HumAIn Podcast

[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