HumAIn Podcast

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

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSBeth Partridge is the CEO, Founder and Chief Data Scientist of Milk+Honey, a company that  creates and supports an environment in which data scientists and business professionals can learn from one another, develop common understandings and goals, and advance both business and the human experience. Beth brings nearly 30 years of executive-level experience in manufacturing, product engineering, quality control, technical support and operations. Her formal training includes a BS in Electrical Engineering, and a Master of Information and Data Science from UC Berkeley. Episode Links:  Beth Partridge’s LinkedIn: https://www.linkedin.com/in/beth-partridge-b382673/ Beth Partridge’s Twitter:  https://twitter.com/bretgreenstein?s=20 Beth Partridge’s Website: https://milkandhoney.ai/ Podcast Details: Podcast website: https://www.humainpodcast.com/ Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:  – Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators   – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/  – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here’s the timestamps for the episode: (00:00) – Introduction(03:16) – Milk+Honey helps bridge the gap between business and data science for the rest of the world. There's confusion starting with job titles, how to organize teams too, really what data science means in terms of organizational structure requirements and cultural change requirements. (04:47) – Milk+Honey has created their own internal, very detailed profiling tool. They cross-reference candidates’ toolset and the roles that they say they do on their projects and the whole package in order to really figure out who's who.(05:37) – There's a complete lack of understanding about who's going to do what. You can have the best data scientists in the whole planet and the most committed C-suite willing to put whatever resources they have into making the transition to adopting enterprise AI. And if you don't have somebody in the middle, then it's still not going to work.(07:10) – Most companies don't even have data science teams. Many have tried, most are trying at a project level, but data science takes cross-functional teams, commitment from the top and the cultural stuff.(08:46) – If somebody has enough confidence and understanding of the business and confidence in the models themselves, then as you get more data, the right data, move to a different kind of model and the confidence is constantly growing, but there's not that bridge in between.(10:05) – The Data Strategist: somebody that understands the business, but then understands machine learning enough to understand the different types of approaches and what it means in terms of risk and accuracy.(13:25) – We need people that understand the business and understand machine learning enough to make the connections and to really be that catalyst. And then we need to create coursework in serious applications of machine learning and business. (15:34) – The emergence of segments such as the term “data engineering” is starting to stick. But the more catalyst role of applied data science is still missing. It hasn't really been broadly recognized and we need to find a way to describe what it is and label it.(17:00) – There's some debate about the certification programs and the bootcamp programs and how effective those are. You really do need to have some understanding of business in order to effectively do the job.(19:25) – The traditional question of make versus buy: you can't take advantage of buying software unless you have somebody that's doing the strategic plan that understands those different levels of expertise.(19:57) – 80% of building a machine learning model is data wrangling. And there's such an opportunity to bring in young data scientists to assist with. Stretch machine learning resources further while training younger data scientists with practical experience. (21:59) – ML productivity tools help make easy, quick and dirty feasibility analysis. You don't get a finished model, but you figure out how to approach it algorithmically.(23:13) – Check the for cultural holders, figure how you're going to implement it and sit down and understand what resources are necessary for a data science team to be successful. There has to be the business domain expertise, the machine learning expertise and the data engineering expertise. (29:32) – Get the education, get the training, get solid on at least your machine learning basics, and then find a job at a company that's next to data science. (33:29) – Python is the machine learning language of choice for sure.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Special Edition: Coronavirus Predicted Early by AI Company that tracked SARS and EbolaQuarantines Expand, Flight Suspensions between China and United States, Surgical Masks Sell-out🚀 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