Podcast appearances and mentions of eric daimler

In this episode of Alter Everything, we sit down with Eric Daimler, CEO and co-founder of Conexus, and the first AI advisor to the White House under President Obama. Eric explores how AI-driven data consolidation is transforming industries, the critical role of neuro-symbolic AI, and the evolving landscape of AI regulation. He shares insights on AI's impact across sectors like healthcare and defense, highlighting the importance of inclusive discussions on AI safety and governance. Discover how responsible AI implementation can drive innovation while ensuring ethical considerations remain at the forefront.Panelists:Eric Daimler, Chair, CEO & Co-Founder @ Conexus - LinkedInMegan Bowers, Sr. Content Manager @ Alteryx - @MeganBowers, LinkedInShow notes:SB 1047: Safe and Secure Innovation for Frontier Artificial Intelligence Models Act.Neuro-symbolic AIUber Data Consolidation Interested in sharing your feedback with the Alter Everything team? Take our feedback survey here!This episode was produced by Megan Bowers, Mike Cusic, and Matt Rotundo. Special thanks to Andy Uttley for the theme music and Mike Cusic for the for our album artwork.

The future of AI in business? It's already arrived.Eric Daimler, a titan in the AI space with over 20 years of experience, joins Peer Effect to show you why. With a background that spans advising the White House, co-founding over 6 tech startups, and leading Conexus AI, Eric brings some incredible insights into the transformative power of AI.Together, we dive into: • The importance of timing and adaptation, and why philosophies like "Blitz Scaling" and "Failing Fast" may not always apply to your business. • How getting specific will help your business avoid inefficiencies, enhance collaboration, and leverage AI technologies more effectively.• The value of learning from a variety of real-life experiences and being selective about the advice that founders should incorporate into their business.Discover more about Eric Daimler's work at Conexus AI or follow him on LinkedIn for more exciting insights.More from James: Connect with James on LinkedIn or at peer-effect.com

Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can't even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like.Guest BioDr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society's Biggest Problems.Highlights(00:00) Meet Dr. Eric Daimler(01:46) Eric's role in the Obama Administration(06:32) Challenges in government data integration(10:31) The importance of technical expertise in policy(16:06) Founding Connexus AI(18:09) Understanding category theory(20:51) Applications of Conexus AI(27:16) The future of AI: safe and symbolic(38:35) Insights from Eric's upcoming book(47:49) Advice for data scientists and final thoughtsLinksConnect with Eric on LinkedInConexus AI websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Genevieve Hayes Consulting Episode 43: Shaping the Future of AI Two years ago, no one could imagine the impact generative AI would have on our world, and most of us can't even begin to imagine the impact the next generation of AI will have on our world two years from now. The only thing that is certain is uncertainty.But that uncertainty brings with it great opportunities and choices. We can choose to sit back and let the future of AI play out in front of us or engage with this new technology and shape the future of AI and the world as we know it.In this episode, Dr Eric Daimler joins Dr Genevieve Hayes to discuss his extraordinary work in shaping the future of AI and what that future might look like. Guest Bio Dr. Eric Daimler is the Chair, CEO and Co-Founder of Conexus AI and has previously co-founded five other companies in the technology space. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. He is also the author of the upcoming book The Future is Formal: The Roadmap for Using Technology to Solve Society's Biggest Problems. Highlights (00:00) Meet Dr. Eric Daimler(01:46) Eric’s role in the Obama Administration(06:32) Challenges in government data integration(10:31) The importance of technical expertise in policy(16:06) Founding Connexus AI(18:09) Understanding category theory(20:51) Applications of Conexus AI(27:16) The future of AI: safe and symbolic(38:35) Insights from Eric’s upcoming book(47:49) Advice for data scientists and final thoughts Links Connect with Eric on LinkedInConexus AI website Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE The post Episode 43: Shaping the Future of AI first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Dr. Eric Daimler is a leading authority in robotics and artificial intelligence with over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. He served as a Presidential Innovation Fellow for AI and Robotics under the Obama Administration, driving U.S. leadership in AI research and commercialization. Eric has founded and led several pioneering tech companies and currently serves on the boards of WelWaze Medical and Petuum. His latest venture, Conexus, addresses the critical issue of data deluge in information technology. With a career spanning business, academia, and policy, Eric offers a unique perspective on shaping the future of AI for societal benefit.  00:00 Snippet01:09 Our Guest05:40 AI is much more than Machine Learning10:57 Lisp and data30:54 Conexus 32:53 Type Theory and Quantum compiling34:44 The government's role in AI39:14 Connecting with Eric ------------------------------------------------------------------ To learn more about Eric visit https://www.linkedin.com/in/ericdaimler/ To learn more about Dark Rhiino Security visit https://www.darkrhiinosecurity.com ------------------------------------------------------------------ SOCIAL MEDIA: Stay connected with us on our social media pages where we'll give you snippets, alerts for new podcasts, and even behind the scenes of our studio! Instagram: @securityconfidential and @Darkrhiinosecurity Facebook: @Dark-Rhiino-Security-Inc Twitter: @darkrhiinosec LinkedIn: @dark-rhiino-security Youtube: @DarkRhiinoSecurity ​

Eric Daimler is the cofounder and CEO of Conexus AI, a data management platform that provides composable and machine-verifiable data integration. He was previously an assistant dean and assistant professor at Carnegie Mellon University. He was the founding partner of Hg Analytics and managing director at Skilled Science. He was also the White House Presidential Innovation Fellow for Machine Intelligence and Robotics. Eric's favorite book: ReCulturing (Author: Melissa Daimler) (00:00) Understanding Symbolic AI(02:42) Symbolic AI mirrors biological intelligence(06:01) Category Theory(08:42) Comparing Symbolic AI and Probabilistic AI(11:22) Symbolic Generative AI(14:19) Implementing Symbolic AI(18:25) Symbolic Reasoning(21:24) Explainability(24:39) Neuro Symbolic AI(26:41) The Future of Symbolic AI(30:43) Rapid Fire Round--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-91047b19 Twitter: https://twitter.com/prateekvjoshi 

The implications of Artificial Intelligence (AI) could be profound, wonderful, and maybe terrifying. Eric Daimler -- former AI advisor to President Obama -- helps us sort through the good, the bad, and whether the AI apocalypse is actually a risk (hint: yes, sort of). 01:36 The Real Concerns and Misunderstandings of AI 08:04 The Surprising Origins and Current Challenges of Cybersecurity 13:50 The Ubiquity of AI: From Everyday Anxieties to Real-World Applications 17:32 Flipping the Script: The Positive Side of AI 23:47 The Power of Data in Government and Business 26:37 The Future of AI 31:28 Debating the AI Apocalypse 38:52 Empowering Individuals in the Age of AI

Welcome to Step It Up Entrepreneur podcast, where we delve into the intersection of artificial intelligence, critical infrastructure, and societal impact. I'm your host, Tomas Keenan, and in today's episode, we have the privilege of speaking with Eric Daimler, a seasoned expert with a unique blend of experience in AI and politics. Join us as Eric takes us on a journey through the evolution of AI, from its roots in generative symbolic systems to its current probabilistic applications. We'll explore the profound implications of AI on critical infrastructure, touching on topics such as job automation and the imperative role of societal feedback in shaping the trajectory of technological advancement. So, grab your headphones and join us as we embark on this enlightening exploration of AI, critical infrastructure, and the pivotal role of individuals in shaping our technological future. Welcome to Step It Up Entrepreneur!   Find more about Eric https://www.conexus.com/ or his socials! LinkedIn, Instagram, Twitter, Facebook and Podcast.   https://www.linkedin.com/in/ericdaimler/ https://www.instagram.com/ericdaimler/ https://www.twitter.com/ead?lang=en https://www.facebook.com/ericdaimler/   https://www.kitcaster.com/eric-daimler/  

Eric Daimler, CEO of Conexusdeep dives into the realms of AI, autonomous technologies, and data security. As a former Presidential Innovation Fellow under the Obama administration, Daimler shares his insights on AI's potential, its implications for data protection, and the revolutionary work of Conexus in solving the data deluge challenge. With discussions ranging from the basics of AI and its various applications to the nuances of autonomous driving and the strategic implementation of AI in businesses both large and small, this episode is a rich mixture of technical knowledge, practical advice, and visionary perspectives.

Today, we have Eric Daimler, the CEO and co-founder of Conexus AI. Conexus AI serves as a hybrid generative AI platform, facilitating reliable and rapid digital modernization, empowering enterprises to seamlessly migrate, integrate, and transform their IT systems. In this episode, we delve into the utilization of category algebra to implement a domain-driven approach to interoperability. This approach focuses on computing the optimal data model rather than relying on manual design. Additionally, we explore the common issue of architects misunderstanding the practical structure of databases, leading to the failure of IT programs, as opposed to adhering to their originally intended structures. Key Discussion Points: What key concepts, such as data mesh and strategies, should companies consider when building the right architecture to effectively leverage their data assets? How does Conexus AI help companies facing decentralized data challenges? What does the before-and-after scenario look like in terms of data usage and outcomes? To learn more about our guest, you can find him at: Website: https://conexus.com/ LinkedIn: https://www.linkedin.com/in/ericdaimler/  

Do you want to learn how to use AI to solve your complex data issues?  That's what I'm talking about today with Eric Daimler. Eric is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor.  He is the CEO and Co-Founder of Conexus, which provides solutions that analyze and integrate large amounts of data from multiple sources. Eric is a frequent speaker, lecturer, and commentator.  He works to empower communities and citizens to leverage AI for a more sustainable, secure, and prosperous future. Eric studied at Stanford University, the University of Washington-Seattle, and Carnegie Mellon University, where he earned his PhD in its School of Computer Science. “Every company is going to become an Artificial Intelligence company.” – Eric Daimler Today on the Tech Leader Talk podcast: - Why Artificial Intelligence is the economic engine of the future - The important relationship between Data Infrastructure and AI - Policies and regulation related to AI - Tasks that are best handled by AI today - What's coming next for AI Resources Book:  ReCulturing by Melissa Daimler - https://www.amazon.com/ReCulturing-Company-Culture-Connect-Strategy/dp/1264278608 Connect with Eric Daimler: Website: https://conexus.com/ LinkedIn: https://www.linkedin.com/in/ericdaimler/ Thanks for listening! Be sure to get your free copy of Steve's latest book, Cracking the Patent Code, and discover his proven system for identifying and protecting your most valuable inventions. Get the book at https://stevesponseller.com/book.

This installment of The Axis Effect features Eric Daimler, CEO and Co-Founder of Conexus AI Inc. As a data integration company, Conexus AI hybridizes different forms of AI to create comprehensive models for clients, with one of their most notable examples being Uber. Knowing that data analytics are more important than ever, Daimler and his team act as leaders in algebraic data integration to help companies make sense of information and understand how to analyze and manage risk in the most cost-effective way possible. Daimler discusses AI's place in the modern landscape of workforces, how his company creates effective models, and his time working at the White House in the Office of Science and Technology Policy.  To learn more, tune in to “Living in the Intersection of Data & Time.”

In a thought-provoking episode, I sit down with Dr. Eric Daimler, an eminent authority on artificial intelligence and robotics with over two decades of multifaceted experience. As a Presidential Innovation Fellow for AI and Robotics under the Obama Administration and the CEO of Conexus, Dr. Daimler offers a unique vantage point on the intersection of policy, innovation, and entrepreneurship in the ever-evolving AI landscape. The conversation kicks off with an exploration into Eric's role as the first AI authority in the Obama White House. They delve into the strategic importance of having technology expertise within the governmental structure, examining how such expertise can shape public policy and drive national initiatives. From the macro to the micro, the conversation shifts towards the mounting challenges of data management in AI implementations. With his current venture, Conexus, Dr. Daimler aims to revolutionize data integration and migration through a category-theory-based platform, CQL. The discussion takes a deep dive into the complexities of managing data deluge and the role of category theory in simplifying this monumental task. The discourse then moves into the social and ethical dimensions of AI and robotics. Eric and Neil ponder on the responsibility of communities and citizens in shaping the future of these technologies. They stress the necessity for a collective approach towards understanding the ethical, societal, and economic impacts of AI. As the conversation advances, we discuss the prospects and challenges for AI in automating vocational IT work and synthesizing various tools' actions. They underscore the transformative potential of AI in diverse sectors, including supply chain management, organ donation, and drug discovery. Dr. Daimler brings an unparalleled blend of academic rigor, policy insight, and entrepreneurial spirit to the episode. This is a must-listen for anyone interested in the multifaceted aspects of AI, from data management to ethical considerations and policy implications.

Eric Daimler is the founder and CEO of Conexus, a groundbreaking solution for what is perhaps today's biggest information technology problem: data deluge. Eric is leading the development of CQL, a patent-pending platform founded upon category theory — a revolution in mathematics — to help companies manage the overwhelming and rapidly growing challenge of data integration and migration. In addition, Eric has over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of the President. He was the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics.   Advertiser:  https://timezest.com/mspradio/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/ Support the show on Patreon:  https://patreon.com/mspradio/ Want our stuff?  Cool Merch?  Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on: Facebook: https://www.facebook.com/mspradionews/ Twitter: https://twitter.com/mspradionews/ Instagram: https://www.instagram.com/mspradio/ LinkedIn: https://www.linkedin.com/company/28908079/

This and all episodes at: https://aiandyou.net/ .   Feeling inundated with data? If you're running a business, that's no joke, and it's getting worse. Helping people dig through a mountain of data is Eric Daimler, founder and CEO of Conexus. He has over 20 years of experience as an entrepreneur, investor, technologist, and policymaker where he served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of the President. He was the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI and robotics.  We had a freewheeling, thought-provoking discussion about regulation, business, and state of the art AI. In the conclusion of our conversation, Eric helps us understand how a business should think about and interface with today's AI to leverage it successfully. All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.        

This and all episodes at: https://aiandyou.net/ .   Feeling inundated with data? If you're running a business, that's no joke, and it's getting worse. Helping people dig through a mountain of data is Eric Daimler, founder and CEO of Conexus. He has over 20 years of experience as an entrepreneur, investor, technologist, and policymaker where he served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of the President. He was the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI and robotics.  We had a freewheeling, thought-provoking discussion about regulation, business, and state of the art AI. In this first part of our conversation, we touch on everything from self-driving cars to ChatGPT and China. And category theory as the solution to data deluge. All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.        

Eric Daimler is the chair, CEO and co-founder of Conexus AI. He also sits on numerous boards and is a leading expert in the field of robotics and AI. He joins Ryan to talk about the revolutionary things Conexus are doing in the world of AI, how you can set up AI in your own company and how he thinks AI will create endless possibilities for humanity and the world we live in. KEY TAKEAWAYS Conexus was originally bootstrapped, funding its MVP and explorations before attracting funding to hire staff to turn themselves into a scalable product and business Conexus integrates data models, allowing for collaboration between teams. AI and Robotics are simply a system, they are something that senses and acts, learning from the experience. Good AI can create a seamless customer experience by solving universal problems, particularly with databases and systems. For the future, Conexus is aiming to focus on product-led growth with its offering. Focusing on automation first is how Conexus plan to grow without having to expand its team. BEST MOMENTS “AI, robotics, learning algorithms can be thought of as a system” “It's not because of security, it's because your systems suck, they don't talk to one another” “It's super useful for everyday business and the operation of complex systems, especially when lives are at stake” “We're lucky to be the leaders in the expression of category theory for the integrity of the integration database” Do You Want The Closing Secrets That Helped Close Over $125 Million in New Business for Free?"  Grab them HERE: https://www.whalesellingsystem.com/closingsecrets Ryan Staley Founder and CEO Whale Boss 312-848-7443 ryan@whalesellingsystem.com www.ryanstaley.io  EPISODE RESOURCES ABOUT THE SHOW How do you grow like a VC-backed company without taking on investors? Do you want to create a lifestyle business, a performance business or an empire? How do you scale to an exit without losing your freedom?Join the host Ryan Staley every Monday and Wednesday for conversations with the brightest and best Founders, CEO and Entrepreneurs to crack the code on repeatable revenue growth, leadership, lifestyle freedom and mindset.This show has featured Startup and Billion Dollar Founders, Best Selling Authors, and the World's Top Sales and Marketing Experts like Terry Jones (Founder of Travelocity and Chairman of Kayak), Andrew Gazdecki (Founder of Micro Acquire), Harpal Sambhi (Founder of Magical with a previous exit to Linkedin) and many more. This is where Scaling and Sales are made simple in 25 minutes or less.Saas, Saas growth, Scale, Business Growth, B2b Saas, Saas Sales, Enterprise Saas, Business growth strategy, founder, ceo: https://www.whalesellingsystem.com/closingsecretsSee omnystudio.com/listener for privacy information.

Eric Daimler is a six-time entrepreneur and Founder and CEO of Conexus. The company helps organizations exchange data, even under the most complex of circumstances.Eric brings extensive knowledge of Artificial Intelligence (AI) to the show. He served as a former Presidential Innovation Fellow for Artificial Intelligence (AI) and Robotics under the Obama administration. Now, as the head of a company that specializes in AI deployment and data usage.In our conversation, Eric takes us through the world of AI, from its development over the past 30 years to the dramatic leaps forward.We discuss Conexus and how they are helping organizations harness overwhelming amounts of data.Eric explains why unassuming and unsexy business ventures that offer the most opportunity for growth and profit.We delve into the technical aspects of ChatGPT, the innovations that made this natural language processing tool possible. We also examine the fears that many have around AI and the impact these tools will have on society.Eric's enthusiasm and his gift for simplifying these complex topics make for an informative episode. You won't need a tin foil hat for this fascinating dive into the world of AI!Check out some of our most popular episodes:Chicken S#!it CEOs w. Mogens SmedLeadership Lessons from Louis Vuitton, Samsonite, and Now, EVCP Growth EquityCanada's Best Venture Partner w. Bruce CroxonStay in the know and follow along:Connect with our host, Cory Cleveland on LinkedInVisit The Insider's Guide to Finance WebsiteFollow us on LinkedInSubscribe to our YouTube channelSubscribe to The Knowledge Bank Letter - a periodic letter of actionable insights, interviews, and quality curations

Dr. Eric Daimler is a leading authority in robotics and artificial intelligence with over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. Eric served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. Highlights of the episode: · AI: the tech marvel that senses, plans, acts, and learns. · Will AI's bias shape the way we think and act? · Automation is used for undesirable jobs. · Have we reached a point of no return with AI? · No longer underhyped: AI is finally getting the attention it deserves. · Collaborative AIs are knocking on the door of the future. Additional resources: · Conexus · Welwaze Medical · Petuum · Connect with Eric Daimler ------------------------------------------------------------------------------------------------------------------------------- ------------------------------------------------------------------------------------------------------------------------------- Subscribe to the show: · Apple Podcasts · EricRMueller.com · Spotify --- Support this podcast: https://anchor.fm/ericmuellershow/support

Show Notes(02:15) Eric reflected on his early interest in computer science and his decision to study at Carnegie Mellon University in the early 90s.(05:40) Eric recalled his academic and overall college experience, emphasizing the importance of the people he was surrounded with.(08:22) Eric talked about his time working as a quant analyst early in his career, the moment he encountered the birth of the Mosaic browser, and his decision to join the tech industry.(13:01) Eric imparted wisdom learned from venture investing during the dot-com boom.(18:02) Eric talked about the next phase of his academic career - earning a Ph.D. in Computer Science from Carnegie Mellon and dropping out of a Ph.D. program at Stanford.(21:06) Eric discussed his academic research on Computational Economics for corporate malfeasance during his time as a Ph.D. student.(27:39) Eric shared different initiatives he worked on with Carnegie Mellon University - serving as the Assistant Dean and Assistant Professor of Software Engineering, launching CMU's Silicon Valley Campus, and founding CMU's Entrepreneurial Management program.(31:54) Eric described his journey in founding Hg Analytics, a hedge fund focused on statistical arbitrage, alongside other CMU's Computer Science PhDs.(37:36) Eric revisited his passion for AI and robotics, which eventually led to serving as a Presidential Innovation Fellow during the Obama Administration with the White House Office of Science and Technology Policy.(42:54) Eric shared his perspective on the role of AI in geopolitics and highlighted the challenges with data integration.(47:29) Eric explained his company Conexus, which develops a technology spin-off from MIT's Mathematics department using a branch of math called Category Theory.(50:55) Eric went over a customer case study that uses Conexus's solution to guarantee the semantics of data integrity during data transformation.(54:20) Eric showed his enthusiasm for the concept of data relationships.(56:59) Eric provided a sneak peek of his forthcoming book, "The Coming Composability: The roadmap for using technology to solve society's biggest problems."(58:38) Closing segment.Eric's Contact InfoTwitterLinkedInConexus' ResourcesWebsite | ResourcesMentioned ContentPeopleKai-Fu LeeAndrew NgEric XingBook"ReCulturing: Design Your Company Culture to Connect with Strategy and Purpose for Lasting Success" (by Melissa Daimler)About the showDatacast features long-form, in-depth conversations with practitioners and researchers in the data community to walk through their professional journeys and unpack the lessons learned along the way. I invite guests coming from a wide range of career paths — from scientists and analysts to founders and investors — to analyze the case for using data in the real world and extract their mental models (“the WHY and the HOW”) behind their pursuits. Hopefully, these conversations can serve as valuable tools for early-stage data professionals as they navigate their own careers in the exciting data universe.Datacast is produced and edited by James Le. Get in touch with feedback or guest suggestions by emailing khanhle.1013@gmail.com.Subscribe by searching for Datacast wherever you get podcasts, or click one of the links below:Listen on SpotifyListen on Apple PodcastsListen on Google PodcastsIf you're new, see the podcast homepage for the most recent episodes to listen to or browse the full guest list.

Our collection of data is growing much faster than our capacity to use it, says Eric Daimler, the founder and CEO of Conexus. He explains to Bradley the opportunities and pitfalls of artificial intelligence and machine learning — and why companies with better math will be the ultimate winners.

Todays guest is CEO and co-founder of Conexus, the first spinoff of the MIT math department that takes discoveries in high level mathematics (category theory) and applies them to make databases intelligent across many industries.

Today's conversation is with Dr Eric Daimler. Eric is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Eric served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialisation, and public adoption of AI & Robotics. Expect to understand on a basic level what AI is, how it has been implemented in society and which sectors it is likely to be more prominent in. I ask about Healthcare, transport, and even data. Importantly this leads to ethical discussions on what AI can and should do, what boundaries we should draw, and what frameworks need to be in place. As part of this, we delve into the impact of AI on the job market which is a common criticism of machines taking the jobs of humans. Eric shares his vision for AI in our lives and how he hopes people like you and I engage with the conversation about how it will impact society and our lives. Today's podcast is supported by Crypto Glasgow. Founders Donald and Dec have appeared three time on the podcast sharing the principles behind the best performing investment asset of the last 12 months and the inner workings of the Crypto currency market. The Crypto space is vast and growing. You can get easily lost in the noise. Investing in crypto differs to other assets and the Crypto Glasgow team have 20+ years of investing experience in all asset classes, you can rely on their specific crypto expertise to navigate what can be a confusing market. No matter your investment approach and how much you are looking to invest, whether you're a newbie or experienced investor, Crypto Glasgow have you covered with their wide range of products and services to support you in the fast growing and innovative world of cryptocurrencies. I'm a member of the CG Pro discord for just £29.99 per month/ It combines everything you need from investing, trading and education to go from beginner to PRO. There is also the CG PRO affiliate program where you can earn a side income from simply being a member of CG Pro. You can visit https://www.ccgla.co.uk to learn more today. Today's podcast is sponsored by FitLogic Systems. Owner Joe McNee has worked in the fitness space for a number of years whilst juggling a full time job. He started automating boring repetitive tasks and over time grew the part time hobby into a business and has worked with some of big names in the fitness industry. FitLogic systems can fully implement a custom automation solution to your business. This allows you as coach to do more coaching and less admin work. This can work whether you are a one man band looking to get back more time, a coach looking to reduce their admin load, or you're trying to scale your business to the next level and increase your capacity with more coaches and more clients. Regular podcast guest, David Hatt uses FitLogic systems to ensure he can keep the quality of coaching high without getting bogged down in boring admin work. Not sure if its right for you? Take the quick quiz below and find out https://readinesstoscale.scoreapp.com/ Or find him on Instagram www.instagram.com/joe_fitlogic_systems Connect with Eric: Website - https://conexus.com/ LinkedIn - https://www.linkedin.com/in/ericdaimler/

In this episode, we're joined by Eric Daimler, CEO & co-founder of Conexus AI, Inc, an MIT spin out. We discuss the Conexus software platform, which is built on top of breakthroughs in the mathematics of Category Theory, and how it guarantees the integrity of universal data models. Eric shares real-world examples of applying this approach to various complex industries, such as transportation and logistics, avionics, and energy.Listen to this episode wherever you listen to podcasts. Eric Daimler: https://www.linkedin.com/in/ericdaimler/ Joey Dodds: https://www.linkedin.com/in/joey-dodds-4b462a41/ Rob Dockins: https://galois.com/team/robert-dockins/ Galois, Inc.: https://galois.com/ Contact us: podcast@galois.com

Highlights from this week's conversation include:Eric's background and career journey (3:30)Presenting to people without knowledge of AI (11:04)Why math was chosen over AI (19:03)From compilers to databases (25:42)The contribution of category theory (30:09)The Connexus customer experience (37:45)The primary user of Connexus (46:33)Interacting with 300,000 databases (51:07)When Connexus begins to add value (54:02)The best way to learn this mathematical approach (55:46)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we'll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Francois Candelon, Managing Director at the BCG Henderson Institute, has spent 30 years researching how companies adopt modern technology.  His research spans business, technology, economics, and science. Francois is a popular speaker, author, and advisor who has been featured at events including Mobile World Congress, TED@BCG, Politico AI Summit, and Wuzhen Internet Conference. Francois is also a leader on BCG's GAMMA AI@Scale team. Listen and learn...The one company Francois says best illustrates how AI can transform legacy industriesWhy "artificial intelligence" isn't really "intelligent"What is an "AI strategy"... and what are the four questions to ask to define yoursHow a fintech company in the UK reduced costs to transfer money by 90% with AIWhat's required to earn the public's trust in AIWhy every company should be required to have a "social license" to use AIReferences in this episode...Fortune article on human-machine collaboration by FrancoisFrancois' "BCG Expert" profileDr. Eric Daimler, Obama's AI authority, on AI and the Future of WorkThe McKinsey "AI in 2020 Survey"

Today's episode features Dr. Eric Daimler, who is an authority in the Artificial Intelligence community with over 20 years of experience in the field. He currently leads MIT's first-ever spinout from its Math department and has co-founded six technology companies that have pioneered work in fields ranging from software systems to statistical arbitrage. As a Presidential Innovation Fellow during the Obama Administration, Eric helped drive the agenda for U.S. leadership in research, commercialization, and public adoption of AI. Eric is a passionate technologist, and we dove deep into conversations about AI - the potential, algorithm regulation and much more. It was great speaking with Dr. Daimler on compositionality, his work at Conexus and I loved his points on having “circuit breakers” for AI, and his philosophy around lifesaving AI innovations should be quickly adopted and embraced, while emphasizing that it is important to be bringing more people into the conversation around AI so more people are comfortable with it - particularly with regard to bias and ethics in AI. Links of interest: Dr. Eric DaimlerAbout Conexus - Adaptable Data Consolidation

This show often discusses artificial intelligence and ideas to consider as technology progresses. We have discussed the deep tech of how it works and its implications on privacy. In this episode, we'll talk about the complex and controversial topic of AI policy and speak about some of the things we should be worried about regarding its future. In a time crunch? Check out the time stamps below:[01:15] - Guest Intro [03:38] - Western technology leadership[04:50] - Regulating AI[11:00] - The promise of self-driving cars[13:05] - AI data audition [17:50] - Neural networks to train AI[19:00] - Reducing mathematical knowledge, AI bottleneck [20:35] - What is in the way of the promise of AI[24:20] - Eric Daimler book[27:50] - The uses of trained AI models[29:30] - Health care industry data usage[33:25] - AI to speed up research[33:50] - What is rural AI? Guest Links:https://www.linkedin.com/in/ericdaimler/https://conexus.com/ Our Team:Host: Angelo KastroulisExecutive Producer: Náture KastroulisProducer: Albert PerrottaCommunications Strategist: Albert PerrottaAudio Engineer: Ryan ThompsonMusic: All Things Grow by Oliver Worth

A 10/10 guest. Dr. Eric Daimler is a leading authority in robotics and artificial intelligence with over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. Eric served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. In this episode, expect to learn: - The layman's definition of Artificial Intelligence and how it dates back - An expert's opinion on why we should be scared of technology and if it REALLY does looks like the Terminator films - What working for the Executive Office of the White House and being the authority for AI for Obama looks like - Dr. Eric's concerns of the Metaverse and how Augmented Reality can improve our life - What it is like to interview with Jeff Bezo's before Amazon became what it is today - Dr Eric's hope for the future as a technologist and how A.I can save and change lives. ---- If you love this podcast you can buy me a coffee - https://www.buymeacoffee.com/Dbyd ---- Extra Stuff: Follow Eric on Instagram **Instagram - https://www.instagram.com/ericdaimler/** Follow Eric on Twitter **Twitter - https://www.twitter.com/ead/** ME:Reach out to me on: Instagram: https://www.instagram.com/developmentbydavid/ LinkedIn: https://www.instagram.com/developmentbydavid/ The Development by David Podcast on: Spotify: https://open.spotify.com/show/6DV9tUfz5nGCmH0bfZUFrM **Apple Podcasts: https://podcasts.apple.com/gb/podcast/the-development-by-david-podcast/id1542740010**

Join me as I speak with Dr. Eric Daimler the current impact of AI in the government, Conexus AI and Universities impact on AI. We even touch on categorical algebra and how it will impact the world of AI and ML.

Harry's guest Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration, has an interesting argument about math. If you're a young person today trying to decide which math course you're going to take—or maybe an old person who just wants to brush up—he says you shouldn't bother with trigonometry or calculus. Instead he says you should study category theory. An increasingly important in computer science, category theory is about the relationships between sets or structures. It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after the data has been transformed in some way. Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability. Longtime listeners know that data interoperability in healthcare, or more often the lack of interoperability, is a repeating theme of the show. In fields from drug development to frontline medical care, we've got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data. That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it's all stored in different databases and formats that can't be safely merged without a nightmarish amount of work. So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data's integrity – well, it's time to pay attention. That's why on today's show, we're all going back to school for an introductory class in category theory.Please rate and review The Harry Glorikian Show on Apple Podcasts! Here's how to do that from an iPhone, iPad, or iPod touch:1. Open the Podcasts app on your iPhone, iPad, or Mac. 2. Navigate to The Harry Glorikian Show podcast. You can find it by searching for it or selecting it from your library. Just note that you'll have to go to the series page which shows all the episodes, not just the page for a single episode.3. Scroll down to find the subhead titled "Ratings & Reviews."4. Under one of the highlighted reviews, select "Write a Review."5. Next, select a star rating at the top — you have the option of choosing between one and five stars. 6. Using the text box at the top, write a title for your review. Then, in the lower text box, write your review. Your review can be up to 300 words long.7. Once you've finished, select "Send" or "Save" in the top-right corner. 8. If you've never left a podcast review before, enter a nickname. Your nickname will be displayed next to any reviews you leave from here on out. 9. After selecting a nickname, tap OK. Your review may not be immediately visible.That's it! Thanks so much.TranscriptHarry Glorikian: Hello. I'm Harry Glorikian, and this is The Harry Glorikian Show, where we explore how technology is changing everything we know about healthcare.My guest today is Eric Daimler, a serial software entrepreneur and a former Presidential Innovation Fellow in the Obama Administration.And he has an interesting argument about math. Daimler says if you're a young person today trying to decide which math course you're going to take, or maybe an old person who just wants to brush up, you shouldn't bother with trigonometry or calculus.Instead he says you should study category theory.That's a field that isn't even part of the curriculum at most high schools. But it's increasingly important in computer science.Category theory is about the relationships between sets or structures. It can be used to prove that different structures are consistent or compatible with one another, and to prove that the relationships in a dataset are still intact even after you've transformed that data in some way.Together with two former MIT mathematicians, Daimler co-founded a company called Conexus that uses category theory to tackle the problem of data interoperability.Now…longtime listeners of the show know that data interoperability in healthcare, or more often the lack of interoperability, is one of my biggest hobby horses. In fields from drug development to frontline medical care, we've got petabytes of data to work with, in the form of electronic medical records, genomic and proteomic data, and clinical trial data.That data could be the fuel for machine learning and other kinds of computation that could help us make develop drugs faster and make smarter decisions about care. The problem is, it's all stored in different databases and formats that can't be safely merged without a nightmarish amount of work.So when someone like Daimler says they have a way to use math to bring heterogeneous data together without compromising that data's integrity – well, I pay attention.So on today's show, we're all going back to school for an introductory class in category theory from Conexus CEO Eric Daimler.Harry Glorikian: Eric, welcome to the show.Eric Daimler: It's great to be here.Harry Glorikian: So I was reading your varied background. I mean, you've worked in so many different kinds of organizations. I'm not sure that there is a compact way or even an accurate way to describe you. So can you describe yourself? You know, what do you do and what are your main interest areas?Eric Daimler: Yeah, I mean, the easiest way to describe me might come from my mother. Well, where, you know, somebody asked her, is that the doctor? And she says, Well, yes, but he's not the type that helps people. So I you know, I've been doing research around artificial intelligence and I from a lot of different perspectives around my research in graph theory and machine learning and computational linguistics. I've been a venture capitalist on Sand Hill Road. I've done entrepreneurship, done entrepreneurship, and I started a couple of businesses which I'm doing now. And most notably I was doing policy in Washington, D.C. is part of the Obama administration for a time. So I am often known for that last part. But my background really is rare, if not unique, for having the exposure to AI from all of those angles, from business, academia and policy.Harry Glorikian: Yeah. I mean, I was looking at the obviously the like you said, the one thing that jumped out to me was the you were a Presidential Innovation Fellow in the Obama administration in 2016. Can you can you give listeners an idea of what is what is the Presidential Innovation Fellowship Program? You know, who are the types of people that are fellows and what kind of things do they do?Eric Daimler: Sure, it was I guess with that sort of question, it's helpful then to give a broader picture, even how it started. There was a a program started during the Nixon administration that's colloquially known as the Science Advisers to the President, you know, a bipartisan group to give science advice to the president that that's called the OSTP, Office of Science and Technology Policy. There are experts within that group that know know everything from space to cancer, to be super specific to, in my domain, computer security. And I was the authority that was the sole authority during my time in artificial intelligence. So there are other people with other expertise there. There are people in different capacities. You know, I had the particular capacity, I had the particular title that I had that was a one year term. The staffing for these things goes up and down, depending on the administration in ways that you might be able to predict and guess. The people with those titles also also find themselves in different parts of the the executive branch. So they will do a variety of things that are not predicted by the the title of the fellow. My particular role that I happened to be doing was in helping to coordinate on behalf of the President, humbly, on behalf of the President, their research agenda across the executive branch. There are some very able people with whom I had the good fortune of working during my time during my time there, some of which are now in the in the Biden administration. And again, it's to be a nonpartisan effort around artificial intelligence. Both sides should really be advocates for having our research agenda in government be most effective. But my role was coordinating such things as, really this is helpful, the definition of robotics, which you might be surprised by as a reflex but but quickly find to be useful when you're thinking that the Defense Department's definition and use, therefore, of robotics is really fundamentally different than that of health and human services use and a definition of robotics and the VA and Department of Energy and State and and so forth.Eric Daimler: So that is we find to be useful, to be coordinated by the Office of the President and experts speaking on behalf. It was started really this additional impulse was started after the effects of, I'll generously call them, of healthcare.gov and the trip-ups there where President Obama, to his great credit, realized that we needed to attract more technologists into government, that we had a lot of lawyers to be sure we had, we had a ton of academics, but we didn't have a lot of business people, practical technologists. So he created a way to get people like me motivated to come into government for short, short periods of time. The the idea was that you could sit around a cabinet, a cabinet meeting, and you could you would never be able to raise your hand saying, oh, I don't know anything about economics or I don't know anything about foreign policy, but you could raise your hand and say, Oh, I don't know anything about technology. That needs to be a thing of the past. President Obama saw that and created a program starting with Todd. Todd Park, the chief technologist, the second chief technology officer of the United States, is fantastic to to start to start some programs to bring in people like me.Harry Glorikian: Oh, yeah. And believe me, in health care, we need we need more technologists, which I always preach. I'm like, don't go to Facebook. Come here. You know, you can get double whammy. You can make money and you can affect people's lives. So I'm always preaching that to everybody. But so if I'm not mistaken, in early 2021, you wrote an open letter to the brand new Biden administration calling for sort of a big federal effort to improve national data infrastructure. Like, can you summarize for everybody the argument in that piece and. Do you see them doing any of the items that you're suggesting?Eric Daimler: Right. The the idea is that despite us making some real good efforts during the Obama administration with solidifying our, I'll say, our view on artificial intelligence across the executive, and this continuing actually into the Trump administration with the establishment of an AI office inside the OSTP. So credit where credit is due. That extended into the the Biden administration, where some very well-meaning people can be focusing on different parts of the the conundrum of AI expressions, having various distortions. You know, the popular one we will read about is this distortion of bias that can express itself in really ugly ways, as you know, as individuals, especially for underrepresented groups. The point of the article was to help others be reminded of of some of the easy, low hanging fruit that we can that we can work on around AI. So, you know, bias comes in a lot of different ways, the same way we all have cognitive distortions, you know, cognitive biases. There are some like 50 of them, right. You know, bias can happen around gender and ethnicity and age, sexual orientation and so forth. You know, it all can also can come from absence of data. There's a type of bias that's present just by being in a developed, rich country in collecting, for example, with Conexus's customers, my company Conexus's customers, where they are trying to report on their good efforts for economic and social good and around clean, renewable energies, they find that there's a bias in being able to collect data in rich countries versus developing countries.Eric Daimler: That's another type of bias. So that was that was the point of me writing that open letter, to prioritize, these letters. It's just to distinguish what the low hanging fruit was versus some of the hard problems. The, some of tthe low hanging fruit, I think is available, I can say, In three easy parts that people can remember. One is circuit breakers. So we we can have circuit breakers in a lot of different parts of these automated systems. You know, automated car rolling down a road is, is the easiest example where, you know, at some point a driver needs to take over control to determine to make a judgment about that shadow being a person or a tumbleweed on the crosswalk, that's a type of circuit breaker. We can have those circuit breakers in a lot of different automated systems. Another one is an audit. And the way I mean is audit is having people like me or just generally people that are experts in the craft being able to distinguish the data or the biases can become possible from the data model algorithms where biases also can become possible. Right. And we get a lot of efficiency from these automated systems, these learning algorithms. I think we can afford a little bit taken off to audit the degree to which these data models are doing what we intend.Eric Daimler: And an example of a data model is that Delta Airlines, you know, they know my age or my height, and I fly to San Francisco, to New York or some such thing. The data model would be their own proprietary algorithm to determine whether or not I am deserving of an upgrade to first class, for example. That's a data model. We can have other data models. A famous one that we all are part of is FICO scores, credit scores, and those don't have to be disclosed. None of us actually know what Experian or any of the credit agencies used to determine our credit scores. But they they use these type of things called zero knowledge proofs, where we just send through enough data, enough times that we can get to a sense of what those data models are. So that's an exposure of a data model. A declarative exposure would be maybe a next best thing, a next step, and that's a type of audit.Eric Daimler: And then the third low hanging fruit, I'd say, around regulation, and I think these are just coming towards eventualities, is demanding lineage or demanding provenance. You know, you'll see a lot of news reports, often on less credible sites, but sometimes on on shockingly credible sites where claims are made that you need to then search yourself and, you know, people in a hurry just won't do it, when these become very large systems and very large systems of information, alert systems of automation, I want to know: How were these conclusions given? So, you know, an example in health care would be if my clinician gave me a diagnosis of, let's say, some sort of cancer. And then to say, you know, here's a drug, by the way, and there's a five chance, 5 percent chance of there being some awful side effects. You know, that's a connection of causation or a connection of of conclusions that I'm really not comfortable with. You know, I want to know, like, every step is like, wait, wait. So, so what type of cancer? So what's the probability of my cancer? You know, where is it? And so what drug, you know, how did you make that decision? You know, I want to know every little step of the way. It's fine that they give me that conclusion, but I want to be able to back that up. You know, a similar example, just in everyday parlance for people would be if I did suddenly to say I want a house, and then houses are presented to me. I don't quite want that. Although that looks like good for a Hollywood narrative. Right? I want to say, oh, wait, what's my income? Or what's my cash? You know, how much? And then what's my credit? Like, how much can I afford? Oh, these are houses you can kind of afford. Like, I want those little steps or at least want to back out how those decisions were made available. That's a lineage. So those three things, circuit breaker, audit, lineage, those are three pieces of low hanging fruit that I think the European Union, the State of New York and other other government entities would be well served to prioritize.Harry Glorikian: I would love all of them, especially, you know, the health care example, although I'm not holding my breath because I might not come back to life by how long I'd have to hold my breath on that one. But we're hoping for the best and we talk about that on the show all the time. But you mentioned Conexus. You're one of three co founders, I believe. If I'm not mistaken, Conexus is the first ever commercial spin out from MIT's math department. The company is in the area of large scale data integration, building on insights that come out of the field of mathematics that's called category algebra, categorical algebra, or something called enterprise category theory. And to be quite honest, I did have to Wikipedia to sort of look that up, was not familiar with it. So can you explain category algebra in terms of a non mathematician and maybe give us an example that someone can wrap their mind around.Eric Daimler: Yeah. Yeah. And it's important to get into because even though what my company does is, Conexus does a software expression of categorical algebra, it's really beginning to permeate our world. You know, the the way I tell my my nieces and nephews is, what do quantum computers, smart contracts and Minecraft all have in common? And the answer is composability. You know, they are actually all composable. And what composable is, is it's kind of related to modularity, but it's modularity without regard to scale. So the the easy analogy is in trains where, yeah, you can swap out a boxcar in a train, but mostly trains can only get to be a couple of miles long. Swap in and out boxcars, but the train is really limited in scale. Whereas the train system, the system of a train can be infinitely large, infinitely complex. At every point in the track you can have another track. That is the difference between modularity and composability. So Minecraft is infinitely self referential where you have a whole 'nother universe that exists in and around Minecraft. In smart contracts is actually not enabled without the ability to prove the efficacy, which is then enabled by categorical algebra or its sister in math, type theory. They're kind of adjacent. And that's similar to quantum computing. So quantum computing is very sexy. It gets in the press quite frequently with forks and all, all that. If it you wouldn't be able to prove the efficacy of a quantum compiler, you wouldn't actually. Humans can't actually say whether it's true or not without type theory or categorical algebra.Eric Daimler: How you think of kind categorical algebra you can think of as a little bit related to graph theory. Graph theory is those things that you see, they look like spider webs. If you see the visualizations of graph theories are graphs. Category theory is a little bit related, you might say, to graph theory, but with more structure or more semantics or richness. So in each point, each node and each edge, in the vernacular, you can you can put an infinite amount of information. That's really what a categorical algebra allows. This, the discovery, this was invented to be translating math between different domains of math. The discovery in 2011 from one of my co-founders, who was faculty at MIT's Math Department, was that we could apply that to databases. And it's in that the whole world opens up. This solves the problem that that bedeviled the good folks trying to work on healthcare.gov. It allows for a good explanation of how we can prevent the next 737 Max disaster, where individual systems certainly can be formally verified. But the whole plane doesn't have a mechanism of being formally verified with classic approaches. And it also has application in drug discovery, where we have a way of bringing together hundreds of thousands of databases in a formal way without risk of data being misinterpreted, which is a big deal when you have a 10-year time horizon for FDA trials and you have multiple teams coming in and out of data sets and and human instinct to hoard data and a concern about it ever becoming corrupted. This math and the software expression built upon it opens up just a fantastically rich new world of opportunity for for drug discovery and for clinicians and for health care delivery. And the list is quite, quite deep.Harry Glorikian: So. What does Conexus provide its clients? Is it a service? Is it a technology? Is it both? Can you give us an example of it?Eric Daimler: Yeah. So Conexus is software. Conexus is enterprise software. It's an enterprise software platform that works generally with very large organizations that have generally very large complex data data infrastructures. You know the example, I can start in health care and then I can I can move to an even bigger one, was with a hospital group that we work with in New York City. I didn't even know health care groups could really have this problem. But it's endemic to really the world's data, where one group within the same hospital had a particular way that they represented diabetes. Now to a layman, layman in a health care sense, I would think, well, there's a definition of diabetes. I can just look it up in the Oxford English Dictionary. But this particular domain found diabetes to just be easily represented as yes, no. Do they have it? Do they not? Another group within the same hospital group thought that they would represent it as diabetes, ow are we treating it? A third group would be representing it as diabetes, how long ago. And then a fourth group had some well-meaning clinicians that would characterize it as, they had it and they have less now or, you know, type one, type two, you know, with a more more nuanced view.Eric Daimler: The traditional way of capturing that data, whether it's for drug discovery or whether it's for delivery, is to normalize it, which would then squash the fidelity of the data collected within those groups. Or they most likely to actually just wouldn't do it. They wouldn't collect the data, they wouldn't bring the data together because it's just too hard, it's too expensive. They would use these processes called ETL, extract, transform, load, that have been around for 30 years but are often slow, expensive, fragile. They could take six months to year, cost $1,000,000, deploy 50 to 100 people generally from Accenture or Deloitte or Tata or Wipro. You know, that's a burden. It's a burden, you know, so the data wasn't available and that would then impair the researchers and their ability to to share data. And it would impair clinicians in their view of patient care. And it also impaired the people in operations where they would work on billing. So we work with one company right now that that works on 1.4 trillion records a year. And they just have trouble with that volume and the number of databases and the heterogeneous data infrastructure, bringing together that data to give them one view that then can facilitate health care delivery. Eric Daimler: The big example is, we work with Uber where they they have a very smart team, as smart as one might think. They also have an effectively infinite balance sheet with which they could fund an ideal IT infrastructure. But despite that, you know, Uber grew up like every other organization optimizing for the delivery of their service or product and, and that doesn't entail optimizing for that infrastructure. So what they found, just like this hospital group with different definitions of diabetes, they found they happen to have grown up around service areas. So in this case cities, more or less. So when then the time came to do analysis -- we're just passing Super Bowl weekend, how will the Super Bowl affect the the supply of drivers or the demand from riders? They had to do it for the city of San Francisco, separate than the city of San Jose or the city of Oakland. They couldn't do the whole San Francisco Bay Area region, let alone the whole of the state or the whole of the country or what have you. And that repeated itself for every business question, every organizational question that they would want to have. This is the same in drug discovery. This is the same in patient care delivery or in billing. These operational questions are hard, shockingly hard.Eric Daimler: We had another one in logistics where we had a logistics company that had 100,000 employees. I didn't even know some of these companies could be so big, and they actually had a client with 100,000 employees. That client had 1000 ships, each one of which had 10,000 containers. And I didn't even know like how big these systems were really. I hadn't thought about it. But I mean, they're enormous. And the question was, hey, where's our personal protective equipment? Where is the PPE? And that's actually a hard question to ask. You know, we are thinking about maybe our FedEx tracking numbers from an Amazon order. But if you're looking at the PPE and where it is on a container or inside of a ship, you know, inside this large company, it's actually a hard question to ask. That's this question that all of these organizations have. Eric Daimler: In our case, Uber, where they they they had a friction in time and in money and in accuracy, asking every one of these business questions. They went then to find, how do I solve this problem? Do I use these old tools of ETL from the '80s? Do I use these more modern tools from the 2000s? They're called RDF or OWL? Or is there something else? They discovered that they needed a more foundational system, this categorical algebra that that's now expressing itself in smart contracts and quantum computers and other places. And they just then they found, oh, who are the leaders in the enterprise software expression of that math? And it's us. We happen to be 40 miles north of them. Which is fortunate. We worked with Uber to to solve that problem in bringing together their heterogeneous data infrastructure to solve their problems. And to have them tell it they save $10 million plus a year in in the efficiency and speed gains from the solution we helped provide for them.[musical interlude]Harry Glorikian: Let's pause the conversation for a minute to talk about one small but important thing you can do, to help keep the podcast going. And that's leave a rating and a review for the show on Apple Podcasts.All you have to do is open the Apple Podcasts app on your smartphone, search for The Harry Glorikian Show, and scroll down to the Ratings & Reviews section. Tap the stars to rate the show, and then tap the link that says Write a Review to leave your comments. It'll only take a minute, but you'll be doing a lot to help other listeners discover the show.And one more thing. If you like the interviews we do here on the show I know you'll like my new book, The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer.It's a friendly and accessible tour of all the ways today's information technologies are helping us diagnose diseases faster, treat them more precisely, and create personalized diet and exercise programs to prevent them in the first place.The book is now available in print and ebook formats. Just go to Amazon or Barnes & Noble and search for The Future You by Harry Glorikian.And now, back to the show.[musical interlude]Harry Glorikian: So your website says that your software can map data sources to each other so that the perfect data model is discovered, not designed. And so what does that mean? I mean, does that imply that there's some machine learning or other form of artificial intelligence involved, sort of saying here are the right pieces to put together as opposed to let me design this just for you. I'm trying to piece it together.Eric Daimler: Yeah. You know, the way we might come at this is just reminding ourselves about the structure of artificial intelligence. You know, in the public discourse, we will often find news, I'm sure you can find it today, on deep learning. You know, whatever's going on in deep learning because it's sexy, it's fun. You know, DeepMind really made a name for themselves and got them acquired at a pretty valuation because of their their Hollywood-esque challenge to Go, and solving of that game. But that particular domain of AI, deep learning, deep neural nets is a itself just a subset of machine learning. I say just not not not to minimize it. It's a fantastically powerful algorithm. But but just to place it, it is a subset of machine learning. And then machine learning itself is a subset of artificial intelligence. That's a probabilistic subset. So we all know probabilities are, those are good and bad. Fine when the context is digital advertising, less fine when it's the safety of a commercial jet. There is another part of artificial intelligence called deterministic artificial intelligence. They often get expressed as expert systems. Those generally got a bad name with the the flops of the early '80s. Right. They flopped because of scale, by the way. And then the flops in the early 2000s and 2010s from IBM's ill fated Watson experiment, the promise did not meet the the reality.Eric Daimler: It's in that deterministic A.I. that that magic is to be found, especially when deployed in conjunction with the probabilistic AI. That's that's where really the future is. There's some people have a religious view of, oh, it's only going to be a probabilistic world but there's many people like myself and not to bring up fancy names, but Andrew Ng, who's a brilliant AI researcher and investor, who also also shares this view, that it's a mix of probabilistic and deterministic AI. What deterministic AI does is, to put it simply, it searches the landscape of all possible connections. Actually it's difference between bottoms up and tops down. So the traditional way of, well, say, integrating things is looking at, for example, that hospital network and saying, oh, wow, we have four definitions of diabetes. Let me go solve this problem and create the one that works for our hospital network. Well, then pretty soon you have five standards, right? That's the traditional way that that goes. That's what a top down looks that looks like.Eric Daimler: It's called a Golden Record often, and it rarely works because pretty soon what happens is the organizations will find again their own need for their own definition of diabetes. In most all cases, that's top down approach rarely works. The bottoms up approach says, Let's discover the connections between these and we'll discover the relationships. We don't discover it organically like we depend on people because it's deterministic. I, we, we discover it through a massive, you know, non intuitive in some cases, it's just kind of infeasible for us to explore a trillion connections. But what the AI does is it explores a factorial number actually is a technical, the technical equation for it, a factorial number of of possible paths that then determine the map of relationships between between entities. So imagine just discovering the US highway system. If you did that as a person, it's going to take a bit. If you had some infinitely fast crawlers that robot's discovering the highway system infinitely fast, remember, then that's a much more effective way of doing it that gives you some degree of power. That's the difference between bottoms up and tops down. That's the difference between deterministic, really, we might say, and probabilistic in some simple way.Harry Glorikian: Yeah, I'm a firm believer of the two coming together and again, I just look at them as like a box. I always tell people like, it's a box of tools. I need to know the problem, and then we can sort of reach in and pick out which set of tools that are going to come together to solve this issue, as opposed to this damn word called AI that everybody thinks is one thing that they're sort of throwing at the wall to solve a problem.Harry Glorikian: But you're trying to solve, I'm going to say, data interoperability. And on this show I've had a lot of people talk about interoperability in health care, which I actually believe is, you could break the system because things aren't working right or I can't see what I need to see across the two hospitals that I need information from. But you published an essay on Medium about Haven, the health care collaboration between Amazon, JPMorgan, Berkshire Hathaway. Their goal was to use big data to guide patients to the best performing clinicians and the most affordable medicines. They originally were going to serve these first three founding companies. I think knowing the people that started it, their vision was bigger than that. There was a huge, you know, to-do when it came out. Fireworks and everything. Launched in 2018. They hired Atul Gawande, famous author, surgeon. But then Gawande left in 2020. And, you know, the company was sort of quietly, you know, pushed off into the sunset. Your essay argued that Haven likely failed due to data interoperability challenges. I mean. How so? What what specific challenges do you imagine Haven ran into?Eric Daimler: You know, it's funny, I say in the article very gently that I imagine this is what happened. And it's because I hedge it that that the Harvard Business Review said, "Oh, well, you're just guessing." Actually, I wasn't guessing. No, I know. I know the people that were doing it. I know the challenges there. But but I'm not going to quote them and get them in trouble. And, you know, they're not authorized to speak on it. So I perhaps was a little too modest in my framing of the conclusion. So this actually is what happened. What happens is in the same way that we had the difficulty with healthcare.gov, in the same way that I described these banks having difficulty. Heterogeneous databases don't like to talk to one another. In a variety of different ways. You know, the diabetes example is true, but it's just one of many, many, many, many, many, many cases of data just being collected differently for their own use. It can be as prosaic as first name, last name or "F.last name." Right? It's just that simple, you know? And how do I bring those together? Well, those are those are called entity resolutions. Those are somewhat straightforward, but not often 100 percent solvable. You know, this is just a pain. It's a pain. And, you know, so what what Haven gets into is they're saying, well, we're massive. We got like Uber, we got an effectively infinite balance sheet. We got some very smart people. We'll solve this problem. And, you know, this is some of the problem with getting ahead of yourself. You know, I won't call it arrogance, but getting ahead of yourself, is that, you think, oh, I'll just be able to solve that problem.Eric Daimler: You know, credit where credit is due to Uber, you know, they looked both deeper saying, oh, this can't be solved at the level of computer science. And they looked outside, which is often a really hard organizational exercise. That just didn't happen at Haven. They thought they thought they could they could solve it themselves and they just didn't. The databases, not only could they have had, did have, their own structure, but they also were stored in different formats or by different vendors. So you have an SAP database, you have an Oracle database. That's another layer of complication. And when I say that these these take $1,000,000 to connect, that's not $1,000,000 one way. It's actually $2 million if you want to connect it both ways. Right. And then when you start adding five, let alone 50, you take 50 factorial. That's a very big number already. You multiply that times a million and 6 to 12 months for each and a hundred or two hundred people each. And you just pretty soon it's an infeasible budget. It doesn't work. You know, the budget for us solving solving Uber's problem in the traditional way was something on the order of $2 trillion. You know, you do that. You know, we had a bank in the U.S. and the budget for their vision was was a couple of billion. Like, it doesn't work. Right. That's that's what happened Haven. They'll get around to it, but but they're slow, like all organizations, big organizations are. They'll get around to solving this at a deeper level. We hope that we will remain leaders in database integration when they finally realize that the solution is at a deeper level than their than the existing tools.Harry Glorikian: So I mean, this is not I mean, there's a lot of people trying to solve this problem. It's one of those areas where if we don't solve it, I don't think we're going to get health care to the next level, to sort of manage the information and manage people and get them what they need more efficiently and drive down costs.Eric Daimler: Yeah.Harry Glorikian: And I do believe that EMRs are. I don't want to call them junk. Maybe I'm going too far, but I really think that they you know, if you had decided that you were going to design something to manage patients, that is not the software you would have written to start. Hands down. Which I worry about because these places won't, they spent so much putting them in that trying to get them to rip them out and put something in that actually works is challenging. You guys were actually doing something in COVID-19, too, if I'm not mistaken. Well, how is that project going? I don't know if it's over, but what are you learning about COVID-19 and the capabilities of your software, let's say?Eric Daimler: Yeah. You know, this is an important point that for anybody that's ever used Excel, we know what it means to get frustrated enough to secretly hard code a cell, you know, not keeping a formula in a cell. Yeah, that's what happened in a lot of these systems. So we will continue with electronic medical records to to bring these together, but they will end up being fragile, besides slow and expensive to construct. They will end up being fragile, because they were at some point hardcoded. And how that gets expressed is that the next time some other database standard appears inside of that organization's ecosystem from an acquisition or a divestiture or a different technical standard, even emerging, and then the whole process starts all over again. You know, we just experience this with a large company that that spent $100 million in about five years. And then they came to us and like, yeah, we know it works now, but we know like a year from now we're going to have to say we're going to go through it again. And, it's not like, oh, we'll just have a marginal difference. No, it's again, that factorial issue, that one database connected to the other 50 that already exist, creates this same problem all over again at a couple of orders of magnitude. So what we discover is these systems, these systems in the organization, they will continue to exist.Eric Daimler: These fragile systems will continue to exist. They'll continue to scale. They'll continue to grow in different parts of the life sciences domain, whether it's for clinicians, whether it's for operations, whether it's for drug discovery. Those will continue to exist. They'll continue to expand, and they will begin to approach the type of compositional systems that I'm describing from quantum computers or Minecraft or smart contracts, where you then need the the discovery and math that Conexus expresses in software for databases. When you need that is when you then need to prove the efficacy or otherwise demonstrate the lack of fragility or the integrity of the semantics. Conexus can with, it's a law of nature and it's in math, with 100 percent accuracy, prove the integrity of a database integration. And that matters in high consequence context when you're doing something as critical as drug side effects for different populations. We don't want your data to be misinterpreted. You can't afford lives to be lost or you can't, in regulation, you can't afford data to be leaking. That's where you'll ultimately need the categorical algebra. You'll need a provable compositional system. You can continue to construct these ones that will begin to approach compositionality, but when you need the math is when you need to prove it for either the high consequence context of lives, of money or related to that, of regulation.Harry Glorikian: Yeah, well, I keep telling my kids, make sure you're proficient in math because you're going to be using it for the rest of your life and finance. I always remind them about finance because I think both go together. But you've got a new book coming out. It's called "The Future is Formal" and not tuxedo like formal, but like you're, using the word formal. And I think you have a very specific meaning in mind. And I do want you to talk about, but I think what you're referring to is how we want automated systems to behave, meaning everything from advertising algorithms to self-driving trucks. And you can tell me if that my assumption is correct or not.Eric Daimler: Though it's a great segue, actually, from the math. You know, what I'm trying to do is bring in people that are not programmers or research technology, information technology researchers day to day into the conversation around automated digital systems. That's my motivation. And my motivation is, powered by the belief that we will bring out the best of the technology with more people engaged. And with more people engaged, we have a chance to embrace it and not resist it. You know, my greatest fear, I will say, selfishly, is that we come up with technology that people just reject, they just veto it because they don't understand it as a citizen. That also presents a danger because I think that companies' commercial expressions naturally will grow towards where their technology is needed. So this is actually to some extent a threat to Western security relative to Chinese competition, that we embrace the technology in the way that we want it to be expressed in our society. So trying to bring people into this conversation, even if they're not programmers, the connection to math is that there are 18 million computer programmers in the world. We don't need 18 million and one, you know. But what we do need is we do need people to be thinking, I say in a formal way, but also just be thinking about the values that are going to be represented in these digital infrastructures.Eric Daimler: You know, somewhere as a society, we will have to have a conversation with ourselves to determine the car driving to the crosswalk, braking or rolling or slowing or stopping completely. And then who's liable if it doesn't? Is it the driver or is it the manufacturer? Is it the the programmer that somehow put a bug in their code? You know, we're entering an age where we're going to start experiencing what some person calls double bugs. There's the bug in maybe one's expression in code. This often could be the semantics. Or in English. Like your English doesn't make sense. Right? Right. Or or was it actually an error in your thinking? You know, did you leave a gap in your thinking? This is often where where some of the bugs in Ethereum and smart contracts have been expressed where, you know, there's an old programming rule where you don't want to say something equals true. You always want to be saying true equals something. If you get if you do the former, not the latter, you can have to actually create bugs that can create security breaches.Eric Daimler: Just a small little error in thinking. That's not an error in semantics. That level of thinking, you don't need to know calculus for, or category theory for that matter. You just need to be thinking in a formal way. You know, often, often lawyers, accountants, engineers, you know, anybody with scientific training can, can more quickly get this idea, where those that are educated in liberal arts can contribute is in reminding themselves of the broader context that wants to be expressed, because often engineers can be overly reductionist. So there's really a there's a push and pull or, you know, an interplay between those two sensibilities that then we want to express in rules. Then that's ultimately what I mean by formal, formal rules. Tell me exactly what you mean. Tell me exactly how that is going to work. You know, physicians would understand this when they think about drug effects and drug side effects. They know exactly what it's going to be supposed to be doing, you know, with some degree of probability. But they can be very clear, very clear about it. It's that clear thinking that all of us will need to exercise as we think about the development and deployment of modern automated digital systems.Harry Glorikian: Yeah, you know, it's funny because that's the other thing I tell people, like when they say, What should my kid take? I'm like, have him take a, you know, basic programming, not because they're going to do it for a living, but they'll understand how this thing is structured and they can get wrap their mind around how it is. And, you know, I see how my nephew thinks who's from the computer science world and how I think, and sometimes, you know, it's funny watching him think. Or one of the CTOs of one of our companies how he looks at the world. And I'm like you. You got to back up a little bit and look at the bigger picture. Right. And so it's the two of us coming together that make more magic than one or the other by themselves.Harry Glorikian: So, you know, I want to jump back sort of to the different roles you've had in your career. Like like you said, you've been a technology investor, a serial startup founder, a university professor, an academic administrator, an entrepreneur, a management instructor, Presidential Innovation Fellow. I don't think I've missed anything, but I may have. You're also a speaker, a commentator, an author. Which one of those is most rewarding?Eric Daimler: Oh, that's an interesting question. Which one of those is most rewarding? I'm not sure. I find it to be rewarding with my friends and family. So it's rewarding to be with people. I find that to be rewarding in those particular expressions. My motivation is to be, you know, just bringing people in to have a conversation about what we want our world to look like, to the degree to which the technologies that I work with every day are closer to the dystopia of Hollywood narratives or closer to our hopes around the utopia that's possible, that where this is in that spectrum is up to us in our conversation around what these things want to look like. We have some glimpses of both extremes, but I'd like people, and I find it to be rewarding, to just be helping facilitate the helping catalyze that conversation. So the catalyst of that conversation and whatever form it takes is where I enjoy being.Harry Glorikian: Yeah, because I was thinking about like, you know, what can, what can you do as an individual that shapes the future. Does any of these roles stand out as more impactful than others, let's say?Eric Daimler: I think the future is in this notion of composability. I feel strongly about that and I want to enroll people into this paradigm as a framework from which to see many of the activities going around us. Why have NFTs come on the public, in the public media, so quickly? Why does crypto, cryptocurrency capture our imagination? Those And TikTok and the metaverse. And those are all expressions of this quick reconfiguration of patterns in different contexts that themselves are going to become easier and easier to express. The future is going to be owned by people that that take the special knowledge that they've acquired and then put it into short business expressions. I'm going to call them rules that then can be recontextualized and redeployed. This is my version of, or my abstraction of what people call the the future being just all TikTok. It's not literally that we're all going to be doing short dance videos. It's that TikTok is is an expression of people creating short bits of content and then having those be reconfigured and redistributed. That can be in medicine or clinical practice or in drugs, but it can be in any range of expertise, expertise or knowledge. And what's changed? What's changed and what is changing is the different technologies that are being brought to bear to capture that knowledge so that it can be scalable, so it can be compositional. Yeah, that's what's changing. That's what's going to be changing over the next 10 to 20 years. The more you study that, I think the better off we will be. And I'd say, you know, for my way of thinking about math, you might say the more math, the better. But if I were to choose for my children, I would say I would replace trig and geometry and even calculus, some people would be happy to know, with categorical algebra, category theory and with probability and statistics. So I would replace calculus, which I think is really the math of the 20th century, with something more appropriate to our digital age, which is categorical algebra.Harry Glorikian: I will tell my son because I'm sure he'll be very excited to to if I told him that not calculus, but he's not going to be happy when I say go to this other area, because I think he'd like to get out of it altogether.Eric Daimler: It's easier than calculus. Yeah.Harry Glorikian: So, you know, it was great having you on the show. I feel like we could talk for another hour on all these different aspects. You know, I'm hoping that your company is truly successful and that you help us solve this interoperability problem, which is, I've been I've been talking about it forever. It seems like I feel like, you know, the last 15 or 20 years. And I still worry if we're any closer to solving that problem, but I'm hopeful, and I wish you great success on the launch of your new book. It sounds exciting. I'm going to have to get myself a copy.Eric Daimler: Thank you very much. It's been fun. It's good to be with you.Harry Glorikian: Thank you.Harry Glorikian: That's it for this week's episode. You can find a full transcript of this episode as well as the full archive of episodes of The Harry Glorikian Show and MoneyBall Medicine at our website. Just go to glorikian.com and click on the tab Podcasts.I'd like to thank our listeners for boosting The Harry Glorikian Show into the top three percent of global podcasts.If you want to be sure to get every new episode of the show automatically, be sure to open Apple Podcasts or your favorite podcast player and hit follow or subscribe. Don't forget to leave us a rating and review on Apple Podcasts. And we always love to hear from listeners on Twitter, where you can find me at hglorikian.Thanks for listening, stay healthy, and be sure to tune in two weeks from now for our next interview.

Eric Daimler advised the Obama administration on how to have conversations about AI. His work led to the creation of the AI office within the Science Advisory Group of The White House which has now become a cabinet-level position reporting to The President. Eric's a walking encyclopedia about AI policy and he shares all in this fascinating discussion about the future of technology, ethics, and society.Listen and learn...What it's like to shift from academia to venture capital to entrepreneurship to public serviceHow the growth of data sources as well as data creates an unimaginably large number of data relationshipsHow Conexus applied categorical algebra to bring together 300k databases at UberWhy it's data integration limitations that are constraining AI innovation more than compute, storage, or algorithms How category theory is required for smart contracts on blockchains and quantum computing How Eric thinks about when AI should make autonomous decisions vs. requiring human intervention The role of regulation in managing job elimination due to AI The ethical framework Eric proposes for evaluating what decisions AI can and should makeThe challenges of enforcing data policies like GDPR in the EUHow Eric defines "responsible AI"References in this episode...Eric's company, ConexusThe President's Council of Advisors on Science and TechnologyAftershock which includes a chapter by EricEric on Twitter

Where is the biggest AI bottleneck and what is the next foundational shift in AI? On this week's State of Identity podcast, host Cameron D'Ambrosi welcomes Dr. Eric Daimler, CEO & Co-Founder of Conexus AI to dive into data integration and consolidation. They break down the limitations of AI and look at  regulatory headwinds around the development and deployment of AI technologies.   

This week we feature our interview with Eric Daimler, PhD. Eric and I discussed how AI can unlock the potential of humanity.   Dr. Eric Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book "The Coming Composability: The roadmap for using technology to solve society's biggest problems." A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI for a more sustainable, secure, and prosperous future. As a Presidential Innovation Fellow during the Obama Administration, Daimler helped drive the agenda for U.S. leadership in research, commercialization, and public adoption of AI. He has also served as Assistant Dean and Assistant Professor of Software Engineering in Carnegie Mellon's School of Computer Science. His academic research focuses on the intersection of Machine Learning, Computational Linguistics, and Network Science (Graph Theory). He has a specialization in public policy and economics, helped launch Carnegie Mellon's Silicon Valley Campus, and founded its Entrepreneurial Management program. A frequent keynote speaker, he has presented at venues including the engineering schools of MIT, Stanford, and Harvard. Daimler studied at Stanford University, the University of Washington-Seattle, and Carnegie Mellon University, where he earned his PhD in its School of Computer Science. Contact Information Twitter: @ead LinkedIn: linkedin.com/in/ericdaimler Website: http://www.conexus.com/  Re-read Saturday  News Multitasking is the first or second greatest LIE in the modern business world. The best description of multitasking would include thrash, waste, and hubris. The problem is that EVERYONE thinks they are special and can multitask their way to the effective delivery of value. Chapter 3 of Why Limit WIP: We Are Drowning In Work blasts away at multitasking (another take on the topic from 2015: Multitasking Yourself Away From Efficiency | Software Process and Measurement https://bit.ly/37XmrSY). Multitasking is bad, don't do it.   Remember to buy a copy and read along.  Amazon Affiliate LInk:  https://amzn.to/36Rq3p5  Previous Entries Week 1: Preface, Foreword, Introduction, and Logistics – https://bit.ly/3iDezbp Week 2: Processing and Memory – https://bit.ly/3qYR4yg  Week 3: Completion - https://bit.ly/3usMiLm Week 4: Multitasking - https://bit.ly/37hUh5z    Upcoming Events: Final Call!  Free Webinar When Prioritization Goes Bad https://www.greatpro.org/Webinar-Live-Register?id=1954  April 19, 2022 11 AM EDT to 1230 EDT   Next SPaMCAST  Next week for SPaMCAST 700 we will feature our interview with Slater Victoroff. Slater presents an alternate definition for AI.  Compare and contrast to Dr. Daimler's definition?    

Eric is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. He is the CEO and Co-Founder of Conexus, which provides solutions that analyze and integrate large amounts of data from multiple sources. Eric is a frequent speaker, lecturer, and commentator. He works to empower communities and citizens to leverage AI for a more sustainable, secure, and prosperous future. Eric studied at Stanford University, the University of Washington-Seattle, and Carnegie Mellon University, where he earned his PhD in its School of Computer Science. “Every company is going to become an Artificial Intelligence company.” – Eric Daimler Today on the Tech Leader Talk podcast: - Why Artificial Intelligence is the economic engine of the future - The important relationship between Data Infrastructure and AI - Policies and regulation related to AI - Tasks that are best handled by AI today - What's coming next for AI ResourcesBook: ReCulturing by Melissa Daimler - https://www.amazon.com/ReCulturing-Company-Culture-Connect-Strategy/dp/1264278608 Connect with Eric Daimler: Website: https://conexus.com/ LinkedIn: https://www.linkedin.com/in/ericdaimler/ Twitter: https://twitter.com/ead Thanks for listening! Be sure to get your free copy of Steve's latest book, Cracking the Patent Code, and discover his proven system for identifying and protecting your most valuable inventions. Get the book at https://stevesponseller.com/book.

Part 2: This week on Making Data Simple, we have Eric Diamler. Eric is an authority in the Artificial Intelligence community with over 20 years of experience in the field. He currently leads MIT's first-ever spinout from its Math department and has co-founded six technology companies that have pioneered work in fields ranging from software systems to statistical arbitrage. Show Notes 00:00 -Definition of gov success8:02 - Six startups and counting and the latest : Conexus13:12 - Conexus differentiation20:05 - AI regulationWant to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. Abstract Making Data Simple Podcast is hosted by Al Martin, WW VP Account Technical Leader IBM Technology Sales, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Part 2: This week on Making Data Simple, we have Eric Diamler. Eric is an authority in the Artificial Intelligence community with over 20 years of experience in the field. He currently leads MIT's first-ever spinout from its Math department and has co-founded six technology companies that have pioneered work in fields ranging from software systems to statistical arbitrage. Show Notes 00:00 -Definition of gov success8:02 - Six startups and counting and the latest : Conexus13:12 - Conexus differentiation20:05 - AI regulationWant to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. Abstract Making Data Simple Podcast is hosted by Al Martin, WW VP Account Technical Leader IBM Technology Sales, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Part 1: This week on Making Data Simple, we have Eric Diamler.  Eric is an authority in the Artificial Intelligence community with over 20 years of experience in the field. He currently leads MIT's first ever spinout from its Math department and has cofounded six technology companies that have pioneered work in fields ranging from software systems to statistical arbitrage. Show Notes 2:24 –Presidential Innovation Fellow during the Obama Administration4:30 -Be careful about confusing a vision with a short-term time horizon12:56 - Don't be a technology laggard, know what the "data" inhibitors are20:12 - Mr. President.  Here is the situation27:49 - How does the US compare to other countries?Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. Abstract Making Data Simple Podcast is hosted by Al Martin, WW VP Account Technical Leader IBM Technology Sales, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

Part 1: This week on Making Data Simple, we have Eric Diamler.  Eric is an authority in the Artificial Intelligence community with over 20 years of experience in the field. He currently leads MIT's first ever spinout from its Math department and has cofounded six technology companies that have pioneered work in fields ranging from software systems to statistical arbitrage. Show Notes 2:24 –Presidential Innovation Fellow during the Obama Administration4:30 -Be careful about confusing a vision with a short-term time horizon12:56 - Don't be a technology laggard, know what the "data" inhibitors are20:12 - Mr. President.  Here is the situation27:49 - How does the US compare to other countries?Want to be featured as a guest on Making Data Simple? Reach out to us at [almartintalksdata@gmail.com] and tell us why you should be next. Abstract Making Data Simple Podcast is hosted by Al Martin, WW VP Account Technical Leader IBM Technology Sales, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. 

This week, The Periphery talks to Dr. Eric Daimler, who formerly served as a Presidential Innovation Fellow in the Obama Administration and as Professor at Carnegie Mellon University. We talk regulatory trends, policy frustrations, the political and social conditions that drive innovation, and what the rapid proliferation of AI means for the future of work, wealth distribution, society, and culture. We also discuss Pixar's Wall-E.Leave us an honest review, subscribe, and send us any ideas or feedback that you'd like to share at theperipherypodcast@gmail.com. And be sure to become a Conversationalist on our Patreon if you are eager to support our efforts to diversify tech. Our GDPR privacy policy was updated on August 8, 2022. Visit acast.com/privacy for more information.

In this episode, host Michael Marchuk talks with Dr. Eric Daimler, CEO of Conexus AI and an expert with over 20 years in artificial intelligence and a Presidential Innovation Fellow during the Obama Administration. Eric shares his thoughts on automation and AI as it relates to the context of our knowledge and processing and provides insights on business automation as every company becomes an "AI business". . Here's what we talked with Eric about: * How implicit knowledge can be encapsulated using AI to create explicit, sharable knowledge across the organization * Why data in context is critical to the application of AI * Where RPA and AI will leverage category theory to extend the useful lifespan of existing systems as new systems are introduced in an organization . To ensure that you never miss an episode of Transform NOW, be sure to subscribe!

Dr. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. He is also the founder of data integration firm Conexus, and we kick our conversation off with the work he is doing to integrate large heterogeneous data infrastructures. This leads us into an exploration of the concept of compositionality, a structural feature that enables systems to scale, which Dr. Daimler argues is the future of IT infrastructure. We discuss how the way we apply AI to data is constantly changing, with data sources growing quadratically, and how this necessitates an understanding of newer forms of math such as category theory by AI specialists. Towards the end of our discussion, we move on to the subject of the adoption of AI in technologies that lives depend on, and Dr. Daimler gives his recommendation for how to engender trust amongst the larger population. Key Points From This Episode:Experience Dr. Daimler has in AI in an academic, commercial, and governmental capacity.An issue in the choices being made around how to create data that is useful in large organizations.Dr. Daimler's work bringing heterogeneous data together to influence better business decisions.How much money is wasted on ETL processes and how bad the jobs in that field are.The difference between modularity and compositionality and why the latter is the future of IT infrastructure.How compositionality enables scalability and the need of certain branches of math to justify it.The work Dr. Daimler is doing in the field of compositionality at Connexus.Whether it is crucial to grasp these newer forms of math to achieve AI mastery.How AI systems can integrate into contexts involving human labor and empathy.The need to bring together probabilistic and deterministic AI in life and death contexts.How to get the public to trust and believe in AI-powered tech with the capacity to save lives.What AI practitioners can do to ensure they use their skillset to create a better future.Tweetables:“You can create data that doesn't add more fidelity to the knowledge you're looking to gain for better business decisions and that is one of the limitations that I saw expressed in the government and other large organizations.” — @ead [0:01:32]“That's the world, is compositionality. That is where we are going and the math that supports that, type theory, categorical theory, categorical logic, that's going to sweep away everything underneath IT infrastructure.” — @ead [0:10:23]“At the trillions of data, a trillion data sources, each growing quadratically, what we need is category theory.” — @ead [0:13:51]“People die and the way to solve that problem when you are talking about these life and death contexts for commercial airplane manufacturers or in energy exploration where the consequences of failure can be disastrous is to bring together the sensibilities of probabilistic AI and deterministic AI.” — @ead [0:24:07]“Circuit breakers, oversight, and data lineage, those are three ways that I would institute a regulatory regime around AI and algorithms that will engender trust amongst the larger population.” — @ead [0:35:12]Links Mentioned in Today's Episode:Dr. Eric Daimler on LinkedInDr. Eric Daimler on TwitterConexus

In this episode of “Technically Human,” I sit down with Dr. Eric Daimler. We talk about one of the biggest technology problems facing us today—data deluge—and how new computational models and theories can help solve it and, Dr. Daimler weighs in on the gaps, differences, and possibilities for collaboration between policy, industry, and academia. And we talk about what a vision of “AI for Good” might look like in a world of increasingly infinite data. Dr. Eric Daimler is a leading authority in robotics and artificial intelligence with over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. He served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics. Dr. Daimler has incubated, built and led several technology companies recognized as pioneers in their fields ranging from software systems to statistical arbitrage. His newest venture, Conexus, is a groundbreaking solution for what is perhaps today's biggest information technology problem — data deluge. As founder and CEO of Conexus, Dr. Daimler  is leading the development of CQL, a patent-pending platform founded upon category theory — a revolution in mathematics — to help companies manage the overwhelming and rapidly growing challenge of data integration and migration. His academic research has been at the intersection of AI, Computational Linguistics, and Network Science (Graph Theory). His work has expanding to include economics and public policy. He served as Assistant Professor and Assistant Dean at Carnegie Mellon's School of Computer Science where he founded the university's Entrepreneurial Management program and helped to launch Carnegie Mellon's Silicon Valley Campus. He has studied at the University of Washington-Seattle, Stanford University, and Carnegie Mellon University, where he earned his Ph.D. in Computer Science. Dr. Daimler's extensive career spanning business, academics and policy give him a rare perspective on the next generation of AI. Dr. Daimler sees clearly how information technology can dramatically improve our world. However, it demands our engagement. Neither a utopia nor dystopia is inevitable. What matters is how we shape and react to, its development. This episode was produced by Matt Perry. Our head of reseaarch is Sakina Nuruddin. Art by Desi Aleman.

On this episode, Dara sits down with Dr. Eric Daimler, founder of Connexus and Presidential Innovation Fellow during the Obama administration, to discuss the biggest dangers lurking in AI, the first ever spin-out from MIT's math department and how its addressing today's AI challenges, and Eric's views on how the heck we go about trying to regulate an algorithm. 

Before we wish everyone happy holidays, we wanted to share some of our favorite highlights from the Before IT Happened's first year! This show is about people who are all about innovation and making the world a better and more equitable place. Regardless of their background or the problem, they're working to solve, it really comes down to the human spirit and the big idea that something needs to change. So join us for this end-of-year round-up of some of the most interesting and groundbreaking guests we've talked to on this podcast so far! Then we'll see you in January to talk with more trailblazers and changemakers!  Before any world-changing innovation, there was a moment, an event, a realization that sparked the idea before it happened. This is a podcast about that moment — about that idea. Before IT Happened takes you on a journey with the innovators who imagined — and are still imagining — our future. Join host Donna Loughlin as her guests tell their stories of how they brought their visions to life. JUMP STRAIGHT INTO: (02:26) - On Before IT Happened's evolution - “When this podcast first started, initially it was conversations with some of the most amazing innovators and visionaries that are working in the technology sectors, as well as science and space. Real futurists.” (04:10) - Shaping the Future of AI with Eric Daimler - “Whatever technology that is invented between when you are 15 and 25 is something you think you can build a career on, whatever comes after you are 25 or 30 is against the law of nature. You don't want to learn new techniques.” (05:48) - Getting a Black Belt in Blockchain with Medha Parlikar - “My goal is that the user experiences that will emerge and the tooling that will emerge will make it very easy for the consumer to interact with blockchain.” (08:46) - Smart, Electric Motorcycles and Achieving Zero Fatalities Before 2030 with Damon Founder Jay Giraud - “I was in Jakarta and I realized if I've dedicated my life to getting the world off oil and the car side of it's handled, and the motorcycle side in the world actually dwarfs the number of vehicles driven compared to cars, so here's this really big missing link.” (10:24) - A New Wave of Manning Up With the 2 LADS Daniel Sharman and Leggy Langdon - “We're putting the weapons down for a moment and saying: ‘Oh God, isn't it weird being human?'” (13:25) - Disrupting the Silicon Valley Boys Club with Mercedes Soria - “If you're a woman, especially in technology and Silicon Valley, you have to prove your worth. As soon as you come in, they assume that you don't know anything.” (16:53) - Getting Kids Excited About STEM and Space with “The NASA Lady” Pamela Greyer - “I look at Elon Musk and I'm like, ‘Well, at what point and where did he start?'  There is still such a long way to go when we look at black and brown women and men in the industry pursuing these careers.” (20:23) - Sprouting our way to Better Health and Sustainability with Doug Evans - “I think for the people that are listening to this, you can start just with a Mason jar and some seeds and you could change the world.” (22:14) - From Farm to Table to Feeding the World with Uncrushable Celebrity Chef and Entrepreneur Tyler Florence - “Right now, hands down, there's enough food grown to feed the world. There's no. It's just not managed properly.” (23:44) - In the Pursuit of Cleaner Farming, Making Great Wine and Chasing Monarch Butterflies with Carlo Mondavi - “Farmers are the most important people on the planet. We need farmers.”  (25:30) The impact that every guest has made on this show's journey - “We have to set a goal that's seemingly impossible because it changes what we believe we can do.” GET THE FULL EPISODES FEATURED HERE: https://www.beforeithappened.com/podcast-episodes/shaping-the-future-of-ai-with-eric-daimler-episode-26 (Shaping the Future of AI with Eric Daimler)...

Behind the complexity of artificial intelligence, there are groundbreaking stories like that of AI and robotics expert, entrepreneur and investor Eric Daimler.  Eric joins this episode of Before IT Happened to tell us all about his journey from being an entrepreneurial engineering student who found massive success before hitting his twenties to advising former president Barack Obama's administration on how to regulate and develop AI and data infrastructure. Are we adapting or adopting? Listen for Eric's answer and more! Before any world-changing innovation, there was a moment, an event, a realization that sparked the idea before it happened. This is a podcast about that moment — about that idea. Before IT Happened takes you on a journey with the innovators who imagined — and are still imagining — our future. Join host Donna Loughlin as her guests tell their stories of how they brought their visions to life.  JUMP STRAIGHT INTO: (01:52) - Growing up in a basement surrounded by computers: “My aspirations were Bill Gates, Steve Jobs and Mitch Kapor. Those were actually three faces I literally had in my high school locker.” (08:19) - Eric's college experience at Carnegie Mellon: “I demonstrated the ability of a personal computer at that time to have a cheap robotic arm do its thing. I wanted to do robotics at an early age.” (11:44) - Dropping a PhD program and moving to London for work at 18: “As a young kid, I was able to buy my first Porsche, which is some sort of an accomplishment for a boy.” (17:46) - Studying two PhDs simultaneously while running a company: “If you looked at the flight, whether it was going to Pittsburgh from Silicon Valley, I was probably the only one on that flight doing machine learning problem sets.” (21:54) - Explaining why AI is the economic engine of the future: “It's often said that every company is a data company. It's not really just the data, it's that what you learn from the data and getting even more precise, it's in the data relationships” (28:04) - Machine intelligence and robotics at the White House: “I was just motivated really to serve the country. It was a pay cut, a substantial pay cut, but my father and my brother had served in the military” (33:40) - Eric's advice on how to apply AI in a safe way: “This is a big deal. It's a national imperative, maybe even saying a Western civilization imperative that everybody is involved.” EPISODE RESOURCES: Connect with Eric on https://www.linkedin.com/in/ericdaimler (LinkedIn) and https://twitter.com/ead (Twitter) Read: https://ericdaimler.medium.com/dear-mr-president-274d94f91996 (Eric's letter to President Biden on how the U.S. can ‘win the AI race') Learn more about Eric's data consolidation company https://conexus.com/ (Conexus)  Watch Eric's SALT talk: https://www.youtube.com/watch?v=YP9kodLGvT8 (AI, Robotics & Consciousness) Read Alvin Toffler's 1984 book https://www.amazon.com/Future-Shock-Alvin-Toffler/dp/0553277375/ref=asc_df_0553277375/?tag=hyprod-20&linkCode=df0&hvadid=312519927002&hvpos=&hvnetw=g&hvrand=13524889963189346404&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9031945&hvtargid=pla-522053264324&psc=1 (“Future Shock”) Thank you for listening! Follow https://www.beforeithappened.com/ (Before IT Happened) on https://www.instagram.com/beforeithappenedshow/ (Instagram) and https://twitter.com/TheBIHShow (Twitter), and don't forget to subscribe, rate and share the show wherever you listen to podcasts!  Before IT Happened is produced by Donna Loughlin and https://www.studiopodsf.com/ (StudioPod Media) with additional editing and sound design by https://nodalab.com/ (Nodalab). The Executive Producer is Katie Sunku Wood and all episodes are written by Jack Buehrer.

EPISODE 32, SEASON 2: Dr. Eric Daimler is a leading authority in data science. A serial entrepreneur and academic, he served as a Presidential Innovation Fellow during the Obama administration driving US leadership in AI and robotics. He's currently the CEO at Conexus, working to solve the massive problem of data deluge and deliver practical applications for business and governments alike.ABOUT THE SERIES: Future of XYZ is a weekly interview series dedicated to fostering forward-thinking discussions about where we are as a world and where we're going.FOR MORE INFORMATION: Visit future-of.xyz and follow on social media... LinkedIn: @lisagralnek, @lvg-co-strategy | Twitter: @lgralnek | IG: @futureofxyz

What is Category Theory, and why is it called the math of the 21st century?We're fortunate to have the CEO and Co-Founder of Conexus, Eric Daimler, join us for this episode. Eric carries a wide breadth of experience, from Professional Investing to Policy Advisor and Author. He taught at Carnegie Mellon and worked under the Obama Administration.Eric emphatically speaks on the potent potential of Category Theory. When working with billions and even trillions of data points, this branch of math holds the key to faultless data.Eric provides real-world applications where Conexus is using Category Theory, including ride-sharing apps such as Uber, investing applications, and diabetes datasets. When looking at big data, there's a necessity for zero mistake data. With thousands of ambiguities, this becomes impractical.Category theory's relevance and applications are only taking off. Eric recommends his Co-Founder's book to learn more on the subject, which you can check out at the link below.Listen to the episode to learn more now!Don't forget to subscribe to the show on iTunes, Spotify, or wherever you get your podcasts. See you in the next episode! Resources:https://mitpress.mit.edu/books/category-theory-sciences 

In this episode of the IoT For All Podcast, Conexus CEO and Co-Founder Eric Daimler joins us to talk AI systems. Eric shares some of the most important components of AI systems, what new use cases they enable, and what the market looks like for AI technology and applications. Eric also shares the story of Conexus including how it came to be and some of the challenges of bringing an AI-powered data integration solution to market.Dr. Eric Daimler is a leading authority in robotics and artificial intelligence with over 20 years of experience as an entrepreneur, investor, technologist, and policymaker. Eric served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of the President, as the sole authority driving the agenda for U.S. leadership in research, commercialization, and public adoption of AI & Robotics.As a successful entrepreneur, Eric is looking towards the next generation of AI as a system that creates a multi-tiered platform for fueling the development and adoption of emerging technology for industries that have traditionally been slow to adapt. As founder and CEO of Conexus, Eric is leading CQL, a patent-pending platform founded upon category theory — a revolution in mathematics — to help companies manage the overwhelming challenge of data integration and migration.Interested in connecting with Eric Daimler? Reach out to him on Linkedin!About Conexus: Conexus was founded to deal with one of the biggest problems plaguing the majority of businesses today — data deluge. Every business is now a data-driven business but they are few means to manage data efficiently with minimal time and cost.The Conexus solution uses new math developed at MIT to create new algorithms that establish relationships among large, disparate sets of data resulting in seamless data integration and interoperability which is accomplished in a short time period at a mere fraction of the cost of today's cumbersome, manual integration projects that can take years and waste billions of dollars.Key Questions and Topics from this Episode:(00:54) Intro to Eric Daimler(04:47) Intro to Conexus(06:16) What types of use cases have Conexus been involved in?(10:14) What is an AI system?(14:13) What are the components of an AI system(18:57) What is the industry and customer focus at Conexus(21:11) What challenges did you experience going to market?(23:30) What market trends have you seen in AI?(27:09) What are data lakes?(30:12) What's the best first step for companies to utilize their existing data?(33:42) How will AI affect the workforce?

*Episode Show Notes:* - Eric Daimler is the CEO & Co-Founder of Conexus.com. Daimler is an authority in Artificial Intelligence with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. - Daimler believes the Obama administration made big efforts to bring in more technologists into government for innovation and digital modernization, and is optimistic that sensibility around a digitally native environment will be expressed inside of the Federal Government, and continue to trickle down into states' governments for the benefit of all. - Human failure has come before machines got trained on human failures. Therefore, technologists can't use massive amounts of data on every human problem and expect to come out with mind blowing results. So there's limitations on technology. What can be done is to transform these whole domains of knowledge and map them onto others through a new type of math. -There's a discovery in this domain called category theory. Categorical mathematics, category theory, is really at a level above all those other mathematics that transforms a problem from geometry, into another problem called safe set theory, applying it to databases. The math of category theory changes how we relate to data. This is “the math of the future”. -It's at a higher level of math, a level of abstraction to model the world in which companies operate their business, and make bigger decisions better and faster, reasoning large amounts of data at a higher level to power a whole new change in our environment, as business people, as academics, as citizens. -Daimler suggests three ways to solve data issues: matching data in a unified database, create a silo and then they sell a subscription to data silos and data interoperability math analysis through category theory. -AI definition has been misinterpreted over the years as algorithms that collect data and have machines do stuff, when in reality, AI should be understood as a system that senses plans, acts and learns from the experience. And it senses plans and acts from inputs that are given to it. -Not everyone needs to be a programmer in a basement. People need to be playing a multitude of roles. There's not just a choice between computer science or an English degree. What the current world of tech needs is policy considerations, places to get involved, and a way to focus educational efforts. Automation doesn't mean no human intervention. Societies benefit by that exchange of ideas and communication of values. *Shownotes Links:* https://www.linkedin.com/in/ericdaimler ** https://youtu.be/YP9kodLGvT8 https://youtu.be/jqn4wnSBKuE https://youtu.be/c92rK_UZaXU *About HumAIn Podcast* The HumAIn Podcast is a leading artificial intelligence podcast that explores the topics of AI, data science, future of work, and developer education for technologists. Whether you are an Executive, data scientist, software engineer, product manager, or student-in-training, HumAIn connects you with industry thought leaders on the technology trends that are relevant and practical. HumAIn is a leading data science podcast where frequently discussed topics include ai trends, ai for all, computer vision, natural language processing, machine learning, data science, and reskilling and upskilling for developers. Episodes focus on new technology, startups, and Human Centered AI in the Fourth Industrial Revolution. HumAIn is the channel to release new AI products, discuss technology trends, and augment human performance. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy

Eric Daimler is a leading authority in Robotics and Artificial Intelligence. Eric served under the Obama Administration as a Presidential Innovation Fellow for AI and Robotics in the Executive Office of the President, with the sole authority for driving the agenda for United States leadership in research, commercialization and public adoption of AI & Robotics. “Don't confuse a clear vision with a short time horizon.” Take the Jetsons, for example. We had a clear vision of a robot who would do all these things. Nowadays, we have a Roomba. AI will be most successful when deployed in very structured environments. On AI and consciousness, “this will certainly not happen in the next 10-20 years, if ever. We don't even understand our own consciousness.” ————————————————————————— To learn more about this episode, including podcast transcripts and show notes, visit *salt.org/talks* ( http://salt.org/talks ) Moderated by Anthony Scaramucci.

Dr. Eric Daimler is the CEO and Co-Founder of Conexus which is a company that uses category theory to solve data interoperability.  Eric served in the Obama administration as a White House Fellow for Machine Learning and Robotics.  It has been said if AI is the new electricity then data is the new fuel to power it.  We have increased our output of data but our management of data has not kept pace.  Data interoperability is more crucial than ever as all businesses are data-driven. Conexus website: https://conexus.com/ My website: https://www.ishusingh.com/  

Artificial Intelligence or AI has the ability to change everything about how the world works. In fact PWC estimates that AI will contribute $15.6 trillion to the global economy by 2030. But what is AI exactly? On this week's show, we have the pleasure of having on Dr. Eric Daimler, CEO and Founder of Conexus, Presidential Innovation Fellow and one of the leading experts on AI. Dr. Daimler's passion and vision for AI allows us to gain a better definition of what AI is and how it is changing industries. He also lays out some of the risks that AI might create and how we can manage them. But AI is beyond just investing. Dr. Daimler also discusses how AI can help change wealth building opportunities in economically challenged places. What we find is that all of us should embrace learning about AI and the changes it will bring to our world in the coming years. Please join for the next episode of The Wealth Intersection.