:36) – Python has definitely been the most dominant language on the machine learning side of things. We still see quite a lot of R users. Mostly those with a more traditional statistics background, but we also see people training models in things like Java.(12:31) – You can see the emergence of low-code or no-code solutions. Those will become more and more popular as we go, as well. (13:46) – Deepfakes, like with every new technology, are an amazing technology that is used and can be used for really great things. There's no question that people can abuse it. There are some similarities to hate speech in the sense that we will need to use machine learning to detect them. But we would need to make sure we set some kind of policy.(16:16) – We have major enterprise customers, multiple Fortune 100's across industry. We have some big tech companies, finance, automotives, media companies, biotech, retail, even manufacturing. We do have dedicated models and the platform to look at, computer vision problems, looking at your model predictions and debugging them same from natural language processing, tabulary data and audio. But we're not limited to a certain use case.(18:37) – We recently announced a partnership with Uber AI Labs which developed a really unique product or library called Ludwig. Ludwig is a no-code machine learning library. You kind of define the specification of the models without coding anything. And then you can train your model based on that. And Comet is the built-in experimentation management tool for that. (21:31) – For ancestry, one of the key things is they have Comet as the central place for their team to track their machinery and experiments and debug them. One of the biggest challenges in machine learning is debugging these models.  It's about figuring out where your model predicts the wrong results.(22:39) – One of the biggest value propositions in Comet is that they can look into the results of the model and track predictions over time and better understand what's going on and how to drive the research process forward. You look at the results. You decide that this model is not doing any good. Just you click a small stop button. It's very simple, but it's very valuable if you're trying to move quickly. (24:14) – Transfer learning falls under the subfield of meta machine learning; using machine learning to improve machine learning.The idea with transfer learning is by using a model or a training and a much bigger data set, you can get much better results than with your smaller one. This has two advantages: the ability to get a better result in your data set, but also saving a lot of costs.(29:11) – The predictor is an early stopping mechanism. We try to predict where your model is going, and then once things look like they're not going anywhere, or the model has converged or that the line essentially flattens in a way, you stop the model. You reiterate and try to figure out the next step. And that's essentially the research process. We can actually automate this process, and you get to move 30% faster.(32:48) – Product side, instead of trying to solve all the problems in this space, to build one end to end solution that does everything. If you have one platform that replaces AWS, New Relic, GitHub, Jenkins, all the tools in the world, one product with one login, that's something that's very hard to do.(35:07) – Machine learning is essentially becoming another tool for engineering. Things will definitely converge. And if you look into undergraduate programs, for example, machine learning and AI have become part of the core curriculum.(38:09) – If you're trying to classify some examples you can go back and do the data labeling process and get more data from this class and drive the research process for it. In production time, you won't be surprised anymore because you already looked into all these edge cases and solved them in training. These are the two main approaches people in the industry are taking.(43:16) – It's very important not to get married with a single library, use the best research that's out there. (44:11) – Overlap and collaboration between academia and industry. That convergence of being able to support both ways is very exciting. More companies are being able to get real business value from machine learning and AI.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSMike Robins is an author, speaker, coach, and podcaster who delivers keynotes and seminars (in-person and virtually) to groups of all kinds throughout the world. He’s written five books, which have been translated into 15 different languages. His latest, We're All in This Together, has just been released. Before starting his business in 2001, Mike played baseball at Stanford University and then with the Kansas City Royals organization. After baseball and prior to starting his consulting business, he worked for two tech companies in online ad sales during the "dot-com boom" of the late 1990s.Episode Links:  Mike Robins’ LinkedIn: https://www.linkedin.com/in/mrobbins/ Mike Robins’ Twitter:   mikedrobbinsMike Robins’ Website: https://mike-robbins.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(01:50) – It's definitely called upon a lot of us to dig deep and think, what can I contribute to the world? There's a desire for us to connect, even though we're all separated by time and space more than ever these days.(03:05) – In some ways the rules have changed very quickly through this pandemic, we're all getting a chance to connect with and see each other's humanity as people are trying to do work from the dining room table and their kids are off to the side.(06:52) – There's a lot of companies, especially tech companies who've been needing to embrace video and other platforms in order to communicate. There are a number of companies that we work with for which that is the primary way they communicate anyway, prior to COVID. That's going to allow us to work in a more effective and productive way, but also humanize the technology if you will, because that's been one of the challenges over the years and continues to be. (09:04) –  Everybody is panicked in the sports industry because they don't know what's going to happen. They're just hoping and waiting for things to get back to normal, but it's impacting a lot of people's lives and a lot of people's jobs in the short term.(11:47) – The technological capabilities of schools are often dependent upon the socioeconomics of that school or that community. Not everybody has access to the same technology and so just as that exists in school in person, it also exists online. The education experience, just like the meeting experience or the conversation experience, is different when it's done virtually than when it's done in person.(13:57) – We want an education just like in business, that can be innovative and creative, adaptive and adjusted to the moment.(16:31) – if you had the best players you would have the best team. If you have the most talented players, you should have the best team. But that was not the case at all and I learned it many times in sports. (18:00) – It was the intangible qualities that allowed us somehow, some way to put our little egos aside and be interested in each other's success, wanting to win as a team more than simply just succeed as an individual to cultivate an environment where people can work together in a way that actually brings out the best in everybody.(21:30) – To be good at anything and ultimately sustain that success and grow, you got to master yourself. We're talking a lot more about mindfulness, both in our schools and in businesses, because we're all dealing with similar macro experiences, even though we have our own little world. Much of our success or failure has to do with mindset and approach. We need some talent, but the same is true in business.(28:44) – We actually have a lot more common ground with each other than we think we do. We're separated in a way we've never been forced to be physically separated before, and we're simultaneously connected to each other in this global experience all at the same time.(32:30) – Maslow's Hierarchy, the third place on the pyramid once we get past the physiological and safety needs for human beings is a need to belong. The ultimate  goal is to get to a place where you create an environment where everyone irrespective of their background, their race, their seniority, their age, their gender, their all of that everyone feels like they belong and that's not easy. (35:40) – Authenticity and vulnerability is the iceberg. And it's about lowering the waterline on the iceberg so that we share a little more authentically and vulnerably how we're feeling and we really ask other people how are you and not just the corny hey, how are you? How's it going? What's up? Reach out a little bit to offer some support.(39:26) – Have some compassion for ourselves in the midst of all this, and also have some grace and compassion for other people. There are a bunch of companies and apps and pieces of technology that maybe we didn't realize were super important that are now becoming very important in a different way because of what's going on. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSRishal Hurbans is the Business Solutions Manager at Entelect where he is responsible for business development, strategic planning, ideating, and designing and developing solutions for local and international clients; whilst actively nurturing knowledge, skills, and culture within the company, community, and industry. He has a passion for business mechanics and strategy, growing people and teams, design thinking, artificial intelligence, and philosophy.Rishal is the author of Grokking Artificial Intelligence Algorithms with Manning Publications, aimed at demystifying AI algorithms for technologists by teaching the approaches through practical problem solving and visual explanations: Episode Links:  Rishal Hurbans’ LinkedIn: https://www.linkedin.com/in/rishalhurbans/ Rishal Hurbans’ Twitter:  @RishalHurbansRishal Hurbans’ Website: https://rhurbans.com/  http://bit.ly/gaia-book 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(01:49) – “Grokking Artificial Intelligence Algorithms'' consists of 10 chapters that explore different AI approaches. It's part of Manning Publications, MEAP, which means Manning Early Access Program. And the benefit of that is we get feedback from readers as we release chapters, which allows us to refine and create a better book at the end of the day. Once all the chapters have been released and we get some feedback, the book would be then printed and finalized. (03:22) – The term Grok or Grokking is to gain a deep understanding about something, but through intuition and through some sort of feeling about it, demystifying these algorithms that are sometimes underappreciated. Including the modern hyped concepts like machine learning and neural networks, to actually help the reader understand why it works and how it's useful to the day to day.(05:16) – A lot of Funding has gone into creating this kind of skills and capabilities in different organizations. There's a lot of solutions and proof of concepts that have been bolts that work in theory or work in a ring-fence environment, but perform poorly in production or don't provide the value that was originally envisioned. A lot of effort is going into understanding now, what are the critical aspects to what we're doing with this technology. How do we understand it better? And how do we target it or direct it in a better way as opposed to running a bunch of experiments and see what works.(07:03) –  It's not a lack of engineering or a lack of know-how in actual execution. In any technology that we've built, especially software, at the end of the day, it comes down to solving a real world problem, whether that's a business problem or, whatever the case might be. Usually it comes down to a business problem that you're solving. (07:49) – It's not actually addressing the problems in a meaningful way because we just tried everything. Also, partly it's because people have been trying the hype buzzwords, because they're a good idea. And you feel like if you're not doing it, you're doing something wrong. From a global decision-making perspective, the stakeholders involved there, the different people involved, they need to have a better understanding of what problems the technology is solving, as opposed to just simply using it, to implementing it for the sake of it.(09:40) – The focus on the different algorithms is driven by a theme or concept I mentioned just a bit earlier. So instead of trying any new technique that you come across, I wanted to highlight the advantages of some of the underappreciated algorithms. The goal was basically to expand a technologist or a developer's mind in terms of what the possibilities are when being faced with a problem. There's no silver bullet and here are the advantages and disadvantages of the different approaches. (12:35) – Specifically with search, it's mainly exhaustive, you had to try every possibility to find a good solution, whereas, more modern approaches try to estimate a good solution. A person would have to know what questions to ask. What modern approaches and machine learning and deep learning try to do is learn from examples and learn from previously made decisions to figure out the questions.(14:07) – Modern algorithms are geared towards different problems that we're trying to solve now, but computing has definitely made it possible for things like artificial neural networks to become more prominent.(16:11) – Large amount of data that's been collected through connecting the world, the actual value that's hidden within that data and the kind of advancements in computing have allowed us to leverage these algorithms. And as I said, old algorithms that can now do some really powerful and useful things. (17:26) – The implant search is also sometimes referred to as adversarial search. It's essentially used for two player games like chess, and the whole concept is centered around an agent predicting the future. So if I'm an agent. And I see a certain state of a chess board. I would make a move and then simulate every move that my opponent could make and score that. Games like Dota and StarCraft, they're using something completely different. So they're leveraging reinforcement learning and deep learning. (19:03) – You're not working on a two dimensional space where you're moving pieces a few blocks at a time you're working in a very fluid environment. It's almost simulating reality. Detailing every single piece of information and representing that as a state and then trying to predict every possible future for that state becomes very difficult to do with traditional adversarial search approaches.(19:52) – They try to let an agent learn from experiencing the game. What a deep mind, open AI and similar organizations have done is basically allowed an agent to play itself many times and figure out what short-term actions and mid-term actions may result in long-term rewards. I'd like people to be more pragmatic because the more pragmatic you are, the more effective you are at solving what's important.(22:49) – Technology, including data science, including software engineering or mobile development or whatever facet of technology we're working in, I see it as a tool or a vehicle to deliver value or solve a problem. There's a difference between a successful project and a successful solution. There's this deep focus on what tools and libraries and technologies and programming languages, and what are you using as opposed to, why are you using it? What are you trying to achieve with it? And that's not just a problem in data science. It's a general theme, but we're getting better as we go.(25:57) – A big misunderstanding is the glamour in bolding, something with machine learning or AI algorithms about 60, 70% fried, depending on the surveys, you look at 60 to 70% of  data scientists work is usually understanding, cleaning, preparing, enriching, augmenting that data before it becomes useful. And even after you do all that work, you don't actually know if that data is going to solve your problem or not.(26:58) – Every solution should contain some sort of data science or AI element to it. And that's not really the case. So unless there is a clear use case, not that fits the use of some sort of either classification or reinforcement learning or optimization algorithms. Unless there's a real use case for that, it shouldn't just be taken into consideration. You should think critically about how you can build a minimum solution that solves the problem in the best way. (29:19) – I would have spent a technical perspective and a growth perspective specifically in the area of AI and machine learning, I would have made a bigger effort to figure out why math is useful in these concepts. Do not give up on that and perhaps try and seek material or people or mentors or someone that can explain to you in a more human way, how these mathematical principles work, but more importantly, why they're important.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSEric Adams is a former State Senator, and current Brooklyn Borough President running to be the next Mayor of NYC. He was born in the Brownsville neighborhood of Brooklyn, went on to earn an Associate in Arts degree in data processing from the New York City College of Technology, a Bachelor of Arts degree in criminal justice from John Jay College of Criminal Justice, and a Master of Public Administration degree from Marist College. Eric graduated from the New York City Police Academy in 1984 as one of the highest-ranked students in his class. After initially serving with the New York City Transit Police Department, he was transferred to the New York City Police Department (NYPD) with the merging of the city’s police forces.Episode Links:  Eric Adams’ LinkedIn: https://www.linkedin.com/company/eric-adams-for-mayor/ Eric Adams’ Twitter:   @ericadamsfornycEric Adams’ Website: https://ericadams2021.com/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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) – We were able historically to get away with the dysfunctionalities of cities. In the next 20 years, as we evolve into computer learning and artificial intelligence, we have to change how we run cities so we can keep pace with that.(02:16) – The real fact that we're not addressing COVID-19 in real time with real data and real on the ground response is really exposing. Our cities across America and in general, specifically, here in New York, are not prepared to see how you run cities in the 21st Century.(03:40) – We have a disproportionate negative impact on certain communities. When you look at the term of essential employees, over 70% of these central employees are black and brown people. When we see the decrease or the increase we are talking about specific populations, over 60% of the people who died from Coronavirus are black and brown.(06:37) –  Free food for all New Yorkers is open to people who are in need of a meal who can't travel far to their community.(07:27) – We have a large number of people in this city who are seniors. It is our responsibility to teach our seniors how to be introduced into the technology.(09:11) – Our influence really impacts the entire globe. And here in the city, we're in a fishbowl in that we all live together. Our technology, the technologies that we use must be part of preparing our future employee pool and how we run this city in an effective way.(11:22) – The population that was less likely to use technology, our senior population, are compelled to embrace the technology that's available. (13:30) – Government officials need to make sure students have the devices and the technology that they can remain engaged.(16:21) – The more we build out using the free wifi, and it should be a right in all communities, the more we learn where our gaps are. And it's important to do a GIS mapping of the entire cities.(18:25) – It's not a one day strike. It is imperative that as we go through this crisis, we're thinking about rebuilding in the meantime. How do we look at this new norm that we are going to embrace?(21:11) – The New York City Employee Retention Grant program is a great program because many jobs are being impacted, they want to lose employees. And if you hold onto your employees through this program for a particular period of time, you are able to take the benefits of this program. (22:39) –  We should do a 90 day moratorium on rent as well, as long as it's matched together with the moratorium on mortgage payments. (26:53) – What we must do is continue to get the information out into the crevices of all of our communities. (29:26) – We need to try to provide personal protection equipment to all essential employees. We need to make sure that any employee that's considered an essential employee, that they have some form of healthcare package(31:55) – You don't have to break your traditional bonds of coming together as a family, we just have to be more creative in doing so. We are a resilient community, city and country, we've had hard times before and all we have to do is come together. Show a level of compassion, commitment, and dedication, not only to each other, but to ourselves. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSGabriele Columbro is the Founder and current Executive Director, FINOS at Linux Foundation and Member at Forbes Finance Council. Columbro is an open source leader and technologist at heart, having spent more than 10 years building thriving communities and delivering business value through open source. He thrives in working with open source communities to drive disruptive innovation, whether it’s for an early stage tech startup, a Fortune 500 firm or a non profit organization. Gabriele brings expertise in executive and technical leadership, ranging from FinTech to enterprise collaboration, from developer platforms to SaaS ARR business models. Previously Director of Product Management at Alfresco, as Executive Director, Gabriele built the Symphony Software Foundation from the ground up, with the vision of creating a trusted arena for Wall Street to accelerate the digital transformation, engaging in a new model of open source FinTech innovation, backed by the largest global investments banks like Goldman Sachs, JPMorgan Chase, Morgan Stanley, Citibank, Deutsche Banks, Nomura, Wells Fargo, UBS, Credit Suisse. Gabriele is also a PMC Member for the Apache Software Foundation and an advisor for Bankex.com.Episode Links:  Gabriele Columbro’s LinkedIn: https://www.linkedin.com/in/columbro/ Gabriele Columbro’s Twitter: @mindthegabzGabriele Columbro’s Website: http://mindthegab.com/ http://mindthegab.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(02:01) – There are some major shifts happening in the industry and all the arrows pointing to open source as a brand new way forward for this industry. There are systemic reasons why we're seeing the rise of open source, same financial services margins. Revenues of nowhere nearly where they were 10 years ago in this industry, the cost of regulation keeps rising. (04:20) – So there is not an infinite amount of money to be thrown at every single technology problem in the industry. And open source certainly has had a history of reducing technology costs when using TCO. That's one of the main driving reasons for financial institutions looking at open source collaboration. Open source provides a much larger, much broader talent pool, and allows every individual to continue fostering its own portfolio. Open source doesn't equal free, there's a lot to be saved, but also a lot of money to be made on open source.(09:07) – This generation has grown up with social tools and a really different way of even interacting with each other. The new generation of developers that we see coming up comes with being born and bred in GitHub.(11:19) – Open source is not charity. There's an element of conscience, of openness. Everyone, and most corporations participate in open source right now. And even our foundation, they do it with a business goal. So it's not per se charity. it's not just talent acquisition, it’s certainly a lot of talent retention as well. (13:04) –The rise of open source out there and the rise of non-profit open source foundations is because open source is not easy. Especially if you're a large corporate who's seeking to collaborate either with its competitors or with its customers and ecosystem at large through open source. (13:51) – Code is certainly important. And the quality of the open source code is higher. Everyone feels a bit more accountable for what they put out there than necessarily what you do behind the firewall. But that's just the tip of the iceberg. (10:05) –There's an element of internal and external policies. Regulated industries are very understandably risk averse, and very much careful about what degree of collaboration they have with their competitors. That's why foundations like ours provide a very structured governance framework, conflict of interest policies, antitrust policies, making sure that it's clear that through transparency, you can achieve a very productive level of collaboration without any compliance concerns.(145:39) – Policies are one element. You mentioned standards. The world of open standards and the world of open source had historically been very different, but they are more and more colliding because they reinforce each other. When you add the open source reference implementation to an existing standard, that drastically speeds up the rate of adoption, and certainly the rate of compatibility, cross compatibility through the standard.(16:25) – The generational cultural aspects include a lot to learn before you can be effective and productive in an open source community, the same way you do it in an internal project. You need to relinquish control in favor of influence. And that's a big step for hierarchical organizations, large corporate hierarchy organizations. But there's also an element of code of conduct and behavior. Open source communities that are driven by meritocracy, or even by the more contribution, the more sweat equity you put into, the more influence you have.(19:03) – Government is one of the models that we're using for modeling the collaboration in our community. Governance and code governance and corporate governance. All of our governance is public and transparent, which leads us to traceability. Every decision is traceable and is auditable(24:05) – There is an intention and a goal for the industry to better model the data in a collaborative way to be clear, not only to collaborate on the code itself, on the visual modeler and on the language, but on the models themselves. Create this common modeling tool and common set of data models in the hope that with common data models, we can start building on top of it common tools and common ideally AI and ML and intelligence around it. (30:56) – It is understandable how institutions who have not only such a regulatory nature, but very sensitive information about their customers would think twice before sharing information with some of their competitors, whether that's because it's a unique differentiator or even just because of fear of breaking some regulation. There’s lack of data standardization, and we’re identifying more technical solutions to enable shedding in a safe way. Big tech through open sourcing is enabling some of these better collaborations happening also in financial services. (34:46) – Open source can be a means for financial institutions to really have an alternative to the usual maker by decision. Its transparent nature, which is a talent pool expanding nature, goes back to traceability. That's really a good driver for financial institutions and fintechs to collaborate in the open. Rather than having to train and specialize people in every single system that you're going to have to go and regulate, you can build a broader talent pool if the implementation and the process is dealt with in the open.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSLorna Davis has served as President of multinational consumer goods companies for 20+ years, in Danone, Kraft and Mondelez. Lorna has been a key leader in Danone’s purpose journey and is a Global Ambassador for the B Corp movement. In 2017, she served as CEO and Chairwoman of Danone Wave (now Danone North America), where she established that $6 Billion entity as a Public Benefit Corporation and achieved B Corp status in 2018, making it the largest B Corp in the world. Lorna is a member of the Social Mission Board of Seventh Generation, the Integrity Board of Sir Kensington (both Seventh Generation and Sir Kensington are owned by Unilever)the Advisory Board of Radical Impact and the Board of the Stone Barns Center for Food and Agriculture.She has lived and led businesses in 7 countries including the UK, France and the USA and served on the Global board of Electrolux for 6 years.Lorna was also based in Shanghai, China for 6 years where she was the CEO of the merged Danone and Kraft business.Episode Links:  Lorna Davies' LinkedIn: https://www.linkedin.com/in/lorna-davis-3366ab14/ Lorna Davies' Twitter: @lorna_davis10Lorna Davies' Website: https://www.lornadavis.net/ 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(01:37) – When people ask me how I'm doing i noticed that the answer is only relevant for this moment. I'm variable like everybody else and I'm trying to just take it one moment at a time really. (04:17) – What we will also come out of this with is a really good understanding of ourselves, which will be very important for the next phase of the world. We'll be more self aware and hopefully more compassionate and more loving leaders in the future.(06:31) – Work out how to calibrate, how to be supportive, but now how not to be helpful. Because being helpful is a pain in the neck, nobody wants to be helped. But how can we really provide support for each other at a time when people are still trying to work out what support they want. (09:24) – Very interesting to see how businesses are pivoting. I'm loving the innovation that's coming out of this and I'm also loving the new relationships, new collaboration, new interdependence that's coming from this.(13:26) –We're going to see things that we have never seen before. And we're also going to see a complete reshaping of traditional blocks of time. This sort of neat disruption of the day is challenging for some people. These fixed boundaries between these periods of our lives have dissolved perhaps forever. We'll be easily able to segue away from laying on the couch, reading a book to getting up, to do a yoga class, to doing an hour of work, to going to learn the tuba. There'll be fun. (17:27) – The inclusion of people who are shyer than others. With that hand raising function, people who would otherwise struggle to fight their way into a conversation can put their hand up. These are all things that will enhance intimacy and connection that I hope we hold on to when we go back to more in person meetings.(21:50) –  It was unthinkable before that you and I might build this kind of relationship and never meet. And people have old fashioned ideas about how people need to be face-to-face to really build a relationship. I don't think that that's true.(30:25) – The big question is if you really want to solve the climate challenge, countries need to work together and they need to have a line of legislation on carbon reduction. They obviously need to sign up to agreements and they need to have a shared view that the world has a problem that the world has to solve together. (33:22) – There will be stories that as human activity slows down, natural activity will rectify itself or come back to life. And hopefully we will fall back in love with the world, fall back in love with nature, fall back in love with the universe really. And that'll give us a new sensibility. It is a better, more grounded place to act from when you are in love with other humans and in love with nature than when you're frightened, angry, defensive, and think that your money is going to save you, which is kind of what, has been predominant in parts of the world recently.(35:58) – The water will deliver us into common ground, or intercommon waters, and then we'll be able to find our ground. Everybody knows that it's chaotic. Nobody can pretend that it isn't. So it is, so let that be.(41:50) – Women's ability to deal with ambiguity and complexity and interconnectedness is better than men's. This time of ambiguity, complexity, multitasking is the time for them to really step up. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Story of How AI changed Google Maps with David Yakobovitch.You can support the HumAIn podcast and receive subscriber-only content at http://humainpodcast.com/newsletter .Learn about your advertising choices at: www.humainpodcast.com/advertise .Available for reading on Medium: https://medium.com/swlh/ai-google-maps-79237f8946e3 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSFernando Gomez Baquero is the Director of Runway and Spinouts at Jacobs Technion-Cornell Institute and Founder at Besstech LLC. He’s an innovation economist, nanomaterials engineer and entrepreneur who mentors companies on diverse topics such as IoT, digital innovations, new materials for transportation, creating better electric vehicles, improving wind and solar power, using social networks for gratefulness, and and more.Episode Links:  Fernando Gomez Baquero’s LinkedIn: https://www.linkedin.com/in/fernandogomezbaquero/ Fernando Gomez Baquero’s Twitter:  @FerGomezBaqueroFernando Gomez Baquero’s Website: http://www.fernandogomezbaquero.com/index.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: (00:00) – Introduction(01:37) – The most reasonable thing to do initially was to fairly quickly move every single class to online, which we did pretty fast andthe good thing is that we were already prepared for that. Most of our classes were already streaming and we already had a lot of experience doing that. (04:17) – We live in a good time that we definitely can move a lot of things to virtual and we are able to shift to that pretty fast. And I hope that everybody knows that by doing that, we can deliver not exactly the same content and continue to work that way. So, this is really a test of the future of work.(05:48) – Cornell Tech was created as an economic development story or as an economic development driver for the city of New York. Why don't we basically get the best of both worlds? revitalize an area that hasn't been used for a while, which is the Southern side of Roosevelt Island. And then we use that space to bring a campus of a university that is going to focus a lot of engineering and scientific resources to create the companies in the future. And that's the purpose of the campus, focusing on entrepreneurship and creating new companies.(08:09) – We no longer see entrepreneurship and academia as a binary thing. We don't see it as, you need to do your masters program. And then when you finish, you do entrepreneurship and you build a company in the country. What we see is while you're in the academic environment, you can be doing your degree. You can be working towards your degree, but at the same time, you should be creating a company. And we are more than capable of not only giving you the space to do that, but training you to do that with the people that have done that. So the people that come to Cornell Tech are people that want to have that academic and entrepreneurship experience at the same time, which is a lot of work.(09:43) – If you take a look at that set of degrees, it is  just the right combination of skills to build the company. And so once you take those people that are, one of them is a computer scientist, one of them is an engineer, one of them is an MBA, one of them is a lawyer and you put them together in teams, you build a very early stage, very good company.(10:58) – It really depends on where you are in your life right now, what you want to do. If you want to be an entrepreneur our goal is that we will have a program for you. If you are working in a company right now, you'd be working as a program manager or a project manager for a while, and you really want to have that experience of saying, I can give myself a year to improve my skills, know something better. And at the same time, have that experience of building an early stage company.(13:46) – We give them all of the support that they can get. And as Nanit would really focus on computer vision, we have companies working on genomics on computational biology, on computer vision for construction and infrastructure on a better simulation technologies for spaces. On big data on other types of devices. It's really a wide range of applications. (17:01) – Tech transfer is something that has been done in universities for many years. And that the dynamic of tech transfer has really changed for decades. And that dynamic is, you are a researcher, inside of a university system, creating knowledge, that knowledge belongs to the university. And then the university is trying to find on the outside ways of commercializing that research. (18:29) – People who are creating the knowledge are the best vehicles for commercializing that knowledge. We trust that you're the one that can make this into a billion dollar company. And what you need is for us to help you succeed, to give you the training that you need, to give you the tools that you need, to give you the resources, to give you the connections, to give you the environment that you need.(21:11) – We have a couple of our postdocs that immediately switched their companies to say, we can develop better financing strategies for what needs to be done with COVID. We have some other ones that are saying we definitely need to work a lot on finding a test for immune response to COVID. So now we have all of these people working on the health tech side.(23:15) – We're enabling communication in a different way, but we're also enabling leadership in a different way. We have people working on the future of work this way. We have people that are really building interesting tools for the gig economy.(25:01) –  There's very few segments of the population that are actually doing artificial intelligence. There's some that are, for the most part, who we're trying to teach our companies. And most of them are either doing some type of some interesting application of machine learning. Perhaps it could be some interesting signal processing or hubs or data mining in a particular way, or using tools like natural language processing and computer vision.(27:15) – We're still in a very primitive way on how we see machines and interact with them. We have just scratched the surface of how it is that we can improve our interaction with robots. (30:43) – We have many tools right now. These technologies, these tools, and just are  great opportunities to use all of that toolset for a very big problem. (33:07) – We have people that were product managers and they definitely don't want to be product managers anymore. They want to be entrepreneurs. They want to be CEOs, so this is just a segment of people. We have some that have been product managers and they want to continue to be product managers, but they want to raise their skill level. Now you can be an entrepreneur, you can be a product manager, you can be a CTO, you can be other things. And this really what we want, to open up possibilities for a career. (36:23) – Studio is really, the most innovative part of Cornell Tech. The core idea is that you can practice entrepreneurship while you are in academia, but practicing the real way, meaning that you could be driven to entrepreneurship and you can have that experience of being an entrepreneur at the same time that you are in academia.(39:15) – There are a lot of tools out there that you could use. Figma, Trello, Slack. There's a lot of inducing communication. WhatsApp actually is huge in a lot of parts of the world for communicating with businesses too. So for sure use the tool that makes more sense for the community that you're trying to get to.(42:25) – We have amazingly smart people oriented towards the common good that are putting a lot of effort into finding solutions. So that is some positive news. We don't want to downplay how complicated the situation is. It is an opportunity for all of us to to create things that are important for society, things that are good for society. And we are shifting a lot of resources to solve this problem.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSaleema Vellani is an award-winning serial entrepreneur, keynote speaker, a professor, and the author of Innovation Starts With “I”. At the age of 21, Saleema co-founded and launched Brazil’s largest and #1 language school to finance an orphanage and social development programs, which has taught several thousands of students to date. Shortly after, she co-founded and ran a leading online translation agency in Italy to help companies expand their digital presence globally, while generating hundreds of jobs in the gig economy. The business was acquired in 2012. For over 12 years, Saleema has led 100+ international organizations, nonprofits, and Fortune 500 companies to their next stage of growth and innovation. As an intrapreneur, Saleema has been co-leading award-winning, groundbreaking research with the World Bank on solving food insecurity in conflict-affected countries through climate-smart technologies since 2016. Given her experience with running businesses online, in 2013, Saleema led startup education programs for Upwork (formerly Elance) to train Washington DC-based business owners on how to hire and manage remote teams.Currently, Saleema is the Founder and CEO of Ripple Impact, which helps entrepreneurs increase their influence and impact through accelerating the growth of their platforms and businesses. She also teaches Design Thinking and Entrepreneurship at Johns Hopkins University and is a frequent guest lecturer at business schools.Episode Links:  Saleema Vellani’s LinkedIn: https://www.linkedin.com/in/saleemavellani/ Saleema Vellani’s Twitter: @InnovazingSaleema Vellani’s Website: https://saleemavellani.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(01:53) –  I was embracing a lot of the principles and the actual design thinking process. It is about iterations and cycles, but it was really about understanding a problem and that's something that we talk about customer development, understanding who are potential customers and what's the problem we need to solve. We realized we we needed to really carve our own niche and focus on what was working (05:16) – The skill of being able to think like a designer doesn't mean everyone needs to becomes a design thinking expert or an innovation expert, but just the skill of being able to connect dots that seem unrelated and that's also referred to as associative thinking, there's different theories around this, but really trying to connect things I that already exist in new ways, that ability to think that way is one of the skills that's going to be really important for the future of work.(05:51) – We're in the middle of this re-skilling revolution right now as stated by the world economic forum. Embedding that in the culture of an organization is becoming increasingly important.(06:35) – Innovation starts with I, and the mindset and developing that innovative way of thinking and being able to only share creativity and really just knowing yourself, know your sweet spot, what do you do? Or what can you offer to the world? Understanding who are the stakeholders that you need to really understand when you're solving a problem and then making your impact on the world through that and so that with more and more with technical fields.(07:03) – Showing empathy. Understanding yourself and who you are, and that ability to make things more humane. Understanding humans really starts by understanding yourself.(09:17) – Thinking about what data is available, but the design thinking mindset can be applied and data scientists, being able to question that and using design thinking principles, whether it's starting from empathy to really framing the problem and that's one of the hardest parts of design thinking is being able to frame the problem correctly, because oftentimes we're thinking about the solutions without really understanding the problem.(11:26) – We're entering the fourth industrial revolution as we talked about we're in this re-skilling revolution and a lot of businesses are stalling and they're falling behind, or sometimes it's hard to even see that you're stalling when you're so focused inside of the business and not on the business to develop that awareness until it's too late and you've been replaced or you've been automated.(18:15) – Resilience is really important for everyone to have and when it comes to innovation, entrepreneurship, design thinking. The time where you're hitting a dip, you're going rock bottom and you're not sure whether you should go, you should keep working at it, what to do and it's almost like crisis mode and that's happened to me. Resilience is important because you have to be okay with failure and more and more companies are trying to adopt this culture where failure is and it starts by having a psychologically safe environment.(22:48) – The Coronavirus has actually enabled us to be more human and really understand what's going on in the world and developing that global awareness, which is another insight that I got through my book interviews is really understanding what's going on with different cultures.(26:12) – With design thinking, it's important to understand the experience that humans or your customers go through and on the backend there is lot of the coding, a lot of that's already being automated a lot of things are being replaced,(28:04) – That ability to think in that way, like a designer, even just enough so that you can humanize the code or humanized data science, that's going to be increasingly important. (29:46) – Constant learning, the ability to just constantly be in learning mode and going to conferences, absorbing content. Try to get at least one nugget per day and learn something new and make that part of your routine that's really important to stay up to date with the trends cause it's so easy to just become obsolete in today's economy. (32:12) – This rise of entrepreneurship is like everyone wants to be an entrepreneur, a lot of people are trying to participate in the gig economy, being entrepreneurs and even the concept of an entrepreneur has evolved so much. There's Instagram influencers, social entrepreneurs, focusing on the feeling and the impact that's important, as well as figuring out how to collaborate with other people.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSHefin Rhys is a Senior Scientist (flow cytometry) at UCB. He completed his PhD at the William Harvey Research Institute in Queen Mary University of London in 2017, and graduated from my MPharmacol degree from the University of Bath in 2013. His main academic interests are conventional, imaging and small particle flow cytometry, data science and machine learning. Episode Links:  Hefin Rhys’ LinkedIn: https://www.linkedin.com/in/hefin-rhys/ Hefin Rhys’ Twitter:  @HRJ21Hefin Rhys’ Website: https://www.manning.com/books/machine-learning-with-r-the-tidyverse-and-mlr 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(01:44) – My view is not that of someone who is an expert on this virus, but it's clearly something that's very serious and that we need to take seriously and treat with respect. So as much as the virulence of the virus itself is concerning, I particularly consider how viral misinformation and misinformed practices have gone along with it.(08:24) – As a pharmacologist, my PhD was in immunology. The traditional analysis methods that we had been using and that other people in biological fields were using started to not quite suit our needs, not quite answer our questions. In biological life sciences the level of maths left them. I started to teach statistics, R and machine learning during my PhD. Manning wanted a book that was not for computer scientists necessarily, but more for people who were an expert in their own area but who could use and benefit from machine learning, who could benefit from understanding and learning machine learning to make predictions and extract meaningful insights from the data that they have.(14:57) – The answer to the question of whether somebody should learn R or Python is yes, people should use either or both. Python would probably have been a more convenient choice for a lot of people for machine learning. Carat or MLR in R, which were kind of an answer to scikit-learn and create this common interface so that you learn how to use that package and then substituting in a variety of different machine learning techniques and algorithms is extremely simple. Tidyverse is a collection of data science packages, a set of packages that are designed to make common data science tasks extremely easy, clean and reproducible.(22:21) – There's basically no reason for Python and R to compete, we can incorporate code from both languages.(24:11) – R has a phenomenal community of people. You need only to tweet a question or ask for opinions, and hashtag our stats and you get a ton of really nice supportive answers back and a huge amount of support on github or stackoverflow. (25:41) – Submitting a package to CRAN, the Comprehensive R Archive Network, is not a difficult process at all, if you write your package well. But writing a package for it to be submitted on to CRAN has to meet certain criteria. The documentation has to be of a certain quality in data in a certain way. The script files have to be laid out and documented in a certain way. So the whole CRAN submission process selects for good quality packages. (27:30) – People that are asking the really important questions, whether to do with business or science or health or whatever, the people that know how to ask and are asking those important questions are the ones that should be able to harness and implement statistics, data science, and machine learning to get those answers. I don't think that machine learning should be the purview only of mathematicians and computer scientists.(28:13) – As long as you teach people how to do things properly, that they have enough of an understanding of how the techniques work and what they do and what they don't do, then, absolutely, we can democratize machine learning. We can absolutely teach people to be able to use these techniques, to extract the answers or make the predictions that they're looking for in their field of expertise.(29:18) – The MLR package, which stands for machine learning in R. It provides a unified interface to a huge number of, not only actual machine learning algorithms, but also processes and functions like missing value, imputation, hyperparameter tuning, validation techniques. Where MLR particularly shines is, It makes it extremely simple to validate your models, MLR works very nicely with parallelization. MLR helps achieve that because you can do some extremely complicated validation pre-processing with very small amounts of code. (34:49) – Caret has functions that you can use to split your data into train test validation sets. And it has the ability for you to perform data pre-processing steps like missing value, imputation and things like that. MLR has become more popular recently. Caret has been the mainstay.(38:15) – Tidy Models are a set of packages that come from the Tidyverse. And in a similar way in which MLR is trying to create a uniform interface to machine learning, Tidy models are packages that are trying to create a unified approach to modeling in general. So that includes, and it's probably more widely used, as linear modeling. (41:53) – I really do think that Machine Learning with R, the tidyverse, and mlr is an excellent book. And it sounds very braggy of me and I don't mean to be, because although I wrote the content, a huge number of people other than me have made the book very good. So I do think that people will learn a lot and get a lot from it. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSChristoph Lütge studied business informatics and philosophy in Braunschweig, Paris, Göttingen and Berlin. He was a visiting scholar at the University of Pittsburgh (1997) and research fellow at the University of California, San Diego (1998). After taking his PhD in philosophy in 1999, Lütge held a position as assistant professor at the Chair for Philosophy and Economics of the University of Munich (LMU) from 1999 to 2007, where he also took his habilitation in 2005. He was acting professor at Witten/Herdecke University (2007-2008) and at Braunschweig University of Technology (2008-2010). Since 2010, he holds the Peter Löscher Chair in Business Ethics at the Technical University of Munich. In 2019, Lütge was appointed director of the new TUM Institute for Ethics in Artificial Intelligence. He has held visiting positions in Venice (2003), Kyoto (2015), Taipei (2015), at Harvard (2019) and the University of Stockholm (2020). In 2020, he was appointed Distinguished Visiting Professor of the University of Tokyo. His main areas of interest are ethics of AI, ethics of digitization, business ethics, foundations of ethics as well as philosophy of the social sciences and economics. His major publications include "Business Ethics: An Economically Informed Perspective" (Oxford University Press, 2021, with Matthias Uhl), "An Introduction to Ethics in Robotics and AI“ (Springer, 2021, with coauthors) and "The Ethics of Competition” (Elgar, 2019; Japanese edition with Keio University Press, 2020).He has been a member of the Ethics Commission on Automated and Connected Driving of the German Federal Ministry of Transport and Digital Infrastructure (2016-17), as well as of the European AI Ethics initiative AI4People (2018-). He has also done consulting work for the Singapore Economic Development Board and the Canadian Transport Commission.Episode Links:  Christoph Lütge’s LinkedIn: https://www.linkedin.com/in/christophluetge/ Christoph Lütge’s Twitter: @chluetge Christoph Lütge’s Website: https://www.gov.tum.de/en/wirtschaftsethik/start/ 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(02:25) –  On the Future Forum we developed the idea of forming a kind of global network of centers for AI ethics. And at the end of this forum, we launched a concrete project, the global AI Consortium, which we are now taking forward in order to form a kind of global alliance of centers working in this field.(04:06) – It's not just an academic thing. It's not just a traditional research Institute where you do research behind closed doors, basically intimate. You have to work with both industries, with civil society and with politics, and that's the only way to take these issues forward. (06:54) – More of these systems are more visible to the public, and that's why there's also this discussion about AI and the ethical as well as governance aspects of it.  Certainly the trend is now, and has been already for years, obviously, the machine learning and deep learning aspect of AI, which some of the more conservative countries still refuse to call real AI. So for a long time, the idea has been that there will be something more robot-like systems that are out there in the world and doing certain things. But  this is the major trend. And of course, the implementation into special vehicles, and probably also in the field of health. I would say these are the most important trends for the near future.(09:33) – AI systems can both speed up a lot of processes, as well as create entirely new ones, or let's say connect data. They will provide a lot of new input for doctors. And so we are, and will be more and more, at a point where we can say, it's not responsible anymore not to use AI.(12:10) – We have these different levels of autonomous, striving automated, highly automated driving and fully automated driving. So what we are witnessing now is a progression on these levels. We need to get beyond that level where it's actually where the company is liable during the time that the car was in control, but not the driver.(15:33) – We need to have robust software which must be able to drive on the difficult, maybe not most extreme conditions, that's if we want to drive under any conditions that will be difficult. And of course, that car must be able to deal with, let's say, rain, with hale, with snow, at least light snow, maybe. And that can pose a number of difficulties, also different ones around the globe.(17:05) – We presented our first guidelines for ethics of AI in late 2018 in the European parliament. And we came up with these five ethical principles for AI. So, which are beneficence-maleficence, justice-autonomy. And while these four are quite standard for ethics, the fifth one is quite interesting: the explainability criteria. Then we presented another paper on AI governance issues just recently last November, this was about how companies and States can interact on deriving rules and governance rules for these systems.(20:48) – There are a few people who have the expertise in ethics actually. I'm one of the few ones in there and it will be quite interesting to see how this process works out, because, ultimately, we will need to develop international standards for these AVs.(23:03) – Ethics is quite a fuzzy term. It has lots of connotations and, for some people, it's about personal morality and that's not really what we mean. We are aiming at standards or guidelines, rules which are not always legal ones, which might be so. So we found it also better to use the term responsible AI. Not just the typical research academic conference, but one where we plan to interact with other stakeholders from industry, from civil society, from politics as well.(24:37) – We invite the abstracts on many areas of AI and ethics in a general sense to visit our webpage to find a lot of potential topics, whether it will be AI in the healthcare sector, AI and the STGs, AI policy, AI and diversity and education, and many others.(25:47) – Engineering curriculum should be enriched with elements from humanities and social sciences, not least of which it will be ethics. But now with a focus on AI, it becomes clearer that working on AI will not be enough to just look at it from a purely technical point of view. It needs to generate the necessary trust. Otherwise people would just not use these systems. And this is something that engineers should be familiar with, engineers and computer scientists, and people from technology.(28:23) – One of the key challenges will be how we manage to some extent, standardize explainability. Every step within the system must be transparent and it must be clear, you must be able to track it down. Of course, there's no way to do that, if you are familiar with the technology. So we need to find some kind of middle way. And there is this research field of explainable AI in computer science, and the challenge will be to implement systems. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Transform your Data Science Projects with these 5 Steps of Design Thinking with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/data-science-design-thinking-658a4f293a1c .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Top 10 Data Science & AI Books of 2020 with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/what-are-the-10-must-read-data-science-and-ai-books-of-2020-36e2c5f0d72f .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSNick Walton is the founder and CEO at Latitude, a software company which develops AI-powered games designed for player freedom and self-expression. Latitude is the creator of AI Dungeon.Episode Links:  Nick Walton’s LinkedIn: https://www.linkedin.com/in/waltonnick/ Nick Walton’s Twitter:  @nickwalton00Nick Walton’s Website: https://github.com/nickwalton 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(03:23) – I was able to make a decent little like AI Dungeon master with it and,  I didn't win anything at that hackathon, but I thought it was cool enough that over the next couple months I continued to work on it and worked on how to deploy it and I made a little web app.(04:53) – GPT-2 released the largest model and I also found a dataset of text adventures so I trained the largest model on text adventures, and that's where it got like much better and so released AI Dungeon II  in December and it was initially just released as a Python code and a Google CoLab notebook, which is just a way to run.(05:45) – We've made a mobile and web app version that you can play and now we just passed 750,000 registered users and so it's been growing pretty fast.(07:54) – The thing I love about hackathons is that you can build things completely in an explorative way, you don't have any pressure or time crunch. If it doesn't work out, you can just say I tried that for a day but it didn't work out and I don't have to keep going with it and you don't really feel bad about not continuing this project.(09:32) – In terms of the competition I didn't have a web playable version by the end of the hackathon cause that took quite a bit more work, especially on the machine learning side. Seeing how much fun people had playing it just around me sparked the inclination to take this to the next step, make a web app and I spent like a hundred hours over the next several weeks doing that and then it went from there.(11:21) – Google CoLab servers, a lot of them were in Asia and Europe and our model was hosted in the U.S. so we were getting international egress bandwidth fees. We were afraid of spending all the money on GPU compute but now actually our GPU compute infrastructure is much cheaper than the cost of downloading all those models for the initial Google CoLab version.(13:44) –  Since we released the co-lab version, a team  started to come together and a guy volunteered to build up the mobile apps. Now we have a team together that does the mobile and the web and the model serving infrastructure on the backend and we're looking at growing that team, but what we really want to do is explore all the awesome directions.(14:49) – With AI Dungeon, you literally have an infinite set of possibilities because anything you can express in text, you can do and that's a completely new idea for a game. There are also technical challenges and we have a really strong team to solve those, but we need to explore and figure out how to resolve those technical challenges. Being able to merge those two in a video game format would be really powerful and that's one of the main things we're working on.(16:48) – In the long term, we're thinking much broader than just like fantasy RPG type genre.  AI Dungeon has this vast knowledge from the vanilla GPT-2, which was trained off 40 gigabytes of text data. We're definitely interested in exploring that broad set for the initial game, the fantasy theme has been really powerful because it taps into getting closer to that D&D field that people are hungry for, but we definitely have larger long-term vision.(19:47) – There's two things that this AI generated content makes really powerful: one is this player freedom where you could potentially, rather than having this preset list of possible options you can make it much more expansive; the other thing is much more dynamic and interesting content. With AI generated content, you can have less developers and less creators and maybe the creators are creating more of the longterm and the overarching themes and then the AI is filling in all these details and helping create this super expansive world.(23:05) – There's a lot of potential in that area and in terms of AI generated content for games, NLP is going to be one of the first ones, just because so there are a couple of things that are powerful about NLP. (25:31) –  You can make surprisingly life-like and Dynamic NPCs. You can create a little bit more of that, but  you can do this with every NPCs and that creates a lot of really interesting  individual emotional connections.(29:03) – I transitioned to more of the robotics and machine learning side of things but in terms of doing things, so one of the issues with AI and Dungeon is it actually has the highest minimum required GPU spec of any game we know of. (31:39) – We have good prototypes that we're working on implementing things like multiplayer where it's turn-based. We've got a lot of interesting ways you can modify the game than some of the next steps. Text to speech could be really interesting.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSEdward Scott is the CEO of ElectrifAi, one of the oldest machine learning product companies in the US serving the Fortune 500 as well as the federal and state sectors. Ed has over 25 years of experience in the technology and private equity sectors building, managing and investing in dozens of high-growth enterprises globally.Ed started his career in the LBO group of Drexel Burnham Lambert and joined the Apollo Investment Fund in 1990. While at Apollo, Ed invested in dozens of companies across multiple industries focusing primarily on the TMT sector, chemicals, transportation and financial services sectors and was on the board of directors for numerous Apollo portfolio companies. Ed was also a partner at the Baker Communications Fund, originating and managing the firm’s two most successful portfolio company investments, both of which have become multi-billion dollar enterprises: Akamai Technologies (NASDAQ:AKAM) and Interxion Holding NV (NASDAQ: INXN). Akamai is the global leader in content distribution and edge computing and Interxion is the largest data center and managed services business in Europe. Ed has held senior-level positions at Napier Park Global Capital and White Oak Global Advisors. Ed graduated from Columbia University with a B.A. in history and earned an MBA from the Harvard Business School with second year honors.Episode Links:  Ed Scott’s LinkedIn: https://www.linkedin.com/in/edward-scott-74354923/ Ed Scott’s Twitter:  @ElectrifaiEd Scott’s Website: https://electrifai.net/ Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(02:30) – ElectrifAI is the United States oldest machine learning company that started off in the procurement area and then pivoted to create the first, fully integrated closed proprietary machine learning platform, everything from all the data ingestion and the transformation to the DQM, to the preparation for the models to the scoring, to the insights and so forth.(02:57) – We transitioned that closed proprietary platform into a fully open platform built on the cloud, built on a common spark computational engine with the use of Kubernetes Docker containers and of course, notebooks.(03:29) – We not only change the entire re-architecting to reengineer the entire technology stack, for our customers, to make it more modern and open and agile. We also shifted from being more of a data science consulting type of company to a fledged world-class machine learning products company. (04:43) – We focus on a certain number of verticals and a certain number of products. Our products focus on Procurement AI, Contracts AI, hidden risks, image AI, customer attention, customer acquisition, retention, and development, which is very important in the healthcare area with regard to patient steerage.(06:17) – Everybody's data is disparate and it's disconnected and it's all over the place. It's on SAP system, Oracle systems, IBM systems, Cerner's systems, Epic systems, Allscripts systems. And there's no way really to get at that data until now. And that truly is one of the core competencies of ElectrifAi.(07:10) – Without  clean data, there's no AI, that's simply the case. And we are seeing it across the world in the most sophisticated enterprise customers. And of course in the hospital and the payer space. (08:57) – If we're going to drive AI and ML into every single part of this business, it has to be done by leadership from the top in the digital world. If you are not embracing digitization in this world, your company's dead. (09:55) – When you look at comprehensive AI or a machine learning program, you really have to understand what your objectives are. What the objectives of the C-suite are. You need leadership and you need definition, clear scoping, project definition. The success of AI and ML really is contingent upon your capability and your competency in the data pipeline.(12:58) –  If you, as the CEO or the CFO of your firm, cannot express a return on investment or return on invested capital from all the money you spent on data lakes and data marks and all the tools companies, you're going to be out of a job. (14:22) – Our areas of focus, our verticals are TMT, healthcare, financial services and the federal space. Principally because we have the machine learning products that dial up the revenue, dial down the cost and dial down the risk. (15:14) – The power of machine learning is using AI and NLP to extract key terms, words, and conditions from contracts to show risks, opportunities, how can you can reduce the number of suppliers that gain leverage with the ones that you actually annoyed, how can you can reduce the suppliers who are not focused on social issues.(17:59) – It's a team effort at ElectrifAi. We talk about our culture, our culture of urgency, our culture of transparency, our culture of disruption, re-invention and self-examination and our culture of teamwork.(18:51) – Data is in our blood, but it's practical data and practical ML, and that's why we go back to getting the data prepared and so forth. We are going to change the way the world works in machine learning. They believe that our suite of practical machine learning products will help that C-suite in a very differentiated way. So it's all done with that team.(21:23) – The world is facing a massive demand and supply shock. And that's going to hurt the technology business and the small companies. And it's going to hurt companies that have tremendous fixed costs and cannot adjust those fixed costs or that risk quick enough.(25:14) – We have an image analytics department that automates annotations and then turns all those pixels into ones and zeros, and in a sense, mimic SQL and is able to search a database to say over the last 50 years, and give all the liver tumors. That is real power for ML and it's spreading into how we do with COVID. We can get that person segregated quickly into care versus them going into the cities and spreading it more. That's a game changer. Our technology is three years out ahead of the market.(30:49) – We haven't seen in a while the collaboration of the world together to attack an issue. We are citizens of the world and we have to solve this problem together and we have to solve it now. And it's a very exciting time.(33:46) – Businesses will adapt and will adjust to the new world of not necessarily conducting business by congregating in the office. But, those that are very adaptable and flexible and purposeful and very customer driven.(37:01) – Our mission is to change the way the world and our customers work in machine learning. Our culture is a culture of urgency, transparency, disruption, re-invention, self-examination. We tell our customers, we'll serve you through ML today. But tomorrow there might be a completely new technology, and we'll have to adapt. And that adaptability is at the heart of who we are. (43:13) – I'm going to say that the Time’s 2020 person of the year is humanity, because we're going to come together as a global family and solve this. AI for the good.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSCyrus Radfar is a long-time programmer and serial entrepreneur. Radfar initially studied computer science and psychology at Georgia Tech. His first entrepreneurial endeavor was with AddThis, where he was the founding engineer, led their analytics products, and managed the creation of the monetization offerings. AddThis pioneered the sharing movement and grew to become the largest sharing platform. It was sold to Oracle in 2016. Since leaving AddThis, Radfar has been testing new products and formally advises entrepreneurs building new companies. He founded V1 to share and scale his existing learning with companies who require new solutions to grow and diversify. Episode Links:  Cyrus Radfar’s LinkedIn: https://www.linkedin.com/in/cyrusradfar/ Cyrus Radfar’s Twitter:  https://twitter.com/cyrusradfar?s=20 Cyrus Radfar’s Website: https://www.v1.co/ 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(02:47) – The future of artificial intelligence is going to drive a huge number of trends. We're going to be building something to either replace us or replace all the things that in the positive sense we don't want to be doing with machine intelligence, artificial intelligence, robotics, etcetera(06:32) – Machines are most likely going to solve business problems. AI in general is going to support and augment us so we can focus more on doing what we love. Augmenting humans with robotics is going to replace a lot of jobs. People are going to do a lot more of what they want to do on the knowledge work side and have removed a lot of work they don't want to do. (08:49) – it's more of a political and socio-economic question of how do you structure a society where you don't necessarily need as many people working or doing the jobs people don't want to do today.(11:08) – Social media didn't exist 10 years ago or went well 15 years ago. So the whole term is new, the whole industry and everyone who claims to be in that industry, those are new jobs, and it was created by a platform. Technologists and business in general, eventually, even if the intentions of the founders may not be good, will end up changing things a lot and constantly creating good new things like social media.(14:50) – Are we going to be more or less human? Are we giving more or less empathy? Are we going to care more or less for each other or we're going to be more or less competitive because of that? I don't know the answer, but the reality is we're going to limp through seeing a generation very soon, like gen Z that has completely been immersed in this thing that we created in garages. (16:58) – We've raised a whole generation to respond to apps more comfortably in a closed setting than they do to other humans who manage them. And it's almost evolutionary that we're almost setting ourselves up for this world where we're more comfortable with our machines.(19:27) – The “always on generation”. We're always being connected, whether it's through Slack or WhatsApp or Line or WeChat or Telegram the apps just go on and on. We are being connected. We're being driven by algorithms to make decisions that maybe we wouldn't choose by ourselves, but maybe it's more efficient and better.(20:23) – We're not moving as fast as we thought we would, but we are accelerating. It is possible that the generations that are born today, our children, could be on Mars.(25:19) – With faster travel and transport, more people will move away from cities. The future is remote for a lot of companies. So it's really important that we consider that it is significantly cheaper for companies, it's better for people to be at home.(28:45) – All my experience with remote workers is that they're way more focused. They're not distracted. There's not as much disruption on day-to-day goals. They can focus and do what they need and then go on with their lives.(35:48) – There are so many people who don't actually have broadband in the U.S. alone. There's people all over the country and in rural areas who do not have broadband, which is unfathomable.(37:11) – It's an unwired world. Some have lived through that transition. The phone, then television, radio, the rise of the internet and whatever wired telecommunications and then unwired communications. It's crazy the perspective that folks have, who are still living. (40:08) – Look out for 5G. We're going to be more seamless with immigrations for real time data. Perhaps, maybe that's through the 5G, or more seamless computer vision, getting to self-driving cars or getting to consumer applications that can see things for you or read text for you, or do it more real time. 5G will get us in that direction.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Most Promising Tech Job of 2020 Is Cybersecurity Analyst with David Yakobovitch.Available for reading on Medium: https://medium.com/swlh/the-fastest-growing-hidden-job-in-2020-is-network-engineering-6034bdf288b .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

AI Update: Coronavirus Vaccine powered by AI, White House Expands AI Budget with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/ai-update-coronavirus-vaccine-powered-by-ai-white-house-expands-ai-budget-8f41d672d940 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How DeepFakes will influence Your Vote in the 2020 US Election with David Yakobovitch. Available for reading on Medium: https://towardsdatascience.com/how-deepfakes-will-influence-your-vote-in-the-2020-us-election-82df3fb38673 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJakub Krcmar is the CEO & Co-founder at Veracity Protocol. Jakub has worked on 200+ web and mobile products as a UX/UI professional, Director, and Head of Product, leading teams for clients like World Press Photo, UNICEF, Amnesty Int. He’s used to leading 10+ teams on complex tasks while setting the vision. Jakub is native in cutting-edge technologies. Prior to Veracity Protocol He co-founded ONEPROVE (now part of VP) and Stellar Bold. He was also a Partner & CPO at ARTSTAQ. Previously, Episode Links:  Jakub Krcmar’s LinkedIn: https://www.linkedin.com/in/jakubkrcmar/ Jakub Krcmar’s Twitter: @jakubkrcmar Jakub Krcmar’s Website: https://www.veracityprotocol.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(01:52) – At Veracity, we developed an algorithm based on computer vision and artificial intelligence, which basically enables any camera, be it a smartphone camera or industrial camera to be able to analyze any object’s physical structure, and create something that we call tamper-proof physical code. Any physical object itself is unique.(02:49) – Using this physical code to basically solve the issues of authenticity of manipulation to help detect any anomalies or tampering and to really protect human lives and brands and national security and allow for a fully automated, digitized, tokenized future world and industry 4.0(04:21) – The upcoming era of blockchain and a lot of companies using blockchain as something to track provenance, for example, is another duplication of how we are using physical certificates of authenticity. Even blockchain itself is a very cool technology of decentralized databases. It cannot secure what is essential, which is the way you connect the physical object itself to any digital entry, be it a database or blockchain. So this is a key technology to not only some issues of today, like the counterfeiting and transparency, but also of tomorrow to enable the optimized industry. (07:34) – Techstars was a milestone,which through their process, pushed us to literally get our stuff together and really be precise about how we want to use this technology, what market we want to target and get everything together.(09:25) – We started from Czech Republic, from the middle of Europe, going to New York, where we have most clients, most engagements. Also the business opportunities, because one of the verticals we are focusing on is semiconductors. Taiwan is the place where most of the semiconductors are produced. And that's why it's also become strategically important globally.(12:28) – We obviously are not focusing only on semiconductors, there's two other areas, collectibles and sports memorabilia is a big one, luxury goods and also security documents like IDs and passports. The overarching team is really like objects or items of high value or high security threat. There's security issues of software angle and hardware angle. And software angles you can always fix. But the hardware angle is unfixable until you actually replace and fix that thing physically.(15:53) – Our position was never to have the role of saying this is authentic and this is not. Our position is to provide a technology where you can protect that painting. And we can always guarantee this is that painting, which has been protected there in that time. We won't be able to tell if this is the original Rothko. That's up to the person who's protecting it. For some reason you may be wanting to even protect the fake. That's really not our position to judge. This really brings you the identity to be sure.(16:55) – We are not really focusing on the art sector anymore. That outward is a market, which is very resistant to innovation, to changes or other people want to keep the status quo. And that's not really a market where we would like to grow a company and innovate.(19:57) – I don't even believe you should be doing the authentication as the customer. I believe that should be the role of the marketplace protecting you and doing this authentication for you.I would much rather engage the level higher and bring technology to marketplaces, to work with brands, to work with manufacturers and to be able to totally mitigate this and solve this issue. I believe it's possible. (24:12) – This is the era of when we start having deep fakes in video and audio, it’s kind of a subject going on. The same thing happens with physical objects. You have super fakes that are really impossible, even by a naked eye, to distinguish the details. You really need to be drawing, training your nets and computer vision to really be able to go down and recognize the difference between the fake and original through optimization and AI, increase the accuracy of the process and be able to scale it.(27:05) – To give you a comparison of what's the level of detail we can work with, what we can really recognize is a sheet of white paper against each other.(29:24) – You can take your phone, snap a picture and know immediately. All this is truly based, not only a lack of building this computation algorithm, but also the data sets and the data, and all this data we had to acquire by ourselves.(31:31) – Every object, the 3D printer prints will be different. Its structure will always be different. It just depends how deep you need to look, how much resolution you need. (34:15) – Right now in the RSA constraints, we’re presenting a solution together with Intel of securing the transparent supply chain for the critical hardware, several motherboards, showing the solution, how we are able to fingerprint individual components, several motherboards, to be able to allow anyone down the supply chain to verify this is the authentic motherboard, what's actually its history. Build the industry 4.0, with automated factories, where components and final products are tokenized and everything is settled on blockchain.(36:54) – Security is definitely moving more into the physical world and getting much more attention there, because, thanks to the democratization of technology these times, 3D printing, the availability of chips, this brings further pressure to create barriers of entry to bad actors in the supply chains, which you really need to counter with more advanced technology. (38:53) – Ever since moving into the cloud with upcoming 5G, everything will be in some instant. The verification down with a smartphone with our technology will be instantaneous.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

New AI Trends in February 2020 with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/new-ai-trends-in-february-2020-66f71d844fb9 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Why You Should be Worried about Identity Theft with David Yakobovitch. Available for reading on Medium: https://medium.com/swlh/why-you-should-be-worried-about-identity-theft-54f58eacb2d2 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Solution to Breast Cancer is finally here: Artificial Intelligence with David Yakobovitch. Available for reading on Medium: https://towardsdatascience.com/the-solution-to-breast-cancer-is-finally-here-artificial-intelligence-56e696792428 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How to Fight the Coronavirus with AI and Data Science with David Yakobovitch. Available for reading on Medium: https://towardsdatascience.com/how-to-fight-the-coronavirus-with-ai-and-data-science-b3b701f8a08a .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

How AI Enables Online Shopping with David Yakobovitch.Available for reading on Medium: https://medium.com/swlh/how-online-shopping-has-deployed-artificial-intelligence-1ae18559e3f8 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Is there any Industry that is using AI more than #healthcare with David Yakobovitch.Available for reading on Medium: https://medium.com/@david.yakobovitch/is-there-any-industry-that-is-using-ai-more-than-healthcare-922ca5a6259 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

AI Transformation and the Digital Age with David Yakobovitch.Available for reading on Medium: https://medium.com/@david.yakobovitch/ai-transformation-and-the-digital-age-d752b4d4c864 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Can Artificial Intelligence Help Answer HR's Toughest Questions with David Yakobovitch.Available for reading on Medium: https://medium.com/@david.yakobovitch/can-artificialintelligence-help-answer-hrs-toughest-questions-83ea3eea913a .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBrett Greenstein is a Senior Vice President and Global Head of Artificial Intelligence at Cognizant. His experience in the Internet of Things, technology consulting, solutions in banking, healthcare, customer service, and retail with organizations include IBM and many Fortune 500 products.   Episode Links:  Brett Greenstein’s LinkedIn: https://www.linkedin.com/in/bretgreenstein/Brett Greenstein’s Twitter:  https://twitter.com/bretgreenstein?s=20Brett Greenstein’s Website: https://www.cognizant.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(02:53) – There are ethical implications of putting people out of work that scares them and there's also the fear that when AI is biased, it can cause damage, it can cause people to not be hired, it can cause things that reflect badly on your brand to be used in business. (03:19) – People have begun to extrapolate their inner fears and transfer into AI and assume that using AI must be ethically dangerous. AI might be able to solve a problem better than not using it, and this has come up increasingly because the accuracy of AI-based systems is consistently better than people in very narrow tasks.(04:17) – Everyone wants to be an AI-first company. And it sounds great. It sounds efficient and powerful and smart. It's really good at some things, but it's also not good at everything. (05:14) – If AI could do everything we could do, we’d let the machines do the work. But in practice, it's usually a very specialized skill set that is fairly narrow, and ultimately we're still responsible for business and commerce and government and family. We can't delegate that to a system.(06:10) – A human being is accountable to other human beings in a way that AI is not, but it would be irresponsible to do certain types of diagnosis and not ask AI if it spotted anything(07:32) – We should manage the exceptions instead of managing the bulk of the work, and recognize where the strengths are.(09:23) – Best-in-class for cars gets you into level three, conditional automation. The world is not really designed for self-driving cars as much as self-driving cars are not designed to fully take advantage of the world we built all of our traffic systems and everything under the assumption that people drive cars, people cross streets, like lanes or bike lanes.(11:01) – In the U.S. there's a backlash in several cities around facial recognition and other things, but as regulations help protect us from privacy, cameras can still help drive enormous efficiency and safety in cities.(14:07) – Just the amount of information and work is so high that actually it induces strain, it induces errors, and induces stress on people. But if you had an AI do all the photos and then you touched up and tweaked and fixed the ones that needed it, you'd get more done with less stress and all of our jobs are filled with those kinds of tasks.(15:42) – There's so many extensions and packages that claim to be AI ready, AI enabled, which they're really using these presets that are performing repetitive tasks over and over. You no longer need the human to do that, but then they could double-check.(16:36) – Like with Facebook and Instagram, that's pretty cool when you're face timing with a friend or you're doing something social, but there's also the bad actors, when someone tries to hack the system. There should be regulations put in place there.(17:53) – Using where you can use AI to pre-filter out the really awful stuff so people don't have to look at it in the content moderation side that's just an ethical thing to do, because it's really unfair to make people look at that stuff it's necessary, but it's awful. (19:57) – Responsible use of data: when AI is used, you should know that it was used and have some ability to have discussion or escalation, if you disagree with it with an outcome, because it will enhance the AI for everybody else once you solve it and you should know that it was generated by an algorithm or a person. (23:03) – As these customer service human interaction systems become better, they will also have a little more transparency and what you can do about it, because if it was an algorithm, if it were an algorithm, it would have told you, it was because of this and this and this, which is then correctable. (24:23) – With these new AI recruiting tools that are beginning to emerge, perhaps we're going to move into a process that better serves humans, but also frees up the hiring manager to work on more challenging tasks. The complement of people owning the HR process and whatever policies, governance, and AI is that actually can tell you a little bit more about why they made the decisions they made is a better combination than purely doing with people who are purely doing AI.(27:27) – Setting policies to know what criteria to look for in candidates. Students are using reverse engineering on their resumes so they have the right buzzwords in there so that an algorithm will pick them. It will help get them to the top of an algorithmic decision that's a whole different world, and it's a really interesting result. (28:28) – Once we give AI a task, it now runs on its own, but in reality, people are still ultimately responsible for every system in a business. You can't really just delegate this, you still are responsible for the policies, quality and bias and all the other things that go into making a system work well.(30:24) – We run an ethical AI council at Cognizant, which is a subset of our corporate responsibility office and it specifically focuses on making sure that for the projects we do we've considered the ethical implications of doing it as well as the ethical implications of not doing it. if you have the ability to save someone's life and you choose not to, that's unethical to walk by someone you could save their life, but don't do something when you're involved in AI.(33:22) – Systems should know that you should be able to set it and define it in some way and at least be informed in that moment when you can't make a decision fast enough, at least having an AI tell you what's going on would be better than having nothing tell you it just guessing.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

When Technology Does Not Live up to the Hype with David Yakobovitch.Available for reading on Medium: https://medium.com/datadriveninvestor/when-technology-does-not-live-up-to-hype-98f7531dcb14 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSJake Porway is a machine learning and technology enthusiast. He is the founder and executive director of DataKind, an organization that brings together leading data scientists with high impact social organizations to better collect, analyze, and visualize data in the service of humanity. Jake was most recently the data scientist in the New York Times R&D lab and remains an active member of the data science community, bringing his technical experience from his past work with groups like NASA, DARPA, Google, and Bell Labs to bear on the social sector. Jake’s work has been featured in leading academic journals and conferences (PAMI, ICCV), the Guardian, and the Stanford Social Innovation Review. He has been honored as a 2011 PopTech Social Innovation Fellow and a 2012 National Geographic Emerging Explorer. He holds a B.S. in Computer Science from Columbia University and an M.S. and Ph.D. in Statistics from UCLA.Episode Links:  Jake Porway’s LinkedIn: https://www.linkedin.com/in/jakeporway/ Jake Porway’s Twitter:  https://twitter.com/jakeporway Jake Porway’s Website: http://www.jakeporway.com Podcast Details: Podcast website: https://www.humainpodcast.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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(04:27) – DataKind is a nonprofit dedicated to using data science and AI explicitly in the service of humanity since there are huge opportunities, not just for businesses to use these algorithms to increase profits or efficiency but also social change organizations.(09:21) – Their goal is to help humans on both sides empowering those who would otherwise work together. Social change organizations could be boosted by technology and tons of compassionate technologists who realized they've got skills, whether it's coding or an analytics or machine learning could be using those skills for those problems.(10:47) – It's all about folks who share a vision of the world being better and technology having a role in it working together. (11:41) – The ethical use of AI in our society needs more guard rails and possibly regulation. To build ethical AI you need to make sure that community members and social activists are involved in the process from design all the way to the oversight of the system.(19:06) – Unethical AI is ethical in the end. There are different systems that are designed to do different things and they will use AI for the goals they have. Companies are designed to grow and get big to make profits. Some of that growth comes at the cost of other social elements that we've come to rely on, hence the tension.(22:31) – AI is an accelerant and there are some systems and working social elements that AI could help with. The trick is finding them and really promoting them as opposed to thinking it's naturally ethical if you're doing it  for “good cause” or that it can solve all of the social human challenges.(24:07) –  We are struggling with setting standards for humane or ethical AI because there's been a large push for ethical AI standards, for computer scientists and AI engineers, machine learning folks to adhere to and that is a very natural step towards standardizing our practices.(25:02) – Everyone seems to have wanted to create their own standard, but more than that, standards are only as good as your ability to enforce them. There is one school of thought that if engineers were trained in ethics or had more ethical frameworks, maybe we wouldn't have some of the outcomes we have in companies today.(27:33) – We're in a little bit of frontier land with any of these standards or ethical codes on how AI should or shouldn't be used, for proper labeling of data sets such that you'll have even racially equitable and gender equitable outcomes. (30:49) – When labels are being used for  predicting recidivism and being used in criminal sentencing there's so many horror stories that actually have real implications on people's lives. Whereas AI and machine learning have worked pretty well in terms of  medical diagnosis from scans, or reverse image search, audio search. (37:00) – One of the things that we are really committed to seeing is a world where we may not have cases of things like gender bias in these technologies, if perhaps more folks who were affected by the technology were involved in the design and oversight of the process. (37:50) – We want to create a space where communities can actually build the AI technologies they want for the social outcomes they need. We're really transforming DataKind trying to move from just doing individual projects to significant social challenges.(42:56) – we're moving into a world where everything's being defined by data. Social good, these predictive positive social outcomes is what we have to focus. Then ethical AI just becomes part of our workflow.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Connected Everything - Smart Sensors, 5G, and Always On Displays with David Yakobovitch. Available for reading on Medium: https://medium.com/swlh/connected-everything-a6980ba7dc43 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Talent Wars In-Demand Skills, Tech Shortage, and Income Share Agreements with David Yakobovitch.Available for reading on Medium: https://medium.com/swlh/talent-wars-silicon-valleys-hiring-secret-450632dd4ca6 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

What Skills New and Seasoned Data Scientists should learn in 2020 with David YakobovitchAvailable for reading on Medium: https://towardsdatascience.com/what-skills-new-and-seasoned-data-scientists-should-learn-in-2020-233876b852fa .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Weekly Update: AI Adoption, AI Regulation, and Applied AI with David Yakobovitch.Available for reading on Medium: https://towardsdatascience.com/ai-tech-debrief-roundup-18ce90dd5eef .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy