HumAIn Podcast

How Space Armies will Define the Artificial Intelligence Race with David YakobovitchAvailable for reading on Medium: https://medium.com/@david.yakobovitch/how-space-armies-will-define-the-artificial-intelligence-race-8f288ac23031 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.About HumAIn Podcast:The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Is AI a Job Killer or a Job Creator with David YakobovitchAvailable for reading on Medium: https://medium.com/datadriveninvestor/is-ai-a-job-killer-or-job-creator-49dc12d8f3b9 .🚀 You could sponsor today's episode. Learn about your ad-choices.💙 Show your support for HumAIn with a monthly membership.📰 Receive subscriber-only content with our newsletter.🧪 Visit us online and learn about our trend reports on technology trends and how to bounce back from COVID-19 unemployment.About HumAIn Podcast:The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The Paradox of Artificial Intelligence: Accountability, Responsibility and Ethics in Artificial Intelligence.Available for reading on Medium: https://medium.com/datadriveninvestor/the-paradox-of-artificial-intelligence-accountability-responsibility-and-ethics-in-artificial-d9b204e731f .🚀 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

In this Flash Briefing, David Yakobovitch shares his insights Why Amazon's Return to NYC is Good for All New Yorkers.🚀 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 | RSSKaren Bhatia is the Senior Vice President at the New York City Economic Development Corporation, leading Creative and Applied Tech strategies and initiatives to promote economic development and entrepreneurship throughout the city. Karen is also an attorney, entrepreneur and startup advisor. She was the principal of her own law firm advising tech startups on corporate issues, financing and overall business strategy. As an entrepreneur, Karen founded ActionCam, an educational platform explaining public policy issues and providing resources for people to take action. Karen also founded and is President of Stanford Startups NY, a business network of over 650 Stanford entrepreneurs and investors in the area. She is also on the Board of Trustees of Mott Hall, a middle school in the Bronx. Karen has a B.A. from Stanford University, a Master's degree in Public Policy from Harvard's Kennedy School of Government and a J.D. from George Washington University Law School.Episode Links:  Karen Bhatia’s LinkedIn: https://www.linkedin.com/in/karenbhatia/ Karen Bhatia’s Twitter:  https://twitter.com/karenbhatia Karen Bhatia’s Website: 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(03:25) –New York City Economic Development Corporation, NYCEDC, is responsible for driving and shaping economic growth throughout the five boroughs as the city's economic engine through real estate used for strategic development, building infrastructure, bringing together public and the private sector academia for all and investments in tech.(05:24) –Some reasons why New York is such an attractive place for technology: NYC access to capital, extremely diverse industries, top-notch educational facilities and organizations, an extremely vibrant startup community and the largest and most diverse workforce in the country.(09:58) – All of the industries now integrate technology and need a workforce that's well-versed in technology too. It's not just technology that's growing in New York, but also the applications that industries are leveraging there.(11:24) – Some of the new centers that have been launched in the last five years: Varick Street Incubator, The Cornell and the Technion relationship, the Data Science Institute in Columbia University, NYU's CUS program, the Urban Tech Hub, the Brooklyn Navy Yard, as well as a program at Grand Central Tech now called The Company, Future Works, The Grid, New York City Blockchain Week in partnership with CoinDesk, New York City Blockchain Resource Center and a Virtual Reality and Augmented Reality lab also located at the Brooklyn Navy Yard.(19:36) – How we ensure that New York stays at the forefront of innovation is the most critical component of all of this. There's a workforce development component of training for VR and AR technologies as well. Our strategy for technology is to think about how we ensure that tech growth in New York is equitable and inclusive. (23:30) – The New York City Center for Responsible AI, an applied research lab focused on real pilots, real applications of AI that are being developed in particular industries or in the public sector.(26:45) – New York City Center for Responsible AI is intended to come away with practical solutions for people as they're developing AI. The second component is to think about access to data. The third part is training.(33:04) – The Fourth Industrial Revolution and what's coming up, it's about how we ensure that everybody has access to opportunities, that everybody is able to maximize and realize their potential as well. The second aspect is the future of work. (39:39) – Ultimately it comes down to people first and ensuring that whatever it is that we're working on has an ethical component and is actually used for purposes that we believe in. To ensure that tech is taken to the next level that it's responsible and that it's inclusive 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 | RSSChris Van Pelt joined Weights & Biases as Co-Founder in 2017.Chris co-founded CrowdFlower. He was previously a technical product manager at Powerset, Inc., a natural language search technology company later acquired by Microsoft. Chris has worked as a studio artist, computer scientist, and web engineer, and pours his diverse background into his role as Chief Technology Officer. He combines deep design insight coding abilities that enables him to produce anything, sometimes within minutes. Chris studied both art and computer science at Hope College.Episode Links:  Chris Van Pelt’s LinkedIn: https://www.linkedin.com/in/chrisvanpelt/ Chris Van Pelt’s Twitter:  @vanpelt Chris Van Pelt’s Website: https://wandb.ai/site 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(01:55) –Enabling AI is a paradigm shift in software development. It's going to change the way that software is getting written. (02:32) – Enabling AI by opening up to the community through “benchmarks”, which are  mini Kaggle competitions, oftentimes focused around social good or something to make positive change in the world.(02:54) – Drought Watch exemplifies one of these benchmarks. It's taking satellite imagery of various drought prone regions in the world, as a call to folks in the machine learning community to create an algorithm to predict drought conditions before they happen so that we can take appropriate action and ensure that the impact on humanity is minimal.(05:58) – Developer tools for machine learning show two different approaches in the marketplace: data science as a service from data ingestion and transformation to training of models to actually deploying those models. Weights & Biases tries to create an entire platform as a service focusing on the training and experimentation around creating models. (07:59) – Figure Eight can give companies data in a highly scalable, efficient and accurate manner. Weights & Biases is a tool intended to build a model. But first you'd need to label the data. FigureEight calls it a “Human in the loop” who targets examples that maybe the model didn't do well to go back through a labeling pipeline and get labels on to further improve the model as it is being retrained. (09:56) – Companies like Google have spent tens of millions of dollars, hundreds of years of compute and processing power on working on data sets and labeling data to get it to a good enough steady state that now can outperform a human and still have Humans in the loop. It is a core aspect of any real-world mature machine learning application.(12:38) – The tooling in the space of deep learning was pretty lacking. Weights & Biases was first trying to address this issue of keeping track of what you had done and then hopefully better enabling teams to reproduce any results that had been obtained in the past. (16:13) – We're at least a few years out before we see any meaningful usage of technology, before getting autonomous.(18:42) – Computer vision started the hype around deep learning a few years back. And it's been really exciting to see the advances in natural language processing over the last couple of years. Image captioning merges both worlds.(22:50) – We are going to continue to need humans for cognitively challenging tasks such as authentication, fingerprinting and spoofing. Any time there's some underlying pattern in your data that is not getting after the core of what you're trying to predict, but instead, the systematic of something else in your data collection process, that is bias.(25:00) – Reducing bias by trying to understand data sets. In the initial training data creation and curation process, pull all sorts of statistics over various axes.And once you've created a model, measure how the outputs of that model are performing across an evaluation data center, some set of data. (27:42) – As we create deep learning models with tens of thousands or millions of parameters, it becomes really difficult to explain why any given output was chosen or what their thought process was. (29:15) – Reinforcement learning is definitely more on the frontier of ML. Some companies use Weight and Biases’ RL at least in an experimental context. (31:11) – Research trends include unsupervised machine learning use cases being able to take data that hasn't been labeled by any human and actually surface or unearth patterns simply by looking at all the data.(32:30) – Data sets are going to continue to become larger and computes is going to become less constrained. It's all about the custom hardware. Many startups are trying to make hardware chips that can do all of this matrix math really quickly and highly parallelized. Those are going to be continued innovation, and likely some big step gains as the market matures.(37:46) – Using Weights and Biases tools will help you unearth any underlying bias or issue with your model and enable you to debug it quickly. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Armen Kherlopian, Chief Science Officer at Genpact, discusses AI for social good, revenue generation in industries like ride-sharing, advancements in customer service technology, evolution toward level 5 autonomous vehicles, and integration of unstructured data in enterprise data science.

Chris Butler, Chief Product Architect at IPsoft, discusses human-centered design in AI, ethical hacking, and home automation. The conversation highlights the importance of inclusivity, ethical decision-making, and trust in autonomous systems. They explore the potential of Raspberry Pi for machine learning and the challenges of integrating AI in home environments.

Armen Berjikly, an entrepreneur focusing on AI with emotion and empathy, discusses the ethics of AI in leadership. Topics include the importance of ethical considerations in AI, diverse teams leveraging data, Ultimate Software's people-centric culture, and the significance of AI in decision-making processes.

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSXena Ugrinsky has spent over the last 20 years of her career in technology enabled transformation. The last eight years of those have been focused on the application of data science for corporate performance management. She has a new book titled “Enterprise AI-Your Field Guide to the New Business Normal”.Episode Links:  Xena Ugrinsky’s LinkedIn: https://www.linkedin.com/in/xenaugrinsky/ Xena Ugrinsky’s Twitter:  https://twitter.com/QueenOfDataTech?s=20 Xena Ugrinsky’s Website: https://genrexconsulting.com/                                              https://www.pilotwaveholdings.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(02:39) –At first technology was process optimization. Then there were better individual tools for doing analysis. So there you have spreadsheets. Analytics began to enable in the second phase the ability to do things like driver based forecasting trending analysis. And the third phase and the one that we find ourselves in today is the beginning of applying truly modern mathematical methods in the form of data science to technology(06:41) – Technology isn't the hurdle anymore, it's people, it's process, it's culture, it's organizational structure. (08:23) – Spreadsheets are too deeply ingrained in how business works (10:58) – Siloing of functions was causing a bottleneck of data that prevented executive teams from having the right information at the right time at their fingertips to make a decision(13:15) – Analytics application proliferation have become more accessible to the business community, then the IT organization completely loses control of their tech portfolio and managing costs(16:56) – The things that have to change in an organization to become an intelligent enterprise don't involve technology at all. You have to consider the people and a culture of inclusiveness that moves away from information as power to empowering everyone with the information(20:09) – Our traditional organizational structures need to be rethought. And it starts at the top with re configuring the responsibilities of the C-suite(23:15) – AI as a whole is not going to be the new spreadsheet, AI as a whole describes the transformation of applied data science(24:23) – Organizations fall into three categories: organizations built up and architected as a data company, traditional organizations that have been early adopters to solve something or to embed it in their product, and the ones that don’t know what is the right way to get started in data science(32:43) – Organic transformation: when you change people, you change the entire organization(34:00) – The winners in this race will be transforming so that their organizations are truly data-driven. If you miss this boat, you won't exist as a companyAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy