Podcasts about Pedro Domingos

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   Can AI Ever Reach AGI? Pedro Domingos Explains the Missing Link In this episode of Eye on AI, renowned computer scientist and author of The Master Algorithm, Pedro Domingos, breaks down what's still missing in our race toward Artificial General Intelligence (AGI) — and why the path forward requires a radical unification of AI's five foundational paradigms: Symbolists, Connectionists, Bayesians, Evolutionaries, and Analogizers.   Topics covered: Why deep learning alone won't achieve AGI How reasoning by analogy could unlock true machine creativity The role of evolutionary algorithms in building intelligent systems Why transformers like GPT-4 are impressive—but incomplete The danger of hype from tech leaders vs. the real science behind AGI What the Master Algorithm truly means — and why we haven't found it yet Pedro argues that creativity is easy, reliability is hard, and that reasoning by analogy — not just scaling LLMs — may be the key to Einstein-level breakthroughs in AI.   Whether you're an AI researcher, machine learning engineer, or just curious about the future of artificial intelligence, this is one of the most important conversations on how to actually reach AGI.    

This episode is sponsored by Indeed.  Stop struggling to get your job post seen on other job sites. Indeed's Sponsored Jobs help you stand out and hire fast. With Sponsored Jobs your post jumps to the top of the page for your relevant candidates, so you can reach the people you want faster. Get a $75 Sponsored Job Credit to boost your job's visibility! Claim your offer now: https://www.indeed.com/EYEONAI     In this episode, renowned AI researcher Pedro Domingos, author of The Master Algorithm, takes us deep into the world of Connectionism—the AI tribe behind neural networks and the deep learning revolution.   From the birth of neural networks in the 1940s to the explosive rise of transformers and ChatGPT, Pedro unpacks the history, breakthroughs, and limitations of connectionist AI. Along the way, he explores how supervised learning continues to quietly power today's most impressive AI systems—and why reinforcement learning and unsupervised learning are still lagging behind.   We also dive into: The tribal war between Connectionists and Symbolists The surprising origins of Backpropagation How transformers redefined machine translation Why GANs and generative models exploded (and then faded) The myth of modern reinforcement learning (DeepSeek, RLHF, etc.) The danger of AI research narrowing too soon around one dominant approach Whether you're an AI enthusiast, a machine learning practitioner, or just curious about where intelligence is headed, this episode offers a rare deep dive into the ideological foundations of AI—and what's coming next. Don't forget to subscribe for more episodes on AI, data, and the future of tech.     Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI     (00:00) What Are Generative Models? (03:02) AI Progress and the Local Optimum Trap (06:30) The Five Tribes of AI and Why They Matter (09:07) The Rise of Connectionism (11:14) Rosenblatt's Perceptron and the First AI Hype Cycle (13:35) Backpropagation: The Algorithm That Changed Everything (19:39) How Backpropagation Actually Works (21:22) AlexNet and the Deep Learning Boom (23:22) Why the Vision Community Resisted Neural Nets (25:39) The Expansion of Deep Learning (28:48) NetTalk and the Baby Steps of Neural Speech (31:24) How Transformers (and Attention) Transformed AI (34:36) Why Attention Solved the Bottleneck in Translation (35:24) The Untold Story of Transformer Invention (38:35) LSTMs vs. Attention: Solving the Vanishing Gradient Problem (42:29) GANs: The Evolutionary Arms Race in AI (48:53) Reinforcement Learning Explained (52:46) Why RL Is Mostly Just Supervised Learning in Disguise (54:35) Where AI Research Should Go Next  

This episode is sponsored by Thuma.   Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details.   To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai In this episode of the Eye on AI podcast, Pedro Domingos—renowned AI researcher and author of The Master Algorithm—joins Craig Smith to break down the Symbolist approach to artificial intelligence, one of the Five Tribes of Machine Learning. Pedro explains how Symbolic AI dominated the field for decades, from the 1950s to the early 2000s, and why it's still playing a crucial role in modern AI. He dives into the Physical Symbol System Hypothesis, the idea that intelligence can emerge purely from symbol manipulation, and how AI pioneers like Marvin Minsky and John McCarthy built the foundation for rule-based AI systems. The conversation unpacks inverse deduction—the Symbolists' "Master Algorithm"—and how it allows AI to infer general rules from specific examples. Pedro also explores how decision trees, random forests, and boosting methods remain some of the most powerful AI techniques today, often outperforming deep learning in real-world applications. We also discuss why expert systems failed, the knowledge acquisition bottleneck, and how machine learning helped solve Symbolic AI's biggest challenges. Pedro shares insights on the heated debate between Symbolists and Connectionists, the ongoing battle between logic-based reasoning and neural networks, and why the future of AI lies in combining these paradigms. From AlphaGo's hybrid approach to modern AI models integrating logic and reasoning, this episode is a deep dive into the past, present, and future of Symbolic AI—and why it might be making a comeback. Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of intelligence!   Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI   (00:00) Pedro Domingos onThe Five Tribes of Machine Learning   (02:23) What is Symbolic AI?   (04:46) The Physical Symbol System Hypothesis Explained   (07:05) Understanding Symbols in AI   (11:51) What is Inverse Deduction?   (15:10) Symbolic AI in Medical Diagnosis   (17:35) The Knowledge Acquisition Bottleneck   (19:05) Why Symbolic AI Struggled with Uncertainty   (20:40) Machine Learning in Symbolic AI – More Than Just Connectionism   (24:08) Decision Trees & Their Role in Symbolic Learning   (26:55) The Myth of Feature Engineering in Deep Learning   (30:18) How Symbolic AI Invents Its Own Rules   (31:54) The Rise and Fall of Expert Systems – The CYCL Project   (38:53) Symbolic AI vs. Connectionism   (41:53) Is Symbolic AI Still Relevant Today?   (43:29) How AlphaGo Combined Symbolic AI & Neural Networks   (45:07) What Symbolic AI is Best At – System 2 Thinking   (47:18) Is GPT-4o Using Symbolic AI?   

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence. Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google's ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges. The conversation shifts to deep learning's limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI. We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all. From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what's next for machine intelligence. Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of innovation!    Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction (02:55) The Five Tribes of Machine Learning Explained   (06:34) Bayesian vs. Frequentist: The Probability Debate   (08:27) What is Bayes' Theorem & How AI Uses It   (12:46) The Power & Limitations of Bayesian Networks   (16:43) How Bayesian Inference Works in AI   (18:56) The Rise & Fall of Bayesian Machine Learning   (20:31) Bayesian AI in Medical Diagnosis & Search and Rescue   (25:07) How Google Used Bayesian Networks for Ads   (28:56) The Role of Uncertainty in AI Decision-Making   (30:34) Why Bayesian Learning is Computationally Hard   (34:18) Analogical Learning – The Overlooked AI Paradigm   (38:09) Support Vector Machines vs. Neural Networks   (41:29) How SVMs Once Dominated Machine Learning   (45:30) The Future of AI – Bayesian, Neural, or Hybrid?   (50:38) Where AI is Heading Next  

In an exclusive conversation, AI visionary Pedro shares insights into his groundbreaking contributions, including the unification of AI paradigms and the promise of neuro-symbolic AI. He reflects on key moments in his career and discusses the next frontiers in machine learning. 00:27- About Pedro Domingos Pedro Domingos is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning known for the Markov logic Network enabling uncertain inference.

Will we ever have an AI for president? In this episode of the podcast I speak with Pedro Domingos about the impact of AI on society, industry, and politics. Pedro is professor emeritus of computer science and engineering at the University of Washington and co-founded the International Machine Learning Society. He is also the Author of `The Master Algorithm' and `2040: A Silicon Valley Satire'. This episode is not a paid advertisement for Pedro's books, but we use his book `2040' to set the context for the discussion.  We discuss the hype and fear surrounding AI and the future of tech, and Pedro gives an insiders view into the realities of AI development and impact. In his view AI is a human made tool. It isn't going to take over the planet like the terminator, but it will be something that is used by humans to shift the balance of power in society, industry, and politics. A particularly interesting aspect of the discussion surrounds digital twins. Imagine a world in which dating apps are replaced with a digital platform in which your digital twin simulates dates, and even entire lives with the digital twins of potential partners. Users would then go on physical dates with the top performing selection. This same idea could be extended to predicting crime, presidential election outcomes, and more. All of a sudden the simulation hypothesis doesn't seem to crazy, when simulations are used to predict future outcomes in the real world. Another key topic in this conversation is that of `wokism'. Pedro discusses `wokism' as a new iteration of cultural marxism, an ideology that reinterprets the dynamics of class struggle through cultural and identity-based lenses. While the creation of wealth in a society is not inherently a zero-sum game—where one person's gain is necessarily another's loss— the division and redistribution of that wealth that has been created is a zero-sum conflict. Pedro suggests that `wokism' is an attempt to shift power and resources within society, but where the division between the oppressed and the privileged is identity based. ►Watch on YouTube: https://youtu.be/LjBZc-Zh4bc ►Find out more about Pedro's work here: https://en.wikipedia.org/wiki/Pedro_Domingos https://scholar.google.com/citations?user=KOrhfVMAAAAJ&hl=en https://homes.cs.washington.edu/~pedrod/ ►Follow Pedro on Twitter: @pmddomingos These conversations are supported by the Andrea von Braun foundation (http://www.avbstiftung.de/), as an exploration of the rich, exciting, connected, scientifically literate, and (most importantly) sustainable future of humanity. The Andrea von Braun Foundation has provided me with full creative freedom with their support. As such, the views expressed in these episodes are my own and/or those of my guests.  

Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. An expert in deep learning, neural networks, and AI ethics, Pedro's research work encompasses some of the most cutting edge concepts like the black box of deep networks, and Markov Logic Network. Pedro is the author of the best-selling book ‘The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World' (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies.His latest book, ‘2040: A Silicon Valley Satire', published in August 2024, is a novel that imagines the 2040 presidential election, where the Republican candidate is an AI named PresiBot, and the Democratic candidate is a fake Native American chief seeking to abolish the United States. Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He's a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR.To read more about Pedro Domingos, visit https://businessabc.net/wiki/pedro-do...The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015.The book outlines five approaches of machine learning: inductive reasoning, Connectionism, evolutionary computation, Bayes' theorem, analogical modelling. Pedro explains these tribes to the reader by referring to more understandable processes of logic, connections made in the brain, natural selection, probability and similarity judgments. The book suggests that each different tribe has the potential to contribute to a unifying "master algorithm".Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. Although the algorithm doesn't yet exist, he briefly reviews his own invention of the Markov logic network.In 2016 Bill Gates recommended the book, alongside Nick Bostrom's Superintelligence, as one of two books everyone should read to understand AI.2040: A Silicon Valley SatireIn a chaotic 2040 presidential race, an AI candidate named PresiBot is pitted against a Native American chief determined to dismantle the United States. The mastermind behind PresiBot is Ethan, the struggling CEO of KumbAI, a tech startup that created the AI as a last-ditch publicity stunt. However, with PresiBot prone to malfunctioning and spouting hallucinations, the nation's fate hangs by a thread.Set against a backdrop of a dystopian, tech-dominated San Francisco, 2040 is a fast-paced, satirical narrative that skewers the modern American landscape. From the overwhelming influence of Silicon Valley's tech giants to the fear-mongering surrounding AI and the increasingly bizarre political movements, the novel paints a sharp, witty critique of the near future. With razor-sharp dialogue and a plot that mirrors today's concerns, 2040 offers a darkly comedic reflection on where we might be headed.Support the show

Artificial Intelligence (AI) has changed dramatically in recent years. Today, AI is improving many industries and changing how we live. AI is now a key part of our future, making our lives easier and more efficient. But what is it that we need to look out for? What are some necessary measures to be taken to ensure a smooth and safe adoption of this technology? Watch this episode to learn about the past, present and future of AI. Your host, Mukesh Bansal, not only takes us through the journey of AI but also advices on how to navigate through this technological era. Resource List - More about Physics Nobel Prize - https://www.nobelprize.org/prizes/physics/2024/press-release/ More about Chemistry Nobel Prize - https://www.nobelprize.org/prizes/chemistry/2024/press-release/ More on Research behind Chemistry Nobel Prize Winners - https://youtu.be/KMfgV2QSlns?feature=shared Video by Deepmind on AlphaFold Server Demo - https://youtu.be/9ufplEgtq8w?feature=shared More on the first AI conference - https://home.dartmouth.edu/about/artificial-intelligence-ai-coined-dartmouth Watch the Chess Match between IBM Deep Blue v/s Garry Kasparov - https://youtu.be/KF6sLCeBj0s?feature=shared Watch IBM Watson on Jeopardy! - https://youtu.be/lI-M7O_bRNg?feature=shared 3Blue1Brown YouTube Channel - https://www.youtube.com/@3blue1brown Books from the episode: Perceptrons by Minsky - https://amzn.in/d/c8xn3Fh Genius Makers by Cade Metz - https://amzn.in/d/3pbTV1R The Master Algorithm by Pedro Domingos - https://amzn.in/d/14AiL3F Super Intelligence by Nick Bostrom - https://amzn.in/d/9hhq4td The Worlds I See by Dr. Fei-Fei Li - https://amzn.in/d/0iaga3Y Why Machines Learn by Anil Ananthaswamy - https://amzn.in/d/iiYC45X

Longtime machine-learning researcher, and University of Washington Professor Emeritus, Pedro Domingos joins a16z General Partner Martin Casado to discuss the state of artificial intelligence, whether we're really on a path toward AGI, and the value of expressing unpopular opinions.  It's a very insightful discussion as we head into an era of mainstream AI adoption, and ask big questions about how to ramp up progress and diversify research directions.Here's an excerpt of Pedro sharing his thoughts on the increasing cost of frontier models and whether that's the right direction:"if you believe the scaling laws hold and the scaling laws will take us to human-level intelligence, then, hey, it's worth a lot of investment. That's one part, but that may be wrong. The other part, however, is that to do that, we need exploding amounts of compute. "If if I had to predict what's going to happen, it's that we do not need a trillion dollars to reach AGI at all. So if you spend a trillion dollars reaching AGI, this is a very bad investment."Learn more:The Master Algorithm2040: A Silicon Valley SatireThe Economic Case for Generative AI and Foundation ModelsFollow everyone on Z:Pedro DomingosMartin Casado Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

For video version watch the episode on: → YouTube link: https://youtu.be/9-J4eV8qvZg Join us as we dive deep into the world of AI with Pedro Domingos, a machine learning pioneer and author of the acclaimed book "The Master Algorithm." Pedro shares his insights on the current AI hype cycle, the real impact of AI on society, and how he sees the future of this transformative technology. Guest on the episode: Pedro Domingos (Author and Machine Learning Professor) → Website: pedrodomingos.org → Linkedin: https://www.linkedin.com/in/pedro-domingos-77b183/ → Twitter: https://x.com/pmddomingos → Read Pedro's new book 2040: https://2040novel.com/ Host: Nataraj (Investor at Incisive VC, angel investor, and Senior Product Manager) → Linkedin: https://www.linkedin.com/in/natarajsindam/ → Twitter: https://x.com/natarajsindam → Email updates: https://startupproject.beehiiv.com/ → Website: ⁠⁠https://thestartupproject.io⁠⁠ In this episode, we discuss: 00:00 - Introduction:** Meet Pedro Domingos, a legend in the field of machine learning. 03:28 - Early Career:** Learn how Pedro got into machine learning in the 80s, a time when it wasn't considered "cool". 08:17 - AI vs. Machine Learning:** Pedro clarifies the difference between these two terms and explains why machine learning is the driving force behind all other AI applications. 13:26 - The AI Hype Cycle:** Pedro discusses the rapid progress of AI, but also warns of the potential for a stock market bubble if the hype exceeds the reality. 21:45 - LLMs and the Future of AI:** Pedro analyzes whether the current hype around LLMs will lead to AGI and speculates on what breakthroughs might be needed to get there. 31:44 - The Master Algorithm:** Pedro talks about the purpose and scope of his book, "The Master Algorithm," and how it offers a broader understanding of AI beyond just LLMs. 42:29 - Open AI and the Comedy of Errors:** Pedro dives into the hype surrounding Open AI and its perceived dominance in the AI space, sharing his perspective on their overestimation. 54:25 - AI Safety and the Call for a Moratorium:** Pedro dismisses the fear-mongering around AI's supposed threat to humanity and provides a rational perspective on the current AI safety concerns. 1:03:19 - AI in 2040:** Pedro discusses his satirical book, "2040," which paints a humorous, yet thought-provoking, picture of how AI might impact society. 1:14:18 - Recommendation Systems and Human Psychology:** Pedro examines the limitations of current recommendation systems and argues for a shift towards systems that give users more control over their information consumption. 1:24:38 - The Importance of Critical Information Consumption:** Pedro shares his strategies for staying informed in the age of information overload, emphasizing the need for a balanced and diverse intake of information. 1:38:59 - The Importance of Choosing the Right Problem:** Pedro stresses the importance of finding research problems that align with your passion and potential impact, rather than chasing the latest trends. Don't forget to like and subscribe for more insightful conversations about the world of AI! → YouTube: https://youtu.be/9-J4eV8qvZg→ Spotify: https://open.spotify.com/episode/3Og8mbra1cokQ5cRJdjZn1?si=iqEOqKLLSqSbk8ehkniFqg→ Apple podcasts: https://podcasts.apple.com/us/podcast/85-ai-should-not-be-regulated-author-ml-researcher/id1551300319?i=1000673806783→ Email updates: https://startupproject.beehiiv.com/ → Others: https://spotifyanchor-web.app.link/e/qYaG6vhTRNb #AI #machinelearning #themasteralgorithm #llm #agi #futureofai #tech #society #2040 #openai #google #facebook #recommendation #information #research #podcasts #startups #sharp ratio

Todd Ault interviews Pedro Domingos, a world-renowned AI researcher, tech-industry insider, and the author of the best-selling book The Master Algorithm. This groundbreaking scientific book, released in 2017, introduced machine learning to a wide audience and was recommended by Bill Gates as "required reading for everyone to understand AI". Pedro's latest book is titled 2040: A Silicon Valley Satire,  which explores the concept of an AI robot running for President. Tune in to hear Pedro provide insight into the origins of algorithms, their ethics, challenging limitations, and why backpropagation is driving the AI revolution.    "The goal of the book [2040: A Silicon Valley Satire] is for it to be a cautionary tale. In a sense, it's a caricature of where we are now. But it's also saying to people 'Look if we don't do anything - this is where we're headed' and it's not a good place."Pedro Domingos

Join Ravin Jesuthasan as he delves into the shifting paradigms of work and education. In this enlightening episode, Ravin discusses the historical and future impacts of automation and AI on job structures and educational methodologies. Explore how these technologies are reshaping the skills landscape and what that means for future generations and the global economy. Subscribe for ad-free interviews and bonus episodes https://plus.acast.com/s/the-unmistakable-creative-podcast. Hosted on Acast. See acast.com/privacy for more information.

This episode is sponsored by Bloomreach. Bloomreach is a cloud-based e-commerce experience platform and B2B service specializing in marketing automation, product discovery, and content management systems.   Check out Bloomreach: https://www.bloomreach.com Explore Loomi AI: https://www.bloomreach.com/en/products/loomi Other Bloomreach products: https://www.bloomreach.com/en/products     In this episode of the Eye on AI podcast, we sit down with Pedro Domingos, professor of computer science and author of The Master Algorithm and 2040, to dive deep into the future of artificial intelligence, machine learning, and AI governance.   Pedro shares his expertise in AI, offering a unique perspective on the real dangers and potential of AI, far from the apocalyptic fears of superintelligence taking over. We explore his satirical novel, 2040, where an AI candidate for president—Prezibot—raises questions about control, democracy, and the flaws in both AI systems and human decision-makers.   Throughout the episode, Pedro sheds light on Silicon Valley's utopian dreams clashing with its dystopian realities, highlighting the contrast between tech innovation and societal challenges like homelessness. He discusses how AI has already integrated into our daily lives, from recommendation systems to decision-making tools, and what this means for the future.   We also unpack the ongoing debate around AI safety, the limits of current AI models like ChatGPT, and why he believes AI is more of a tool to amplify human intelligence rather than an existential threat. Pedro offers his insights into the future of AI development, focusing on how symbolic AI and neural networks could pave the way for more reliable and intelligent systems.   Don't forget to like, subscribe, and hit the notification bell to stay updated on the latest insights into AI, machine learning, and tech culture.   Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI     (00:00) Preview and Introduction (01:06) Pedro's Background and Contributions to AI (03:36) The Satirical Take on AI in '2040' (05:42) AI Safety Debate: Geoffrey Hinton vs. Yann LeCun (08:06) Debunking AI's Real Risks (12:45) Satirical Elements in '2040': HappyNet and Prezibot (17:57) AI as a Decision-Making Tool: Potential and Risks (22:55) The Limits of AI as an Arbiter of Truth (27:35) Crowdsourced AI: PreziBot 2.0 and Real-Time Decision Making (29:54) AI Governance and the Kill Switch Debate (37:42) Integrating AI into Society: Challenges and Optimism (47:11) Pedro's Current Research and Future of AI (55:17) Scaling AI and the Future of Reinforcement Learning  

Our guest today is Pedro Domingos, who is joining an elite group of repeat guests – he joined us before in episode 34 in April 2023.Pedro is Professor Emeritus Of Computer Science and Engineering at the University of Washington. He has done pioneering work in machine learning, like the development of Markov logic networks, which combine probabilistic reasoning with first-order logic. He is probably best known for his book "The Master Algorithm" which describes five different "tribes" of AI researchers, and argues that progress towards human-level general intelligence requires a unification of their approaches.More recently, Pedro has become a trenchant critic of what he sees as exaggerated claims about the power and potential of today's AI, and of calls to impose constraints on it.He has just published “2040: A Silicon Valley Satire”, a novel which ridicules Big Tech and also American politics.Selected follow-ups:Pedro Domingos - University of WashingtonPrevious London Futurists Podcast episode featuring Pedro Domingos2040: A Silicon Valley SatireThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our WorldThe Bonfire of the VanitiesRon HowardMike JudgeMartin ScorsesePandora's BrainTranscendenceFuture of Life Institute moratorium open letterOpenAI working on new reasoning technology under code name ‘Strawberry'Artificial Intelligence: A Modern Approach - by Stuart Russell and Peter NorvigGoogle's AI reasons its way around the London Underground - NatureConsciumIs LaMDA Sentient? — an Interview - by Blake LemoineCould a Large Language Model be Conscious? - Talk by David Chalmers at NeurIPS 2022Jeremy BenthamThe Extended Phenotype - 1982 book by Richard DawkinsClarion West: Workshops for people who are serious about writingMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

As the author of the bestselling Master Algorithm, University of Washington professor Pedro Domingos is one of the world's most respected AI experts. So I was a little surprised that his new book, 2040, is a science-fictional satire of American politics & Silicon Valley. In 2040, Domingos is, however, also using fiction to write a critique of the current Silicon Valley mania for AI. The book is both a warning about the technological limits of AI as well as an investigation of the way that it could truly democratize American politics. And so, by 2040, Domingos promised me, we really might be close to the reality of what he calls “an agora in a Presibot”. Pedro Domingos is a leading AI researcher and the author of the worldwide bestseller "The Master Algorithm", an introduction to machine learning for a general audience. He is a professor of computer science at the University of Washington in Seattle. He won the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI, and is a Fellow of AAAS and AAAI. He has written for the Wall Street Journal, Scientific American, Wired, and others, and is a highly sought-after speaker.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe

Professor Pedro Domingos, is an AI researcher and professor of computer science. He expresses skepticism about current AI regulation efforts and argues for faster AI development rather than slowing it down. He also discusses the need for new innovations to fulfil the promises of current AI techniques. MLST is sponsored by Brave: The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmented generation. Try it now - get 2,000 free queries monthly at http://brave.com/api. Show notes: * Domingos' views on AI regulation and why he believes it's misguided * His thoughts on the current state of AI technology and its limitations * Discussion of his novel "2040", a satirical take on AI and tech culture * Explanation of his work on "tensor logic", which aims to unify neural networks and symbolic AI * Critiques of other approaches in AI, including those of OpenAI and Gary Marcus * Thoughts on the AI "bubble" and potential future developments in the field Prof. Pedro Domingos: https://x.com/pmddomingos 2040: A Silicon Valley Satire [Pedro's new book] https://amzn.to/3T51ISd TOC: 00:00:00 Intro 00:06:31 Bio 00:08:40 Filmmaking skit 00:10:35 AI and the wisdom of crowds 00:19:49 Social Media 00:27:48 Master algorithm 00:30:48 Neurosymbolic AI / abstraction 00:39:01 Language 00:45:38 Chomsky 01:00:49 2040 Book 01:18:03 Satire as a shield for criticism? 01:29:12 AI Regulation 01:35:15 Gary Marcus 01:52:37 Copyright 01:56:11 Stochastic parrots come home to roost 02:00:03 Privacy 02:01:55 LLM ecosystem 02:05:06 Tensor logic Refs: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World [Pedro Domingos] https://amzn.to/3MiWs9B Rebooting AI: Building Artificial Intelligence We Can Trust [Gary Marcus] https://amzn.to/3AAywvL Flash Boys [Michael Lewis] https://amzn.to/4dUGm1M

What are the limitations and promises of artificial intelligence? How do we employ it effectively? On this episode, Dr. Pedro Domingos discussed his book, 2040, A Silicon Valley Satire.

Pedro Domingos discusses with Ivan six things which should be better known. Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. He is the author of the best-selling book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies. His new book is 2040: A Silicon Valley Satire at https://2040novel.com/. Moravec's paradox: what seems hard for AI is easy and vice-versa. https://www.scienceabc.com/innovation/what-is-moravecs-paradox-definition.html Automation creates more jobs than it destroys, and AI is no exception. https://www.paltron.com/insights-en/does-ai-create-more-jobs-than-it-destroys John von Neumann was the greatest genius of the 20th century. https://www.nytimes.com/2022/02/23/books/review-man-from-future-john-von-neumann-ananyo-bhattacharya.html Olaf Stapledon's "Star Maker" is the greatest science fiction novel of all time. https://yardsaleofthemind.wordpress.com/2021/08/25/olaf-stapledons-star-maker-book-review/ "Her" is that rare thing: a realistic depiction of AI in a movie. https://www.wired.com/story/spike-jonze-her-10-year-anniversary-artificial-intelligence/ Portugal's discoveries in the 15th and 16th centuries started the age of globalization. https://www.history.com/news/portugal-age-exploration This podcast is powered by ZenCast.fm

In this episode, Pedro Domingos - AI - 2040 - Lauren Hawker Zafer is joined by Pedro Domingos. This unique conversation explores AI's impact on politics, particularly in voter targeting and campaign strategies, and the concept of AI as a tool for enhancing collective intelligence. Domingos, with over 200 technical publications and numerous accolades, shares insights on the future of AI, its challenges, and opportunities. Who is Pedro Domingos? Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. He is the author of the best-selling book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies. He won the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. Domingos is Fellow of the AAAS and AAAI and received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Pedro received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST in Lisbon and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He's a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was the program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. His work has been featured in the Wall Street Journal, Spectator, Scientific American, Wired, and elsewhere. Lastly, Domingos helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. He lives in Seattle. #ai #techpodcast #redefiningai #squirro

Author: Pedro Domingos Book: 2040: A Political Satire Publishing: BookBaby (August 20, 2024) Synopsis (from the Publisher): “I told you not to read books like this.” ―Your Mom When AI and the culture wars collide, hilarity ensues. The 2040 presidential election is unlike any in US history. The Republican candidate is an AI named PresiBot, […] The post PEDRO DOMINGOS – 2040: A Silicon Valley Satire appeared first on KSCJ 1360.

Season Three - Spotlight Thirteen Our thirteenth spotlight of this season is a snippet of our upcoming episode: Pedro Domingos - AI - 2040 Join host Lauren Hawker Zafer as she engages with Pedro Domingos. This unique conversation explores AI's impact on politics, particularly in voter targeting and campaign strategies, and the concept of AI as a tool for enhancing collective intelligence. Domingos, with over 200 technical publications and numerous accolades, shares insights on the future of AI, its challenges, and opportunities. Who is Pedro Domingos? Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. He is the author of the best-selling book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies. He won the SIGKDD Innovation Award and theIJCAI John McCarthy Award, two of the highesthonors in data science and AI. Domingos is Fellow of the AAAS and AAAI and received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Pedro received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST in Lisbon and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He's a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was the program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. His work has been featured in the Wall Street Journal, Spectator, Scientific American, Wired, and elsewhere. Lastly, Domingos helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. He lives in Seattle. #ai #techpodcast #redefiningai #squirro

This and all episodes at: https://aiandyou.net/ . Mounir Shita, CEO of Kimera Systems, is author of the upcoming book The Science of Intelligence, which contains some interesting and thought-provoking explorations of intelligence that had me thinking about Pedro Domingos' book The Master Algorithm. We talk about theories of AGI, free will, egg smashing, and Mounir's prototype smartphone app that learned how to silence itself in a movie theater! All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.          

Pedro Domingos, professor de Ciências da Computação na Universidade de Washington, e conversa com Fernando Alvim sobre algoritmos e Inteligência Artificial.

In this episode of the AdC's CompCast - Competition Talks, Pedro Domingos ponders on the question "Should AI be regulated?". Pedro Domingos is a leading researcher in the field of AI and author of the worldwide bestseller "The Master Algorithm Revolution". He is a professor of Computer Science at the University of Washington and was the founding director of the D. E. Shaw machine learning laboratory.

Eles estão por todo o lado, nas diversas formas tecnológicas em que a nossa rotina mergulhou. Para uns, os algoritmos representam a expressão da oportunidade de acelerar ainda mais o conhecimento e a prosperidade. Para outros, são mecanismos que têm que ser controlados e, no mínimo, regulados para evitar impactos indesejados nas sociedades. Este é o tema que junta, neste «Da Capa à Contracapa», Diogo Queiroz de Andrade, autor do livro «Algoritmos, uma Revolução em Curso» e Pedro Domingos, investigador em Inteligência Artificial da Universidade de Washington. 

Pedro Domingos é professor emérito de Ciências da Computação na Universidade de Washington. Licenciou-se pelo Instituto Superior Técnico e doutorou-se na Universidade da Califórnia em Irvine. Recebeu em 2014 o prémio de inovação, SIGKDD, o mais prestigiado na área de ciências de dados. É autor do livro «A Revolução do Algoritmo Mestre - Como a aprendizagem automática está a mudar o mundo», publicado em 2015. -> Apoie este podcast e faça parte da comunidade de mecenas do 45 Graus em: 45grauspodcast.com -> Inscreva-se aqui nos workshops de Pensamento Crítico em Coimbra e Braga. -> Registe-se aqui para ser avisado(a) de futuras edições dos workshops. _______________ Índice (com timestamps): (04:32) INÍCIO - O que é revolucionário no Machine Learning? | As cinco famílias de modelos (as ‘cinco tribos'): conectivistas (backprop, a tecnologia por trás do ChatGPT), simbolistas, evolucionistas, bayesianos e analogistas. (27:25) Porque é que a robótica tem avançado mais lentamente? (32:10) Como funcionam os modelos conectivistas de deep learning (como o ChatGPT)? | Large language models | Transformers | Alexnet  (40:11) O que entendes por ‘Algoritmo Mestre'? | Como unificar as várias famílias de modelos para chegar à Inteligência Artificial Geral? (55:47) O que é especial no cérebro humano que nos permite generalizar melhor do que a AI? | Algoritmo que conseguiu descobrir as Leis de Kepler | Livro Leonardo da Vinci, de Walter Isaacson. | Livro Analogy as the Fuel and Fire of Thinking, de Douglas R Hofstadter (1:07:47) A IA pode tornar-se perigosa? Vem aí a  singularidade? | Yuval Harari, Elon Musk | Cientistas sérios que se preocupam com o tema.  Livro recomendado: Godel, Escher, Bach, de Douglas Hofstadter (1:32:20) O que explica um engenheiro da Google ter afirmado que o chatbot tinha consciência? _______________ Desde que foi lançado, no final do ano passado, o ChatGPT trouxe o tema da IA de novo para a discussão. Já tardava, por isso, um episódio sobre o tema. E dificilmente poderia pedir melhor convidado.  _______________ Obrigado aos mecenas do podcast: Francisco Hermenegildo, Ricardo Evangelista, Henrique Pais João Baltazar, Salvador Cunha, Abilio Silva, Tiago Leite, Carlos Martins, Galaró family, Corto Lemos, Miguel Marques, Nuno Costa, Nuno e Ana, João Ribeiro, Helder Miranda, Pedro Lima Ferreira, Cesar Carpinteiro, Luis Fernambuco, Fernando Nunes, Manuel Canelas, Tiago Gonçalves, Carlos Pires, João Domingues, Hélio Bragança da Silva, Sandra Ferreira , Paulo Encarnação , BFDC, António Mexia Santos, Luís Guido, Bruno Heleno Tomás Costa, João Saro, Daniel Correia, Rita Mateus, António Padilha, Tiago Queiroz, Carmen Camacho, João Nelas, Francisco Fonseca, Rafael Santos, Andreia Esteves, Ana Teresa Mota, ARUNE BHURALAL, Mário Lourenço, RB, Maria Pimentel, Luis, Geoffrey Marcelino, Alberto Alcalde, António Rocha Pinto, Ruben de Bragança, João Vieira dos Santos, David Teixeira Alves, Armindo Martins , Carlos Nobre, Bernardo Vidal Pimentel, António Oliveira, Paulo Barros, Nuno Brites, Lígia Violas, Tiago Sequeira, Zé da Radio, João Morais, André Gamito, Diogo Costa, Pedro Ribeiro, Bernardo Cortez Vasco Sá Pinto, David , Tiago Pires, Mafalda Pratas, Joana Margarida Alves Martins, Luis Marques, João Raimundo, Francisco Arantes, Mariana Barosa, Nuno Gonçalves, Pedro Rebelo, Miguel Palhas, Ricardo Duarte, Duarte , Tomás Félix, Vasco Lima, Francisco Vasconcelos, Telmo , José Oliveira Pratas, Jose Pedroso, João Diogo Silva, Joao Diogo, José Proença, João Crispim, João Pinho , Afonso Martins, Robertt Valente, João Barbosa, Renato Mendes, Maria Francisca Couto, Antonio Albuquerque, Ana Sousa Amorim, Francisco Santos, Lara Luís, Manuel Martins, Macaco Quitado, Paulo Ferreira, Diogo Rombo, Francisco Manuel Reis, Bruno Lamas, Daniel Almeida, Patrícia Esquível , Diogo Silva, Luis Gomes, Cesar Correia, Cristiano Tavares, Pedro Gaspar, Gil Batista Marinho, Maria Oliveira, João Pereira, Rui Vilao, João Ferreira, Wedge, José Losa, Hélder Moreira, André Abrantes, Henrique Vieira, João Farinha, Manuel Botelho da Silva, João Diamantino, Ana Rita Laureano, Pedro L, Nuno Malvar, Joel, Rui Antunes7, Tomás Saraiva, Cloé Leal de Magalhães, Joao Barbosa, paulo matos, Fábio Monteiro, Tiago Stock, Beatriz Bagulho, Pedro Bravo, Antonio Loureiro, Hugo Ramos, Inês Inocêncio, Telmo Gomes, Sérgio Nunes, Tiago Pedroso, Teresa Pimentel, Rita Noronha, miguel farracho, José Fangueiro, Zé, Margarida Correia-Neves, Bruno Pinto Vitorino, João Lopes, Joana Pereirinha, Gonçalo Baptista, Dario Rodrigues, tati lima, Pedro On The Road, Catarina Fonseca, JC Pacheco, Sofia Ferreira, Inês Ribeiro, Miguel Jacinto, Tiago Agostinho, Margarida Costa Almeida, Helena Pinheiro, Rui Martins, Fábio Videira Santos, Tomás Lucena, João Freitas, Ricardo Sousa, RJ, Francisco Seabra Guimarães, Carlos Branco, David Palhota, Carlos Castro, Alexandre Alves, Cláudia Gomes Batista, Ana Leal, Ricardo Trindade, Luís Machado, Andrzej Stuart-Thompson, Diego Goulart, Filipa Portela, Paulo Rafael, Paloma Nunes, Marta Mendonca, Teresa Painho, Duarte Cameirão, Rodrigo Silva, José Alberto Gomes, Joao Gama, Cristina Loureiro, Tiago Gama, Tiago Rodrigues, Miguel Duarte, Ana Cantanhede, Artur Castro Freire, Rui Passos Rocha, Pedro Costa Antunes, Sofia Almeida, Ricardo Andrade Guimarães, Daniel Pais, Miguel Bastos, Luís Santos _______________ Esta conversa foi editada por: Hugo Oliveira

Pedro Domingos é professor emérito de Ciências da Computação na Universidade de Washington. Licenciou-se pelo Instituto Superior Técnico e doutorou-se na Universidade da Califórnia em Irvine. Recebeu em 2014 o prémio de inovação, SIGKDD, o mais prestigiado na área de ciências de dados. É autor do livro «A Revolução do Algoritmo Mestre - Como a aprendizagem automática está a mudar o mundo», publicado em 2015. -> Apoie este podcast e faça parte da comunidade de mecenas do 45 Graus em: 45grauspodcast.com

Time for a tech lesson! Abby is joined by Professor of Computer Science at the University of Washington Dr. Pedro Domingos for a lesson about algorithms. Dr. Domingos defines what an algorithm is and explains the precision and hard work that goes into creating them. He provides insight on the broad application of algorithms and shares how they're used in animation, machinery, websites, and more. Later, Dr. Domingos looks ahead to the future of machine learning and artificial intelligence and discusses how they could change how algorithms are used and developed. Learn more about your ad choices. Visit megaphone.fm/adchoices

Descubre el Algoritmo Maestro en "El Algoritmo Maestro" de Pedro Domingos. Aprendizaje Automático e Inteligencia Artificial. En este episodio, exploraremos el revolucionario libro que revela el secreto detrás del aprendizaje automático y cómo las máquinas pueden superar la inteligencia humana. Aprende sobre los enfoques simbólico, conexionista, evolutivo, bayesiano y analógico, y cómo se combinan para impulsar el futuro de la IA. Reflexiona sobre las implicaciones éticas y sociales mientras desentrañamos el misterio de la IA. ¡No te pierdas esta oportunidad única de conocer el algoritmo que transformará nuestra sociedad! Haz clic ahora y sumérgete en "El Algoritmo Maestro" para desbloquear el potencial de la inteligencia artificial.

The AI Asia Pacific Institute (AIAPI) is hosting a series of conversations with leading artificial intelligence (AI) experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi, as well as our internal advisory board and research affiliates. We have published a briefing note outlining some of the critical risks of generative AI and highlighting potential concerns.  The following is a conversation with Dr. Luciano Floridi.  Dr. Luciano Floridi holds a double appointment as professor of philosophy and ethics of information at the University of Oxford, Oxford Internet Institute where he is also Governing Body Fellow of Exeter College, Oxford, and as Professor of Sociology of Culture and Communication at the University of Bologna, Department of Legal Studies, where he is the director of the Centre for Digital Ethics. He is adjunct professor ("distinguished scholar in residence"), Department of Economics, American University, Washington D.C. Dr. Floridi is best known for his work on two areas of philosophical research: the philosophy of information, and information ethics (also known as digital ethics or computer ethics), for which he received many awards, including the Knight of the Grand Cross of the Order of Merit, Italy's most prestigious honour. According to Scopus, Floridi was the most cited living philosopher in the world in 2020.Between 2008 and 2013, he held the research chair in philosophy of information and the UNESCO Chair in Information and Computer Ethics at the University of Hertfordshire. He was the founder and director of the IEG, an interdepartmental research group on the philosophy of information at the University of Oxford, and of the GPI the research Group in Philosophy of Information at the University of Hertfordshire. He was the founder and director of the SWIF, the Italian e-journal of philosophy (1995–2008). He is a former Governing Body Fellow of St Cross College, Oxford. *** For show notes and past guests, please visit https://aiasiapacific.org/podcast/ For questions, please contact us at contact@aiasiapacific.org or follow us on Twitter or Instagram to stay in touch.

The AI Asia Pacific Institute (AIAPI) is hosting a series of conversations with leading artificial intelligence (AI) experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi, as well as our internal advisory board and research affiliates. We have published a briefing note outlining some of the critical risks of generative AI and highlighting potential concerns. The following is a conversation with Dr. Pedro Domingos.  Dr. Pedro Domingos is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. He is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is a Fellow of the AAAS and AAAI, and has received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Dr. Domingos received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas. He is a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Dr. Domingos was program co-chair of KDD-2003 and SRL-2009, and served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. He has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. He helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. *** For show notes and past guests, please visit https://aiasiapacific.org/podcast/ For questions, please contact us at contact@aiasiapacific.org or follow us on Twitter or Instagram to stay in touch.

The AI Asia Pacific Institute (AIAPI) has hosted a series of conversations with leading artificial intelligence (AI) experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi, as well as our internal advisory board and research affiliates. The following is a conversation with Dr. Toby Walsh and Dr. Stuart Russell.  Dr. Toby Walsh is Chief Scientist at UNSW.ai, UNSW's new AI Institute. He is a Laureate Fellow and Scientia Professor of Artificial Intelligence in the School of Computer Science and Engineering at UNSW Sydney, and he is also an adjunct fellow at CSIRO Data61. He was named by the Australian newspaper as a "rock star" of Australia's digital revolution. He has been elected a fellow of the Australian Academy of Science, a fellow of the ACM, the Association for the Advancement of Artificial Intelligence (AAAI) and of the European Association for Artificial Intelligence. He has won the prestigious Humboldt Prize as well as the NSW Premier's Prize for Excellence in Engineering and ICT, and the ACP Research Excellence award. He has previously held research positions in England, Scotland, France, Germany, Italy, Ireland and Sweden. He has played a leading role at the UN and elsewhere on the campaign to ban lethal autonomous weapons (aka "killer robots"). His advocacy in this area has led to him being "banned indefinitely" from Russia. Dr. Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI and the Kavli Center for Ethics, Science, and the Public. He is a recipient of the IJCAI Computers and Thought Award and Research Excellence Award and held the Chaire Blaise Pascal in Paris. In 2021 he received the OBE from Her Majesty Queen Elizabeth and gave the Reith Lectures. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book "Artificial Intelligence: A Modern Approach" (with Peter Norvig) is the standard text in AI, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with a current emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons. *** For show notes and past guests, please visit https://aiasiapacific.org/podcast/ For questions, please contact us at contact@aiasiapacific.org or follow us on Twitter or Instagram to stay in touch.

"Learn to use AI. That's, that's my message. You wanna learn to use AI as a professional and as a citizen in your personal life. The more you know how to use it, the better you'll make of it, the better your life will be. AI gives power; like any technology, it gives power to those who understand it and use it" - Pedro Domingos Recent developments in AI, specifically consumer-facing generative AIs, are helping people create a lot of cool content while also generating a ton of concern. A big bucket of that concern is AI alignment - what are the possible unintended consequences to humans? The internet transformed our relationship to information, but it took a few years; now, AI is doing it in real time.  My guest on this episode is Professor Pedro Domingos.  Pedro is a leading AI researcher and the author of the worldwide bestseller "The Master Algorithm." He is a professor of computer science at the University of Washington in Seattle. He won the Special Interest Group on Knowledge Discovery and Data Mining Innovation Award and the international joint Conference on AI John McCarthy Award, two of the highest honors in data science and AI. Pedro helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.  On this episode, we run the gamut to include... Where are we with generative AIs Pedro demystifies LLMs (Large Language Models) Progress and problems with generative AIs Hallucination in AI - and Illusion in humans The homunculus fallacy Risks, regulations, known-unknowns Comments on existential threats The S curve in emerging technologies like AI AI's possible impact on employment and the economy Artificial General Intellience or AGI Goals and end games, is AGI the goal? Does he think LLMs AI's like ChatGPT are conscious? No matter your technical level, you'll enjoy this discussion with Pedro. He is passionate about the subject matter, no surprise - much of what he's predicted has come to pass in the field, And if you feel a tinge of AI anxiety, consider this a bit of exposure therapy.  Listen and learn more about how these systems work and how they might impact your life.  For show notes and more, visit larryweeks.com  

On March 14th, OpenAI launched GPT-4 , which took the world by surprise and storm. Almost everybody, including people within the AI community, was stunned by its capabilities. A week later, the Future of Life Institute (FLI) published an open letter calling on the world's AI labs to pause the development of larger versions of GPT (generative pre-trained transformer) models until their safety can be ensured.Recent episodes of this podcast have presented arguments for and against this call for a moratorium. Jaan Tallin, one of the co-founders of FLI, made the case in favour. Pedro Domingos, an eminent AI researcher, and Kenn Cukier, a senior editor at The Economist, made variants of the case against. In this episode, co-hosts Calum Chace and David Wood highlight some key implications and give our own opinions. Expect some friendly disagreements along the way.Follow-up reading:https://futureoflife.org/open-letter/pause-giant-ai-experiments/https://www.metaculus.com/questions/3479/date-weakly-general-ai-is-publicly-known/Topics addressed in this episode include:*) Definitions of Artificial General Intelligence (AGI)*) Many analysts knowledgeable about AI have recently brought forward their estimates of when AGI will become a reality*) The case that AGI poses an existential risk to humanity*) The continued survival of the second smartest species on the planet depends entirely on the actions of the actual smartest species*) One species can cause another to become extinct, without that outcome being intended or planned*) Four different ways in which advanced AI could have terrible consequences for humanity: bugs in the implementation; the implementation being hacked (or jail broken); bugs in the design; and the design being hacked by emergent new motivations*) Near future AIs that still fall short of being AGI could have effects which, whilst not themselves existential, would plunge society into such a state of dysfunction and distraction that we are unable to prevent subsequent AGI-induced disaster*) Calum's "4 C's" categorisation of possible outcomes regarding AGI existential risks: Cease, Control, Catastrophe, and Consent*) 'Consent' means a superintelligence decides that we humans are fun, enjoyable, interesting, worthwhile, or simply unobjectionable, and consents to let us carry on as we are, or to help us, or to allow us to merge with it*) The 'Control' option arguably splits into "control while AI capabilities continue to proceed at full speed" and "control with the help of a temporary pause in the development of AI capabilities"*) Growing public support for stopping AI development - driven by a sense of outrage that the future of humanity is seemingly being decided by a small number of AI lab executives*) A comparison with how the 1983 film "The Day After" triggered a dramatic change in public opinion regarding the nuclear weapons arms race*) How much practical value could there be in a six-month pause? Or will the six-months be extended into an indefinite ban?*) Areas where there could be at least some progress: methods to validate the output of giant AI models, and choices of initial configurations that would make the 'Consent' scenario more likely*) Designs that might avoid the emergence of agency (convergent instrumental goals) within AI models as they acquire more intelligence*) Why 'Consent' might be the most likely outcome*) The longer a ban remains in place, the larger the risks of bad actors building AGIs*) Contemplating how to secure the best upsides - an "AI summer" - from advanced AIsMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Generative Artificial Intelligence systems have significantly advanced in recent years, enabling machines to generate highly realistic content such as text, images, and audio. While these advancements offer numerous benefits, it is critical that we are aware of the associated risks. The AI Asia Pacific Institute has hosted a series of conversations with leading AI experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi. Join us for season 5 of this Podcast.Subscribe now, wherever you are listening to join these conversations.

Should the pace of research into advanced artificial intelligence be slowed down, or perhaps even paused completely?Your answer to that question probably depends on your answers to a number of other questions. Is advanced artificial intelligence reaching the point where it could result in catastrophic damage? Is a slow down desirable, given that AI can also lead to lots of very positive outcomes, including tools to guard against the worst excesses of other applications of AI? And even if a slow down is desirable, is it practical?Our guest in this episode is Professor Pedro Domingos of the University of Washington. He is perhaps best known for his book "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World".That book takes an approach to the future of AI that is significantly different from what you can read in many other books. It describes five different "tribes" of AI researchers, each with their own paradigms, and it suggests that true progress towards human-level general intelligence will depend on a unification of these different approaches. In other words, we won't reach AGI just by scaling up deep learning approaches, or even by adding in features from logical reasoning.Follow-up reading:https://homes.cs.washington.edu/~pedrod/https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543https://futureoflife.org/open-letter/pause-giant-ai-experiments/Topics addressed in this episode include:*) The five tribes of AI research - why there's a lot more to AI than deep learning*) Why unifying these five tribes may not be sufficient to reach human-level general intelligence*) The task of understanding an entire concept (e.g 'horse') from just seeing a single example*) A wide spread of estimates of the timescale to reach AGI*) Different views as to the true risks from advanced AI*) The case that risks arise from AI incompetence rather than from increased AI competence*) A different risk: that bad actors will gain dangerously more power from access to increasingly competent AI*) The case for using AI to prevent misuse of AI*) Yet another risk: that an AI trained against one objective function will nevertheless adopt goals diverging from that objective*) How AIs that operate beyond our understanding could still remain under human control*) How fully can evolution be trusted to produce outputs in line with a specified objective function?*) The example of humans taming wolves into dogs that pose no threat to us*) The counterexample of humans pursuing goals contrary to our in-built genetic drives*) Complications with multiple levels of selection pressures, e.g genes and memes working at cross purposes*) The “genie problem” (or “King Midas problem”) of choosing an objective function that is apparently attractive but actually dangerous*) Assessing the motivations of people who have signed the FLI (Future of Life Institute) letter advocating a pause on the development of larger AI language models*) Pros and cons of escalating a sense of urgency*) The two key questions of existential risk from AI: how much risk is acceptable, and what might that level of risk become in the near future?*) The need for a more rational discussion of the issues raised by increasingly competent AIsMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration

Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 Christopher Summerfield, Department of Experimental Psychology, University of Oxford is a Professor of Cognitive Neuroscience at the University of Oxford and a Research Scientist at Deepmind UK. His work focusses on the neural and computational mechanisms by which humans make decisions. Chris has just released an incredible new book on AI called "Natural General Intelligence". It's my favourite book on AI I have read so so far. The book explores the algorithms and architectures that are driving progress in AI research, and discusses intelligence in the language of psychology and biology, using examples and analogies to be comprehensible to a wide audience. It also tackles longstanding theoretical questions about the nature of thought and knowledge. With Chris' permission, I read out a summarised version of Chapter 2 from his book on which was on Intelligence during the 30 minute MLST introduction. Buy his book here: https://global.oup.com/academic/product/natural-general-intelligence-9780192843883?cc=gb&lang=en& YT version: https://youtu.be/31VRbxAl3t0 Interviewer: Dr. Tim Scarfe TOC: [00:00:00] Walk and talk with Chris on Knowledge and Abstractions [00:04:08] Intro to Chris and his book [00:05:55] (Intro) Tim reads Chapter 2: Intelligence [00:09:28] Intro continued: Goodhart's law [00:15:37] Intro continued: The "swiss cheese" situation [00:20:23] Intro continued: On Human Knowledge [00:23:37] Intro continued: Neats and Scruffies [00:30:22] Interview kick off [00:31:59] What does it mean to understand? [00:36:18] Aligning our language models [00:40:17] Creativity [00:41:40] "Meta" AI and basins of attraction [00:51:23] What can Neuroscience impart to AI [00:54:43] Sutton, neats and scruffies and human alignment [01:02:05] Reward is enough [01:19:46] Jon Von Neumann and Intelligence [01:23:56] Compositionality References: The Language Game (Morten H. Christiansen, Nick Chater https://www.penguin.co.uk/books/441689/the-language-game-by-morten-h-christiansen-and--nick-chater/9781787633483 Theory of general factor (Spearman) https://www.proquest.com/openview/7c2c7dd23910c89e1fc401e8bb37c3d0/1?pq-origsite=gscholar&cbl=1818401 Intelligence Reframed (Howard Gardner) https://books.google.co.uk/books?hl=en&lr=&id=Qkw4DgAAQBAJ&oi=fnd&pg=PT6&dq=howard+gardner+multiple+intelligences&ots=ERUU0u5Usq&sig=XqiDgNUIkb3K9XBq0vNbFmXWKFs#v=onepage&q=howard%20gardner%20multiple%20intelligences&f=false The master algorithm (Pedro Domingos) https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543 A Thousand Brains: A New Theory of Intelligence (Jeff Hawkins) https://www.amazon.co.uk/Thousand-Brains-New-Theory-Intelligence/dp/1541675819 The bitter lesson (Rich Sutton) http://www.incompleteideas.net/IncIdeas/BitterLesson.html

Glenn brings in chief researcher Jason Buttrill to discuss America's alleged role in the attack on Russia's Nord Stream pipelines after an anonymous source claimed President Biden ordered the bombing. Could America be responsible for such an act of war? University of Washington professor emeritus Pedro Domingos joins to expose artificial intelligence as being the greatest authoritarian tool ever created and the possible deadly consequences. Will activists eventually demand civil rights for artificial intelligence? Glenn discusses the lie surrounding the possibility of a perfect utopian society. Bill O'Reilly joins for his weekly news recap, discussing Biden's possible role in the attack on the Nord Stream pipelines and what everybody missed in Biden's State of the Union address. Learn more about your ad choices. Visit megaphone.fm/adchoices

Glenn brings in chief researcher Jason Buttrill to discuss America's alleged role in the attack on Russia's Nord Stream pipelines after an anonymous source claimed President Biden ordered the bombing. Could America be responsible for such an act of war? University of Washington professor emeritus Pedro Domingos joins to expose artificial intelligence as being the greatest authoritarian tool ever created and the possible deadly consequences. Bill O'Reilly joins for his weekly news recap, discussing Biden's possible role in the attack on the Nord Stream pipelines and what everybody missed in Biden's State of the Union address. Learn more about your ad choices. Visit megaphone.fm/adchoices

Pedro Domingos, Professor Emeritus of Computer Science and Engineering at the University of Washington, is renowned for his research in machine learning, particularly for his work on Markov logic networks that allow for uncertain inference. He is also the author of the acclaimed book "The Master Algorithm". Panel: Dr. Tim Scarfe TOC: [00:00:00] Introduction [00:01:34] Galaxtica / misinformation / gatekeeping [00:12:31] Is there a master algorithm? [00:16:29] Limits of our understanding [00:21:57] Intentionality, Agency, Creativity [00:27:56] Compositionality [00:29:30] Digital Physics / It from bit / Wolfram [00:35:17] Alignment / Utility functions [00:43:36] Meritocracy [00:45:53] Game theory [01:00:00] EA/consequentialism/Utility [01:11:09] Emergence / relationalism [01:19:26] Markov logic [01:25:38] Moving away from anthropocentrism [01:28:57] Neurosymbolic / infinity / tensor algerbra [01:53:45] Abstraction [01:57:26] Symmetries / Geometric DL [02:02:46] Bias variance trade off [02:05:49] What seen at neurips [02:12:58] Chalmers talk on LLMs [02:28:32] Definition of intelligence [02:32:40] LLMs [02:35:14] On experts in different fields [02:40:15] Back to intelligence [02:41:37] Spline theory / extrapolation YT version: https://www.youtube.com/watch?v=C9BH3F2c0vQ References; The Master Algorithm [Domingos] https://www.amazon.co.uk/s?k=master+algorithm&i=stripbooks&crid=3CJ67DCY96DE8&sprefix=master+algorith%2Cstripbooks%2C82&ref=nb_sb_noss_2 INFORMATION, PHYSICS, QUANTUM: THE SEARCH FOR LINKS [John Wheeler/It from Bit] https://philpapers.org/archive/WHEIPQ.pdf A New Kind Of Science [Wolfram] https://www.amazon.co.uk/New-Kind-Science-Stephen-Wolfram/dp/1579550088 The Rationalist's Guide to the Galaxy: Superintelligent AI and the Geeks Who Are Trying to Save Humanity's Future [Tom Chivers] https://www.amazon.co.uk/Does-Not-Hate-You-Superintelligence/dp/1474608795 The Status Game: On Social Position and How We Use It [Will Storr] https://www.goodreads.com/book/show/60598238-the-status-game Newcomb's paradox https://en.wikipedia.org/wiki/Newcomb%27s_paradox The Case for Strong Emergence [Sabine Hossenfelder] https://philpapers.org/rec/HOSTCF-3 Markov Logic: An Interface Layer for Artificial Intelligence [Domingos] https://www.morganclaypool.com/doi/abs/10.2200/S00206ED1V01Y200907AIM007 Note; Pedro discussed “Tensor Logic” - I was not able to find a reference Neural Networks and the Chomsky Hierarchy [Grégoire Delétang/DeepMind] https://arxiv.org/abs/2207.02098 Connectionism and Cognitive Architecture: A Critical Analysis [Jerry A. Fodor and Zenon W. Pylyshyn] https://ruccs.rutgers.edu/images/personal-zenon-pylyshyn/proseminars/Proseminar13/ConnectionistArchitecture.pdf Every Model Learned by Gradient Descent Is Approximately a Kernel Machine [Pedro Domingos] https://arxiv.org/abs/2012.00152 A Path Towards Autonomous Machine Intelligence Version 0.9.2, 2022-06-27 [LeCun] https://openreview.net/pdf?id=BZ5a1r-kVsf Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges [Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković] https://arxiv.org/abs/2104.13478 The Algebraic Mind: Integrating Connectionism and Cognitive Science [Gary Marcus] https://www.amazon.co.uk/Algebraic-Mind-Integrating-Connectionism-D

ChatGPT is both fascinating and weird.Check The Lead-Lag Report on your favorite social networks.Twitter: https://twitter.com/leadlagreportYouTube: https://www.youtube.com/c/theleadlagreportFacebook: https://www.facebook.com/leadlagreportInstagram: https://instagram.com/leadlagreport                             Sign up for The Lead-Lag Report at www.leadlagreport.com and use promo code PODCAST30 for 2 weeks free and 30% off.                              Nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities.                              The content in this program is for informational purposes only. You should not construe any information or other material as investment, financial, tax, or other advice. The views expressed by the participants are solely their own. A participant may have taken or recommended any investment position discussed, but may close such position or alter its recommendation at any time without notice. Nothing contained in this program constitutes a solicitation, recommendation, endorsement, or offer to buy or sell any securities or other financial instruments in any jurisdiction. Please consult your own investment or financial advisor for advice related to all investment decisions.See disclosures for The Lead-Lag Report here: The Lead-Lag Report (leadlagreport.com)Foodies unite…with HowUdish!It's social media with a secret sauce: FOOD! The world's first network for food enthusiasts. HowUdish connects foodies across the world!Share kitchen tips and recipe hacks. Discover hidden gem food joints and street food. Find foodies like you, connect, chat and organize meet-ups!HowUdish makes it simple to connect through food anywhere in the world.So, how do YOU dish? Download HowUdish on the Apple App Store today:

Leonard S. Johnson is the Founder and CEO of AIEDC, a 5G Cloud Mobile App Maker and Service Provider with Machine Learning to help small and midsize businesses create their own iOS and Android mobile apps with no-code or low-code so they can engage and service their customer base, as well as provide front and back office digitization services for small businesses. Victoria talks to Leonard about using artificial intelligence for good, bringing the power of AI to local economics, and truly democratizing AI. The Artificial Intelligence Economic Development Corporation (AIEDC) (https://netcapital.com/companies/aiedc) Follow AIEDC on Twitter (https://twitter.com/netcapital), Instagram (https://www.instagram.com/netcapital/), Facebook (https://www.facebook.com/Netcapital/), or LinkedIn (https://www.linkedin.com/company/aiedc/). Follow Leonard on Twitter (https://twitter.com/LeonardSJ) and LinkedIn (https://www.linkedin.com/in/leonardsjohnson84047/). Follow thoughtbot on Twitter (https://twitter.com/thoughtbot) or LinkedIn (https://www.linkedin.com/company/150727/). Become a Sponsor (https://thoughtbot.com/sponsorship) of Giant Robots! Transcript: VICTORIA: This is The Giant Robots Smashing Into Other Giant Robots Podcast, where we explore the design, development, and business of great products. I'm your host, Victoria Guido. And with us today is Leonard S. Johnson or LS, Founder and CEO AIEDC, a 5G Cloud Mobile App Maker and Service Provider with Machine Learning to help small and midsize businesses create their own iOS and Android mobile apps with no-code or low-code so they can engage and service their customer base, as well as provide front and back office digitization services for small businesses. Leonard, thanks for being with us today. LEONARD: Thank you for having me, Victoria. VICTORIA: I should say LS, thank you for being with us today. LEONARD: It's okay. It's fine. VICTORIA: Great. So tell us a little more about AIEDC. LEONARD: Well, AIEDC stands for Artificial Intelligence Economic Development Corporation. And the original premise that I founded it for...I founded it after completing my postgraduate work at Stanford, and that was 2016. And it was to use AI for economic development, and therefore use AI for good versus just hearing about artificial intelligence and some of the different movies that either take over the world, and Skynet, and watch data privacy, and these other things which are true, and it's very evident, they exist, and they're out there. But at the end of the day, I've always looked at life as a growth strategy and the improvement of what we could do and focusing on what we could do practically. You do it tactically, then you do it strategically over time, and you're able to implement things. That's why I think we keep building collectively as humanity, no matter what part of the world you're in. VICTORIA: Right. So you went to Stanford, and you're from South Central LA. And what about that background led you to pursue AI for good in particular? LEONARD: So growing up in the inner city of Los Angeles, you know, that South Central area, Compton area, it taught me a lot. And then after that, after I completed high school...and not in South Central because I moved around a lot. I grew up with a single mother, never knew my real father, and then my home life with my single mother wasn't good because of just circumstances all the time. And so I just started understanding that even as a young kid, you put your brain...you utilize something because you had two choices. It's very simple or binary, you know, A or B. A, you do something with yourself, or B, you go out and be social in a certain neighborhood. And I'm African American, so high probability that you'll end up dead, or in a gang, or in crime because that's what it was at that time. It's just that's just a situation. Or you're able to challenge those energies and put them toward a use that's productive and positive for yourself, and that's what I did, which is utilizing a way to learn. I could always pick up things when I was very young. And a lot of teachers, my younger teachers, were like, "You're very, very bright," or "You're very smart." And there weren't many programs because I'm older than 42. So there weren't as many programs as there are today. So I really like all of the programs. So I want to clarify the context. Today there's a lot more engagement and identification of kids that might be sharper, smarter, whatever their personal issues are, good or bad. And it's a way to sort of separate them. So you're not just teaching the whole group as a whole and putting them all in one basket, but back then, there was not. And so I just used to go home a lot, do a lot of reading, do a lot of studying, and just knick-knack with things in tech. And then I just started understanding that even as a young kid in the inner city, you see economics very early, but they don't understand that's really what they're studying. They see economics. They can see inflation because making two ends meet is very difficult. They may see gang violence and drugs or whatever it might end up being. And a lot of that, in my opinion, is always an underlining economic foundation. And so people would say, "Oh, why is this industry like this?" And so forth. "Why does this keep happening?" It's because they can't function. And sometimes, it's just them and their family, but they can't function because it's an economic system. So I started focusing on that and then went into the Marine Corps. And then, after the Marine Corps, I went to Europe. I lived in Europe for a while to do my undergrad studies in the Netherlands in Holland. VICTORIA: So having that experience of taking a challenge or taking these forces around you and turning into a force for good, that's led you to bring the power of AI to local economics. And is that the direction that you went eventually? LEONARD: So economics was always something that I understood and had a fascination prior to even starting my company. I started in 2017. And we're crowdfunding now, and I can get into that later. But I self-funded it since 2017 to...I think I only started crowdfunding when COVID hit, which was 2020, and just to get awareness and people out there because I couldn't go to a lot of events. So I'm like, okay, how can I get exposure? But yeah, it was a matter of looking at it from that standpoint of economics always factored into me, even when I was in the military when I was in the Marine Corps. I would see that...we would go to different countries, and you could just see the difference of how they lived and survived. And another side note, my son's mother is from Ethiopia, Africa. And I have a good relationship with my son and his mother, even though we've been apart for over 15 years, divorced for over 15 years or so or longer. But trying to keep that, you can just see this dichotomy. You go out to these different countries, and even in the military, it's just so extreme from the U.S. and any part of the U.S, but that then always focused on economics. And then technology, I just always kept up with, like, back in the '80s when the mobile brick phone came out, I had to figure out how to get one. [laughs] And then I took it apart and then put it back together just to see how it works, so yeah. But it was a huge one, by the way. I mean, it was like someone got another and broke it, and they thought it was broken. And they're like, "This doesn't work. You could take this piece of junk." I'm like, "Okay." [laughs] VICTORIA: Like, oh, great. I sure will, yeah. Now, I love technology. And I think a lot of people perceive artificial intelligence as being this super futuristic, potentially harmful, maybe economic negative impact. So what, from your perspective, can AI do for local economics or for people who may not have access to that advanced technology? LEONARD: Well, that's the key, and that's what we're looking to do with AIEDC. When you look at the small and midsize businesses, it's not what people think, or their perception is. A lot of those in the U.S. it's the backbone of the United States, our economy, literally. And in other parts of the world, it's the same where it could be a one or two mom-and-pop shops. That's where that name comes from; it's literally two people. And they're trying to start something to build their own life over time because they're using their labor to maybe build wealth or somehow a little bit not. And when I mean wealth, it's always relative. It's enough to sustain themselves or just put food on the table and be able to control their own destiny to the best of their ability. And so what we're looking to do is make a mobile app maker that's 5G that lives in the cloud, that's 5G compliant, that will allow small and midsize businesses to create their own iOS or Android mobile app with no-code or low-code, basically like creating an email. That's how simple we want it to be. When you create your own email, whether you use Microsoft, Google, or whatever you do, and you make it that simple. And there's a simple version, and there could be complexity added to it if they want. That would be the back office digitization or customization, but that then gets them on board with digitization. It's intriguing that McKinsey just came out with a report stating that in 2023, in order to be economically viable, and this was very recent, that all companies would need to have a digitization strategy. And so when you look at small businesses, and you look at things like COVID-19, or the COVID current ongoing issue and that disruption, this is global. And you look at even the Ukrainian War or the Russian-Ukrainian War, however you term it, invasion, war, special operation, these are disruptions. And then, on top of that, we look at climate change which has been accelerating in the last two years more so than it was prior to this that we've experienced. So this is something that everyone can see is self-evident. I'm not even focused on the cause of the problem. My brain and the way I think, and my team, we like to focus on solutions. My chairman is a former program director of NASA who managed 1,200 engineers that built the International Space Station; what was it? 20-30 years ago, however, that is. And he helped lead and build that from Johnson Center. And so you're focused on solutions because if you're building the International Space Station, you can only focus on solutions and anticipate the problems but not dwell on them. And so that kind of mindset is what I am, and it's looking to help small businesses do that to get them on board with digitization and then in customization. And then beyond that, use our system, which is called M.I.N.D. So we own these...we own patents, three patents, trademarks, and service marks related to artificial intelligence that are in the field of economics. And we will utilize DEVS...we plan to do that which is a suite of system specifications to predict regional economic issues like the weather in a proactive way, not reactive. A lot of economic situations are reactive. It's reactive to the Federal Reserve raising interest rates or lowering rates, Wall Street, you know, moving money or not moving money. It is what it is. I mean, I don't judge it. I think it's like financial engineering, and that's fine. It's profitability. But then, at the end of the day, if you're building something, it's like when we're going to go to space. When rockets launch, they have to do what they're intended to do. Like, I know that Blue Origin just blew up recently. Or if they don't, they have a default, and at least I heard that the Blue Origin satellite, if it were carrying passengers, the passengers would have been safe because it disembarked when it detected its own problem. So when you anticipate these kinds of problems and you apply them to the local small business person, you can help them forecast and predict better like what weather prediction has done. And we're always improving that collectively for weather prediction, especially with climate change, so that it can get to near real-time as soon as possible or close a window versus two weeks out versus two days out as an example. VICTORIA: Right. Those examples of what you call a narrow economic prediction. LEONARD: Correct. It is intriguing when you say narrow economic because it wouldn't be narrow AI. But it would actually get into AGI if you added more variables, which we would. The more variables you added in tenancies...so if you're looking at events, the system events discretion so discrete event system specification you would specify what they really, really need to do to have those variables. But at some point, you're working on a system, what I would call AGI. But AGI, in my mind, the circles I run in at least or at least most of the scientists I talk to it's not artificial superintelligence. And so the general public thinks AGI...and I've said this to Stephen Ibaraki, who's the founder of AI for Good at Global Summit at the United Nations, and one of his interviews as well. It's just Artificial General Intelligence, I think, has been put out a lot by Hollywood and entertainment and so forth, and some scientists say certain things. We won't be at artificial superintelligence. We might get to Artificial General Intelligence by 2030 easily, in my opinion. But that will be narrow AI, but it will cover what we look at it in the field as cross-domain, teaching a system to look at different variables because right now, it's really narrow. Like natural language processing, it's just going to look at language and infer from there, and then you've got backward propagation that's credit assignment and fraud and detection. Those are narrow data points. But when you start looking at something cross-domain...who am I thinking of? Pedro Domingos who wrote the Master Algorithm, which actually, Xi Jinping has a copy of, the President of China, on his bookshelf in his office because they've talked about that, and these great minds because Stephen Ibaraki has interviewed these...and the founder of Google Brain and all of these guys. And so there's always this debate in the scientific community of what is narrow AI what it's not. But at the end of the day, I just like Pedro's definition of it because he says the master algorithm will be combining all five, so you're really crossing domains, which AI hasn't done that. And to me, that will be AGI, but that's not artificial superintelligence. And artificial superintelligence is when it becomes very, you know, like some of the movies could say, if we as humanity just let it run wild, it could be crazy. VICTORIA: One of my questions is the future of AI more like iRobot or Bicentennial Man? LEONARD: Well, you know, interesting. That's a great question, Victoria. I see most of AI literally as iRobot, as a tool more than anything, except at the end when it implied...so it kind of did two things in that movie, but a wonderful movie to bring up. And I like Will Smith perfectly. Well, I liked him a lot more before -- VICTORIA: I think iRobot is really the better movie. LEONARD: Yeah, so if people haven't seen iRobot, I liked Will Smith, the actor. But iRobot showed you two things, and it showed you, one, it showed hope. Literally, the robot...because a lot of people put AI and robots. And AI by itself is the brain or the mind; I should say hardware are the robots or the brain. Software...AI in and of itself is software. It's the mind itself. That's why we have M.I.N.D Machine Intelligence NeuralNetwork Database. We literally have that. That's our acronym and our slogan and everything. And it's part of our patents. But its machine intelligence is M.I.N.D, and we own that, you know; the company owns it. And so M.I.N.D...we always say AI powered by M.I.N.D. We're talking about that software side of, like, what your mind does; it iterates and thinks, the ability to think itself. Now it's enclosed within a structure called, you know, for the human, it's called a brain, the physical part of it, and that brain is enclosed within the body. So when you look at robots...and my chairman was the key person for robotics for the International Space Station. So when you look at robotics, you are putting that software into hardware, just like your cell phone. You have the physical, and then you have the actual iOS, which is the operating system. So when you think about that, yeah, iRobot was good because it showed how these can be tools, and they were very, in the beginning of the movie, very helpful, very beneficial to humanity. But then it went to a darker side and showed where V.I.K.I, which was an acronym as well, I think was Virtual Interactive Kinetic technology of something. Yeah, I believe it was Virtual Interactive Kinetic inference or technology or something like that, V.I.K.I; I forgot the last I. But that's what it stood for. It was an acronym to say...and then V.I.K.I just became all aware and started killing everyone with robots and just wanted to say, you know, this is futile. But then, at the very, very end, V.I.K.I learned from itself and says, "Okay, I guess this isn't right." Or the other robot who could think differently argued with V.I.K.I, and they destroyed her. And it made V.I.K.I a woman in the movie, and then the robot was the guy. But that shows that it can get out of hand. But it was intriguing to me that they had her contained within one building. This wouldn't be artificial superintelligence. And I think sometimes Hollywood says, "Just take over everything from one building," no. It wouldn't be on earth if it could. But that is something we always have to think about. We have to think about the worst-case scenarios. I think every prudent scientist or business person or anyone should do that, even investors, I mean, if you're investing something for the future. But you also don't focus on it. You don't think about the best-case scenario, either. But there's a lot of dwelling on the worst-case scenario versus the good that we can do given we're looking at where humanity is today. I mean, we're in 2022, and we're still fighting wars that we fought in 1914. VICTORIA: Right. Which brings me to my next question, which is both, what are the most exciting opportunities to innovate in the AI space currently? And conversely, what are the biggest challenges that are facing innovation in that field? LEONARD: Ooh, that's a good question. I think, in my opinion, it's almost the same answer; one is...but I'm in a special field. And I'm surprised there's not a lot of competition for our company. I mean, it's very good for me and the company's sense. It's like when Mark Zuckerberg did Facebook, there was Friendster, and there was Myspace, but they were different. They were different verticals. And I think Mark figured out how to do it horizontally, good or bad. I'm talking about the beginning of when he started Facebook, now called Meta. But I'm saying utilizing AI in economics because a lot of times AI is used in FinTech and consumerism, but not economic growth where we're really talking about growing something organically, or it's called endogenous growth. Because I studied Paul Romer's work, who won the Nobel Prize in 2018 for economic science. And he talked about the nature of ideas. And we were working on something like that in Stanford. And I put out a book in 2017 of January talking about cryptocurrencies, artificial intelligence but about the utilization of it, but not the speculation. I never talked about speculation. I don't own any crypto; I would not. It's only once it's utilized in its PureTech form will it create something that it was envisioned to do by the protocol that Satoshi Nakamoto sort of created. And it still fascinates me that people follow Bitcoin protocol, even for the tech and the non-tech, but they don't know who Satoshi is. But yeah, it's a white paper. You're just following a white paper because I think logically, the world is going towards that iteration of evolution. And that's how AI could be utilized for good in an area to focus on it with economics and solving current problems. And then going forward to build a new economy where it's not debt-based driven or consumer purchase only because that leaves a natural imbalance in the current world structure. The western countries are great. We do okay, and we go up and down. But the emerging and developing countries just get stuck, and they seem to go into a circular loop. And then there are wars as a result of these things and territory fights and so forth. So that's an area I think where it could be more advanced is AI in the economic realm, not so much the consumer FinTech room, which is fine. But consumer FinTech, in my mind, is you're using AI to process PayPal. That's where I think Elon just iterated later because PayPal is using it for finance. You're just moving things back and forth, and you're just authenticating everything. But then he starts going on to SpaceX next because he's like, well, let me use technology in a different way. And I do think he's using AI on all of his projects now. VICTORIA: Right. So how can that tech solve real problems today? Do you see anything even particular about Southern California, where we're both at right now, where you think AI could help predict some outcomes for small businesses or that community? LEONARD: I'm looking to do it regionally then globally. So I'm part of this Southern Cal Innovation Hub, which is just AI. It's an artificial intelligence coordination between literally San Diego County, Orange County, and Los Angeles County. And so there's a SoCal Innovation Hub that's kind of bringing it together. But there are all three groups, like; I think the CEO in Orange County is the CEO of Leadership Alliance. And then in San Diego, there's another group I can't remember their name off the top of my head, and I'm talking about the county itself. So each one's representing a county because, you know. And then there's one in Northern California that I'm also associated with where if you look at California as its own economy in the U.S., it's still pretty significant as an economic cycle in the United States, period. That's why so many politicians like California because they can sway the votes. So yeah, we're looking to do that once, you know, we are raising capital. We're crowdfunding currently. Our total raise is about 6 million. And so we're talking to venture capitalists, private, high net worth investors as well. Our federal funding is smaller. It's just like several hundred thousand because most people can only invest a few thousand. But I always like to try to give back. If you tell people...if you're Steve Jobs, like, okay, I've got this Apple company. In several years, you'll see the potential. And people are like, ah, whatever, but then they kick themselves 15 years later. [laughs] Like, oh, I wish I thought about that Apple stock for $15 when I could. But you give people a chance, and you get the word out, and you see what happens. Once you build a system, you share it. There are some open-source projects. But I think the open source, like OpenAI, as an example, Elon Musk funds that as well as Microsoft. They both put a billion dollars into it. It is an open-source project. OpenAI claims...but some of the research does go back to Microsoft to be able to see it. And DeepMind is another research for AI, but they're owned by Google. And so, I'm also very focused on democratizing artificial intelligence for the benefit of everyone. I really believe that needs to be democratized in a sense of tying it to economics and making it utilized for everyone that may need it for the benefit of humanity where it's profitable and makes money, but it's not just usurping. MID-ROLL AD: As life moves online, brick-and-mortar businesses are having to adapt to survive. With over 18 years of experience building reliable web products and services, thoughtbot is the technology partner you can trust. We provide the technical expertise to enable your business to adapt and thrive in a changing environment. We start by understanding what's important to your customers to help you transition to intuitive digital services your customers will trust. We take the time to understand what makes your business great and work fast yet thoroughly to build, test, and validate ideas, helping you discover new customers. Take your business online with design‑driven digital acceleration. Find out more at tbot.io/acceleration or click the link in the show notes for this episode. VICTORIA: With that democratizing it, is there also a need to increase the understanding of the ethics around it and when there are certain known use cases for AI where it actually is discriminatory and plays to systemic problems in our society? Are you familiar with that as well? LEONARD: Yes, absolutely. Well, that's my whole point. And, Victoria, you just hit the nail on the head. Truly democratizing AI in my mind and in my brain the way it works is it has opened up for everyone. Because if you really roll it back, okay, companies now we're learning...we used to call it several years ago UGC, User Generated Content. And now a lot of people are like, okay, if you're on Facebook, you're the product, right? Or if you're on Instagram, you're the product. And they're using you, and you're using your data to sell, et cetera, et cetera. But user-generated content it's always been that. It's just a matter of the sharing of the economic. That's why I keep going back to economics. So if people were, you know, you wouldn't have to necessarily do advertising if you had stakeholders with advertising, the users and the company, as an example. If it's a social media company, just throwing it out there, so let's say you have a social media...and this has been talked about, but I'm not the first to introduce this. This has been talked about for over ten years, at least over 15 years. And it's you share as a triangle in three ways. So you have the user and everything else. So take your current social media, and I won't pick on Facebook, but I'll just use them, Facebook, Instagram, or Twitter. Twitter's having issues recently because Elon is trying to buy them or get out of buying them. But you just looked at that data, and then you share with the user base. What's the revenue model? And there needs to be one; let me be very clear. There has to be incentive, and there has to be profitability for people that joined you earlier, you know, joined the corporation, or become shareholders, or investors, or become users, or become customers. They have to be able to have some benefit, not extreme greater than everyone else but a great benefit from coming in earlier by what they contributed at the time. And that is what makes this system holistic in my opinion, like Reddit or any of these bloggers. But you make it where they use their time and the users, and you share it with the company and then the data and so forth, and whatever revenue economic model you have, and it's a sort of a three-way split. It's just not always equal. And that's something that I think in economics, we're still on a zero-sum game, I win, you lose sort of economic model globally. That's why there's a winner of a war and a loser of a war. But in reality, as you know, Victoria, there are no winners of any war. So it's funny, [laughs] I was just saying, well, you know, because of the economic mode, but Von Neumann, who talked about that, also talked about something called a non-zero-sum game when he talked about it in mathematics that you can win, and I can win; we just don't win equally because they never will match that. So if I win, I may win 60; you win 40. Or you may win 60, I win 40, and we agree to settle on that. It's an agreement versus I'm just going to be 99, and you'll be 1%, or I'm just going to be 100, and you're at 0. And I think that our economic model tends to be a lot of that, like, when you push forth and there needs to be more of that. When you talk about the core of economics...and I go way back, you know, prior to the Federal Reserve even being started. I just look at the world, and it's always sort of been this land territorial issue of what goods are under the country. But we've got technology where we can mitigate a lot of things and do the collective of help the earth, and then let's go off to space, all of space. That's where my brain is focused on. VICTORIA: Hmm. Oh yeah, that makes sense to me. I think that we're all going to have to evolve our economic models here in the future. I wonder, too, as you're building your startup and you're building your company, what are some of the technology trade-offs you're having to make in the stack of the AI software that you're building? LEONARD: Hmm. Good question. But clarify, this may be a lot deeper dive because that's a general question. And I don't want to...yeah, go ahead. VICTORIA: Because when you're building AI, and you're going to be processing a lot of data, I know many data scientists that are familiar with tools like Jupyter Notebooks, and R, and Python. And one issue that I'm aware of is keeping the environments the same, so everything that goes into building your app and having those infrastructure as code for your data science applications, being able to afford to process all that data. [laughs] And there are just so many factors that go into building an AI app versus building something that's more easy, like a web-based user form. So just curious if you've encountered those types of trade-offs or questions about, okay, how are we going to actually build an app that we can put out on everybody's phone and that works responsibly? LEONARD: Oh, okay. So let me be very clear, but I won't give too much of the secret sauce away. But I can define this technically because this is a technical audience. This is not...so what you're really talking about is two things, and I'm clear about this, though. So the app maker won't really read and write a lot of data. It'll just be the app where people could just get on board digitalization simple, you know, process payments, maybe connect with someone like American Express square, MasterCard, whatever. And so that's just letting them function. That's sort of small FinTech in my mind, you know, just transaction A to B, B to A, et cetera. And it doesn't need to be peer-to-peer and all of the crypto. It doesn't even need to go that level yet. That's just level one. Then they will sign up for a service, which is because we're really focused on artificial intelligence as a service. And that, to me, is the next iteration for AI. I've been talking about this for about three or four years now, literally, in different conferences and so forth for people who haven't hit it. But that we will get to that point where AI will become AI as a service, just like SaaS is. We're still at the, you know, most of the world on the legacy systems are still software as a service. We're about to hit AI as a service because the world is evolving. And this is true; they did shut it down. But you did have okay, so there are two case points which I can bring up. So JP Morgan did create something called a Coin, and it was using AI. And it was a coin like crypto, coin like a token, but they called it a coin. But it could process, I think, something like...I may be off on this, so to the sticklers that will be listening, please, I'm telling you I may be off on the exact quote, but I think it was about...it was something crazy to me, like 200,000 of legal hours and seconds that it could process because it was basically taking the corporate legal structure of JP Morgan, one of the biggest banks. I think they are the biggest bank in the U.S. JPMorgan Chase. And they were explaining in 2017 how we created this, and it's going to alleviate this many hours of legal work for the bank. And I think politically; something happened because they just pulled away. I still have the original press release when they put it out, and it was in the media. And then it went away. I mean, no implementation [laughs] because I think there was going to be a big loss of jobs for it. And they basically would have been white-collar legal jobs, most specifically lawyers literally that were working for the bank. And when they were talking towards investment, it was a committee. I was at a conference. And I was like, I was fascinated by that. And they were basically using Bitcoin protocol as the tokenization protocol, but they were using AI to process it. And it was basically looking at...because legal contracts are basically...you can teach it with natural language processing and be able to encode and almost output it itself and then be able to speak with each other. Another case point was Facebook. They had...what was it? Two AI systems. They began to create their own language. I don't know if you remember that story or heard about it, and Facebook shut it down. And this was more like two years ago, I think, when they were saying Facebook was talking, you know, when they were Facebook, not Meta, so maybe it was three years ago. And they were talking, and they were like, "Oh, Facebook has a language. It's talking to each other." And it created its own little site language because it was two AI bots going back and forth. And then the engineers at Facebook said, "We got to shut this down because this is kind of getting out of the box." So when you talk about AI as a service, yes, the good and the bad, and what you take away is AWS, Oracle, Google Cloud they do have services where it doesn't need to cost you as much anymore as it used to in the beginning if you know what you're doing ahead of time. And you're not just running iterations or data processing because you're doing guesswork versus, in my opinion, versus actually knowing exactly specifically what you're looking for and the data set you're looking to get out of it. And then you're talking about just basically putting in containers and clustering it because it gets different operations. And so what you're really looking at is something called an N-scale graph data that can process data in maybe sub seconds at that level, excuse me. And one of my advisors is the head of that anyway at AGI laboratory. So he's got an N graph database that can process...when we implement it, we'll be able to process data at the petabyte level at sub-seconds, and it can run on platforms like Azure or AWS, and so forth. VICTORIA: Oh, that's interesting. So it sounds like cloud providers are making compute services more affordable. You've got data, the N-scale graph data, that can run more transactions more quickly. And I'm curious if you see any future trends since I know you're a futurist around quantum computing and how that could affect capacity for -- LEONARD: Oh [laughs] We haven't even gotten there yet. Yes. Well, if you look at N-scale, if you know what you're doing and you know what to look for, then the quantum just starts going across different domains as well but at a higher hit rate. So there's been some quantum computers online. There's been several...well, Google has their quantum computer coming online, and they've been working on it, and Google has enough data, of course, to process. So yeah, they've got that data, lots of data. And quantum needs, you know, if it's going to do something, it needs lots of data. But then the inference will still be, I think, quantum is very good at processing large, large, large amounts of data. We can just keep going if you really have a good quantum computer. But it's really narrow. You have to tell it exactly what it wants, and it will do it in what we call...which is great like in P or NP square or P over NP which is you want to do it in polynomial time, not non-polynomial, polynomial time which is...now speaking too fast. Okay, my brain is going faster than my lips. Let me slow it down. So when you start thinking about processing, if we as humans, let's say if I was going to process A to Z, and I'm like, okay, here is this equation, if I tell you it takes 1000 years, it's of no use to us, to me and you Victoria because we're living now. Now, the earth may benefit in 1000 years, but it's still of no use. But if I could take this large amount of data and have it process within minutes, you know, worst case hours...but then I'll even go down to seconds or sub-seconds, then that's really a benefit to humanity now, today in present term. And so, as a futurist, yes, as the world, we will continue to add data. We're doing it every day, and we already knew this was coming ten years ago, 15 years ago, 20 years ago, even actually in the '50s when we were in the AI winter. We're now in AI summer. In my words, I call it the AI summer. So as you're doing this, that data is going to continue to increase, and quantum will be needed for that. But then the specific need...quantum is very good at looking at a specific issue, specifically for that very narrow. Like if you were going to do the trajectory to Jupiter or if we wanted to send a probe to Jupiter or something, I think we're sending something out there now from NASA, and so forth, then you need to process all the variables, but it's got one trajectory. It's going one place only. VICTORIA: Gotcha. Well, that's so interesting. I'm glad I asked you that question. And speaking of rockets going off to space, have you ever seen a SpaceX launch from LA? LEONARD: Actually, I saw one land but not a launch. I need to go over there. It's not too far from me. But you got to give credit where credit's due and Elon has a reusable rocket. See, that's where technology is solving real-world problems. Because NASA and I have, you know, my chairman, his name is Alexander Nawrocki, you know, he's Ph.D., but I call him Rocki. He goes by Rocki like I go by LS. But it's just we talk about this like NASA's budget. [laughs] How can you reduce this? And Elon says they will come up with a reusable rocket that won't cost this much and be able to...and that's the key. That was the kind of Holy Grail where you can reuse the same rocket itself and then add some little variables on top of it. But the core, you wouldn't constantly be paying for it. And so I think where the world is going...and let me be clear, Elon pushes a lot out there. He's just very good at it. But I'm also that kind of guy that I know that Tesla itself was started by two Stanford engineers. Elon came on later, like six months, and then he invested, and he became CEO, which was a great investment for Elon Musk. And then CEO I just think it just fit his personality because it was something he loved. But I also have studied for years Nikola Tesla, and I understand what his contributions created where we are today with all the patents that he had. And so he's basically the father of WiFi and why we're able to communicate in a lot of this. We've perfected it or improved it, but it was created by him in the 1800s. VICTORIA: Right. And I don't think he came from as fortunate a background as Elon Musk, either. Sometimes I wonder what I could have done born in similar circumstances. [laughter] And you certainly have made quite a name for yourself. LEONARD: Well, I'm just saying, yeah, he came from very...he did come from a poor area of Russia which is called the Russian territory, to be very honest, Eastern Europe, definitely Eastern Europe. But yeah, I don't know once you start thinking about that [laughs]. You're making me laugh, Victoria. You're making me laugh. VICTORIA: No, I actually went camping, a backpacking trip to the Catalina Island, and there happened to be a SpaceX launch that night, and we thought it was aliens because it looked wild. I didn't realize what it was. But then we figured it was a launch, so it was really great. I love being here and being close to some of this technology and the advancements that are going on. I'm curious if you have some thoughts about...I hear a lot about or you used to hear about Silicon Valley Tech like very Northern California, San Francisco focus. But what is the difference in SoCal? What do you find in those two communities that makes SoCal special? [laughs] LEONARD: Well, I think it's actually...so democratizing AI. I've been in a moment like that because, in 2015, I was in Dubai, and they were talking about creating silicon oasis. And so there's always been this model of, you know, because they were always, you know, the whole Palo Alto thing is people would say it and it is true. I mean, I experienced it. Because I was in a two-year program, post-graduate program executive, but we would go up there...I wasn't living up there. I had to go there maybe once every month for like three weeks, every other month or something. But when you're up there, it is the air in the water. It's just like, people just breathe certain things. Because around the world, and I would travel to Japan, and China, and other different parts of Asia, Vietnam, et cetera and in Africa of course, and let's say you see this and people are like, so what is it about Silicon Valley? And of course, the show, there is the Hollywood show about it, which is pretty a lot accurate, which is interesting, the HBO show. But you would see that, and you would think, how are they able to just replicate this? And a lot of it is a convergence. By default, they hear about these companies' access because the key is access, and that's what we're...like this podcast. I love the concept around it because giving awareness, knowledge, and access allows other people to spread it and democratize it. So it's just not one physical location, or you have to be in that particular area only to benefit. I mean, you could benefit in that area, or you could benefit from any part of the world. But since they started, people would go there; engineers would go there. They built company PCs, et cetera. Now that's starting to spread in other areas like Southern Cal are creating their own innovation hubs to be able to bring all three together. And those three are the engineers and founders, and idea makers and startups. And you then need the expertise. I'm older than 42; I'm not 22. [laughs] So I'm just keeping it 100, keeping it real. So I'm not coming out at 19. I mean, my son's 18. And I'm not coming out, okay, this my new startup, bam, give me a billion dollars, I'm good. And let me just write off the next half. But when you look at that, there's that experience because even if you look at Mark Zuckerberg, I always tell people that give credit where credit is due. He brought a senior team with him when he was younger, and he didn't have the experience. And his only job has been Facebook out of college. He's had no other job. And now he's been CEO of a multi-billion dollar corporation; that's a fact. Sometimes it hurts people's feelings. Like, you know what? He's had no other job. Now that can be good and bad, [laughs] but he's had no other jobs. And so that's just a credit, like, if you can surround yourself with the right people and be focused on something, it can work to the good or the bad for your own personal success but then having that open architecture. And I think he's been trying to learn and others versus like an Elon Musk, who embraces everything. He's just very open in that sense. But then you have to come from these different backgrounds. But let's say Elon Musk, Mark Zuckerberg, let's take a guy like myself or whatever who didn't grow up with all of that who had to make these two ends meet, figure out how to do the next day, not just get to the next year, but get to the next day, get to the next week, get to the next month, then get to the next year. It just gives a different perspective as well. Humanity's always dealing with that. Because we had a lot of great engineers back in the early 1900s. They're good or bad, you know, you did have Nikola Tesla. You had Edison. I'm talking about circa around 1907 or 1909, prior to World War I. America had a lot of industries. They were the innovators then, even though there were innovations happening in Europe, and Africa, and China, as well and Asia. But the innovation hub kind of created as the America, quote, unquote, "industrial revolution." And I think we're about to begin a new revolution sort of tech and an industrial revolution that's going to take us to maybe from 20...we're 2022 now, but I'll say it takes us from 2020 to 2040 in my head. VICTORIA: So now that communities can really communicate across time zones and locations, maybe the hubs are more about solving specific problems. There are regional issues. That makes a lot more sense. LEONARD: Yes. And collaborating together, working together, because scientists, you know, COVID taught us that. People thought you had to be in a certain place, but then a lot of collaboration came out of COVID; even though it was bad globally, even though we're still bad, if people were at home, they start collaborating, and scientists will talk to scientists, you know, businesses, entrepreneurs, and so forth. But if Orange County is bringing together the mentors, the venture capital, or at least Southern California innovation and any other place, I want to say that's not just Silicon Valley because Silicon Valley already has it; we know that. And that's that region. It's San Jose all the way up to...I forgot how far north it's past San Francisco, actually. But it's that region of area where they encompass the real valley of Silicon Valley if you're really there. And you talk about these regions. Yes, I think we're going to get to a more regional growth area, and then it'll go more micro to actually cities later in the future. But regional growth, I think it's going to be extremely important globally in the very near term. I'm literally saying from tomorrow to the next, maybe ten years, regional will really matter. And then whatever you have can scale globally anyway, like this podcast we're doing. This can be distributed to anyone in the world, and they can listen at ease when they have time. VICTORIA: Yeah, I love it. It's both exciting and also intimidating. [laughs] And you mentioned your son a little bit earlier. And I'm curious, as a founder and someone who spent a good amount of time in graduate and Ph.D. programs, if you feel like it's easy to connect with your son and maintain that balance and focusing on your family while you're building a company and investing in yourself very heavily. LEONARD: Well, I'm older, [laughs] so it's okay. I mean, I've mentored him, you know. And me and his mom have a relationship that works. I would say we have a better relationship now than when we were together. It is what it is. But we have a communication level. And I think she was just a great person because I never knew my real father, ever. I supposedly met him when I was two or one; I don't know. But I have no memories, no photos, nothing. And that was just the environment I grew up in. But with my son, he knows the truth of everything about that. He's actually in college. I don't like to name the school because it's on the East Coast, and it's some Ivy League school; that's what I will say. And he didn't want to stay on the West Coast because I'm in Orange County and his mom's in Orange County. He's like, "I want to get away from both of you people." [laughter] And that's a joke, but he's very independent. He's doing well. When he graduated high school, he graduated with 4.8 honors. He made the valedictorian. He was at a STEM school. VICTORIA: Wow. LEONARD: And he has a high GPA. He's studying computer science and economics as well at an Ivy League, and he's already made two or three apps at college. And I said, "You're not Mark, so calm down." [laughter] But anyway, that was a recent conversation. I won't go there. But then some people say, "LS, you should be so happy." What is it? The apple doesn't fall far from the tree. But this was something he chose around 10 or 11. I'm like, whatever you want to do, you do; I'll support you no matter what. And his mom says, "Oh no, I think you programmed him to be like you." [laughs] I'm like, no, I can't do that. I just told him the truth about life. And he's pretty tall. VICTORIA: You must have -- LEONARD: He played basketball in high school a lot. I'm sorry? VICTORIA: I was going to say you must have inspired him. LEONARD: Yeah. Well, he's tall. He did emulate me in a lot of ways. I don't know why. I told him just be yourself. But yes, he does tell me I'm an inspiration to that; I think because of all the struggles I've gone through when I was younger. And you're always going through struggles. I mean, it's just who you are. I tell people, you know, you're building a company. You have success. You can see the future, but sometimes people can't see it, [laughs] which I shouldn't really say, but I'm saying anyway because I do that. I said this the other night to some friends. I said, "Oh, Jeff Bezo's rocket blew up," going, you know, Blue Origin rocket or something. And then I said Elon will tell Jeff, "Well, you only have one rocket blow up. I had three, [laughter] SpaceX had three." So these are billionaires talking to billionaires about, you know, most people don't even care. You're worth X hundred billion dollars. I mean, they're worth 100 billion-plus, right? VICTORIA: Right. LEONARD: I think Elon is around 260 billion, and Jeff is 160 or something. Who cares about your rocket blowing up? But it's funny because the issues are still always going to be there. I've learned that. I'm still learning. It doesn't matter how much wealth you have. You just want to create wealth for other people and better their lives. The more you search on bettering lives, you're just going to have to wake up every day, be humble with it, and treat it as a new day and go forward and solve the next crisis or problem because there will be one. There is not where there are no problems, is what I'm trying to say, this panacea or a utopia where you personally, like, oh yeah, I have all this wealth and health, and I'm just great. Because Elon has had divorce issues, so did Jeff Bezos. So I told my son a lot about this, like, you never get to this world where it's perfect in your head. You're always going to be doing things. VICTORIA: That sounds like an accurate future prediction if I ever heard one. [laughs] Like, there will be problems. No matter where you end up or what you choose to do, you'll still have problems. They'll just be different. [laughs] LEONARD: Yeah, and then this is for women and men. It means you don't give up. You just keep hope alive, and you keep going. And I believe personally in God, and I'm a scientist who actually does. But I look at it more in a Godly aspect. But yeah, I just think you just keep going, and you keep building because that's what we do as humanity. It's what we've done. It's why we're here. And we're standing on the shoulders of giants, and I just always considered that from physicists and everyone. VICTORIA: Great. And if people are interested in building something with you, you have that opportunity right now to invest via the crowdfunding app, correct? LEONARD: Yes, yes, yes. They can do that because the company is still the same company because eventually, we're going to branch out. My complete vision for AIEDC is using artificial intelligence for economic development, and that will spread horizontally, not just vertically. Vertically right now, just focus on just a mobile app maker digitization and get...because there are so many businesses even globally, and I'm not talking only e-commerce. So when I say small to midsize business, it can be a service business, car insurance, health insurance, anything. It doesn't have to be selling a particular widget or project, you know, product. And I'm not saying there's nothing wrong with that, you know, interest rates and consumerism. But I'm not thinking about Shopify, and that's fine, but I'm talking about small businesses. And there's the back office which is there are a lot of tools for back offices for small businesses. But I'm talking about they create their own mobile app more as a way to communicate with their customers, update them with their customers, and that's key, especially if there are disruptions. So let's say that there have been fires in California. In Mississippi or something, they're out of water. In Texas, last year, they had a winter storm, electricity went out. So all of these things are disruptions. This is just in the U.S., And of course, I won't even talk about Pakistan, what's going on there and the flooding and just all these devastating things, or even in China where there's drought where there are these disruptions, and that's not counting COVID disrupts, the cycle of business. It literally does. And it doesn't bubble up until later when maybe the central banks and governments pay attention to it, just like in Japan when that nuclear, unfortunately, that nuclear meltdown happened because of the earthquake; I think it was 2011. And that affected that economy for five years, which is why the government has lower interest rates, negative interest rates, because they have to try to get it back up. But if there are tools and everyone's using more mobile apps and wearables...and we're going to go to the metaverse and all of that. So the internet of things can help communicate that. So when these types of disruptions happen, the flow of business can continue, at least at a smaller level, for an affordable cost for the business. I'm not talking about absorbing costs because that's meaningless to me. VICTORIA: Yeah, well, that sounds like a really exciting project. And I'm so grateful to have this time to chat with you today. Is there anything else you want to leave for our listeners? LEONARD: If they want to get involved, maybe they can go to our crowdfunding page, or if they've got questions, ask about it and spread the word. Because I think sometimes, you know, they talk about the success of all these companies, but a lot of it starts with the founder...but not a founder. If you're talking about a startup, it starts with the founder. But it also stops with the innovators that are around that founder, male or female, whoever they are. And it also starts with their community, building a collective community together. And that's why Silicon Valley is always looked at around the world as this sort of test case of this is how you create something from nothing and make it worth great value in the future. And I think that's starting to really spread around the world, and more people are opening up to this. It's like the crowdfunding concept. I think it's a great idea, like more podcasts. I think this is a wonderful idea, podcasts in and of themselves, so people can learn from people versus where in the past you would only see an interview on the business news network, or NBC, or Fortune, or something like that, and that's all you would understand. But this is a way where organically things can grow. I think the growth will continue, and I think the future's bright. We just have to know that it takes work to get there. VICTORIA: That's great. Thank you so much for saying that and for sharing your time with us today. I learned a lot myself, and I think our listeners will enjoy it as well. You can subscribe to the show and find notes along with a complete transcript for this episode at giantrobots.fm. If you have questions or comments, email us at hosts@giantrobot.fm. You can find me on Twitter @victori_ousg. This podcast is brought to you by thoughtbot and produced and edited by Mandy Moore. Thanks for listening. See you next time. ANNOUNCER: This podcast was brought to you by thoughtbot. thoughtbot is your expert design and development partner. Let's make your product and team a success. Special Guest: Leonard S. Johnson.

Prof. Pedro Domingos has been at the forefront of the revolutions in artificial intelligence and machine learning for over two decades. In addition to conducting his teaching and research at the Paul G. Allen School of Computer Science and Engineering of the University of Washington, he has worked closely with a variety of the biggest firms in the tech industry. His most recent book is "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Re-Make Our World." Dr. Domingos has also been a vocal critic of the encroachment of woke/DEI ideology in the technology sector, smuggled in under the banner of "ethics." His opposition has put him at the center of controversy. We discuss his various run-ins with the woke contingent in his field, the promise of AI, the fate of the university, and (of course) a bit about stochastic parrots.

Prof. Pedro Domingos has been at the forefront of the revolutions in artificial intelligence and machine learning for over two decades. In addition to conducting his teaching and research at the Paul G. Allen School of Computer Science and Engineering of the University of Washington, he has worked closely with a variety of the biggest firms in the tech industry. His most recent book is "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Re-Make Our World." Dr. Domingos has also been a vocal critic of the encroachment of woke/DEI ideology in the technology sector, smuggled in under the banner of "ethics." His opposition has put him at the center of controversy. We discuss his various run-ins with the woke contingent in his field, the promise of AI, the fate of the university, and (of course) a bit about stochastic parrots.

This episode continues our discussion with AI researcher Aleksa Gordić from DeepMind on understanding today's most advanced AI systems.00.07 This episode builds on Episode 501.05 We start with GANs – Generative Adversarial Networks01.33 Solving the problem of stability, with higher resolution03.24 GANs are notoriously hard to train. They suffer from mode collapse03.45 Worse, the model might not learn anything, and the result is pure noise03.55 DC GANs introduced convolutional layers to stabilise them and enable higher resolution04.37 The technique of outpainting05.55 Generating text as well as images, and producing stories06.14 AI Dungeon06.28 From GANs to Diffusion models06.48 DDPM (De-noising diffusion probabilistic models) does for diffusion models what DC GANs did for GANs07.20 They are more stable, and don't suffer from mode collapse07.30 They do have downsides. They are much more computation intensive08.24 What does the word diffusion mean in this context?08.40 It's adopted from physics. It peels noise away from the image09.17 Isn't that rewinding entropy?09.45 One application is making a photo taken in 1830 look like one taken yesterday09.58 Semantic Segmentation Masks convert bands of flat colour into realistic images of sky, earth, sea, etc10.35 Bounding boxes generate objects of a specified class from tiny inputs11.00 The images are not taken from previously seen images on the internet, but invented from scratch11.40 The model saw a lot of images during training, but during the creation process it does not refer back to them12.40 Failures are eliminated by amendments, as always with models like this12.55 Scott Alexander blogged about models producing images with wrong relationships, and how this was fixed within 3 months13.30 The failure modes get harder to find as the obvious ones are eliminated13.45 Even with 175 billion parameters, GPT-3 struggled to handle three digits in computation15.18 Are you often surprised by what the models do next?15.50 The research community is like a hive mind, and you never know where the next idea will come from16.40 Often the next thing comes from a couple of students at a university16.58 How Ian Goodfellow created the first GAN17.35 Are the older tribes described by Pedro Domingos (analogisers, evolutionists, Bayesians…) now obsolete?18.15 We should cultivate different approaches because you never know where they might lead19.15 Symbolic AI (aka Good Old Fashioned AI, or GOFAI) is still alive and kicking19.40 AlphaGo combined deep learning and GOFAI21.00 Doug Lennart is still persevering with Cyc, a purely GOFAI approach21.30 GOFAI models had no learning element. They can't go beyond the humans whose expertise they encapsulate22.25 The now-famous move 37 in AlphaGo's game two against Lee Sedol in 201623.40 Moravec's paradox. Easy things are hard, and hard things are easy24.20 The combination of deep learning and symbolic AI has been long urged, and in fact is already happening24.40 Will models always demand more and more compute?25.10 The human brain has far more compute power than even our biggest systems today25.45 Sparse, or MoE (Mixture of Experts) systems are quite efficient26.00 We need more compute, better algorithms, and more efficiency26.55 Dedicated AI chips will help a lot with efficiency26.25 Cerebros claims that GPT-3 could be trained on a single chip27.50 Models can increasingly be trained for general purposes and then tweaked for particular tasks28.30 Some of the big new models are open access29.00 What else should people learn about with regard to advanced AI?29.20 Neural Radiance Fields (NERF) models30.40 Flamingo and Gato31.15 We have mostly discussed research in these episodes, rather than engineering

In our first panel episode, we host hacker George Hotz, CS professor Pedro Domingos and OSE founder Marcin Jakubowski. A lively exchange! Learn what decentralization can and cannot do for the world. Work with Marcin: opensourceecology.org Follow Pedro on Twitter: @pmddomingos Follow George on Twitter: @comma_ai

Note: there are no politics discussed in this show and please do not interpret this show as any kind of a political statement from us. We have decided not to discuss politics on MLST anymore due to its divisive nature. Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/HNnAwSduud [00:00:00] Intro [00:01:36] What we all need to understand about machine learning [00:06:05] The Master Algorithm Target Audience [00:09:50] Deeply Connected Algorithms seen from Divergent Frames of Reference [00:12:49] There is a Master Algorithm; and it's mine! [00:14:59] The Tribe of Evolution [00:17:17] Biological Inspirations and Predictive Coding [00:22:09] Shoe-Horning Gradient Descent [00:27:12] Sparsity at Training Time vs Prediction Time [00:30:00] World Models and Predictive Coding [00:33:24] The Cartoons of System 1 and System 2 [00:40:37] AlphaGo Searching vs Learning [00:45:56] Discriminative Models evolve into Generative Models [00:50:36] Generative Models, Predictive Coding, GFlowNets [00:55:50] Sympathy for a Thousand Brains [00:59:05] A Spectrum of Tribes [01:04:29] Causal Structure and Modelling [01:09:39] Entropy and The Duality of Past vs Future, Knowledge vs Control [01:16:14] A Discrete Universe? [01:19:49] And yet continuous models work so well [01:23:31] Finding a Discretised Theory of Everything

CS professor Pedro Domingos talks history, technology and the woke revolution with Corey and Passer. Find Pedro on Twitter: @pmddomingos Follow Corey on Twitter: @drcoreywolf

O que falta para termos um Judiciário 4.0? Muita coisa! Mas, a boa notícia é que estamos no caminho certo. Por enquanto, não precisamos da inteligência artificial mais avançada ou de juízes-robôs. Ainda estamos no estágio inicial dessa revolução e, por isso, devemos pensar nas inúmeras soluções que já estão ao nosso alcance e que podem causar um impacto imenso na nossa realidade atual. E é exatamente sobre o que já está sendo feito e o que poderá ser implementado em breve  no Poder Judiciário e na Advocacia Pública que vamos conversar com o Davi Ramos. Ele já trabalhou na Marinha do Brasil, em Procuradorias e em empresas privadas como desenvolvedor de sistemas. Possui 15 anos de experiência em gestão de sistemas jurídicos para advocacia pública. Já foi secretário do Fórum Nacional de Tecnologia das Procuradorias de Estado e Municípios, product manager focado em IA para a advocacia pública e gerente de produto na Softplan. - DIREITO 4.0 PODCAST -Instagram: https://www.instagram.com/direito4.0podcastLinkedIn: https://www.linkedin.com/company/direito-4-0-podcastE-mail: podcast@floox.com.br - DAVI RAMOS -LinkedIn: https://www.linkedin.com/in/davi-ramos/ - SOFTPLAN -Site: https://www.softplan.com.br/LinkedIn: https://www.linkedin.com/company/softplan/Instagram: https://instagram.com/softplan?utm_medium=copy_link - NOTAS DO EPISÓDIO - CURSOData Science Academy: https://www.datascienceacademy.com.br/ LIVROSOs Nove Titãs Da IA: Como os Gigantes da Tecnologia e Suas Máquinas Pensantes Podem Subverter a Humanidade, Amy Webb:https://www.amazon.com.br/dp/B08LJQ7P5R/ref=dp-kindle-redirect?_encoding=UTF8&btkr=1O Algoritmo Mestre: Como a Busca Pelo Algoritmo de Machine Learning Definitivo Recriará Nosso Mundo, Pedro Domingos:https://www.amazon.com.br/Algoritmo-Mestre-Learning-Definitivo-Recriar%C3%A1/dp/8575225383/ref=asc_df_8575225383/?tag=googleshopp00-20&linkCode=df0&hvadid=379748659420&hvpos=&hvnetw=g&hvrand=16183410791378151807&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9101294&hvtargid=pla-423299861511&psc=1Algoritmos de Destruição em Massa,Cathy O'Neil:https://www.amazon.com.br/Algoritmos-Destrui%C3%A7%C3%A3o-Massa-Cathy-ONeil/dp/6586460026/ref=asc_df_6586460026/?tag=googleshopp00-20&linkCode=df0&hvadid=379792431986&hvpos=&hvnetw=g&hvrand=15334276561256761906&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9101295&hvtargid=pla-1007895878384&psc=1

For a while now, machines have been inseparably tied to our lives. The algorithms on Google, Netflix, Amazon, Xbox, and Tinder have run your life unwittingly. Machines are digesting data that you willingly share with them. Artificial intelligence has also impacted healthcare, from the development of vaccines to the search for a cure for cancer. Machine learning is transforming every aspect of our lives, but what is AI's ultimate foundation?Author and AI expert Pedro Domingos discusses machine learning's five tribes in his book Master Algorithm. During this episode, Pedro shares how the ultimate algorithm can derive knowledge about the past, the present, and the future from data. Listen as he and Greg tackle why such an algorithm should exist and compelling arguments from neuroscience, evolution, physics, statistics, and other branches of computer science.Episode Quotes:Are computer scientists the new age philosophers?I don't think scientists could have supplanted the psychologists and philosophers, and so on. I do think, however, that computer science and machine learning, in particular, changes the way we do everything in a very profound way. If you look at science, more than anything else, its progress is determined by the tools that are available. Galileo was Galileo because he had the telescope. No telescope, no Galileo, and the examples go on. And the thing is that computers are the most extraordinary tool for science, among other things. But for science in particular that we have ever created, they magnify our ability to do things in a way that was —I think — hard to imagine, even 50 years ago.Is machine learning just a bunch of different tools, all trying different approaches to solve the same problems?At the end of the day, the best algorithm is almost never any existing one. What a machine learning algorithm does, it's not magic. It's incorporating knowledge, and knowledge will be different in different domains. There are broad classes of domains where the same knowledge is relevant, and indeed different paradigms tend to do well in different problems. So, deep learning does very well at perceptual problems because, again, you know, these things were inspired by the neurology of the visual system, and et cetera, et cetera.Is the evolutionary model applicable and aligned with what's happening in AI and will there be obstacles in pursuing this line of thinking?There's more to be discovered about how evolution learns. And by the way, there's more to be discovered for the purposes of AI and also for the purposes of understanding evolution. I actually think that if someone really had a supercomputer, that could simulate evolution over a billion years. With the model of evolution that we have today, it would fail. It wouldn't get there. There are some mechanisms that also evolved. But again, this is this interesting series of stages, right? Even within evolution, there are levels of how evolution works. And I think there's a lot of that, that we still don't understand. But we will at some point, and I think that will be beneficial both for biology and for AI.Time Code Guide:00:03:06 How A.I. is revolutionizing the way we think00:04:31 Tycho Brahe stage00:06:44 Is the unified field theory of machine learning the same as the general approach to learning?00:09:11 Computers represent the fourth stage of learning and transmission of knowledge, do you think it's a discontinuity from the first three stages, which all seems to be natural phenomenon?00:10:21 The emergence of AI, life, evolution of the nervous system, and cultures00:12:01 The speed at which computers communicate and facilitate the transfer of Knowledge00:13:10 Possibilities and ways you can play with the computer's processing capacity00:14:29 How did we leap from the AI winter to the AI boom that we have today?00:17:25 Learning machines and self-driving cars00:18:48 AI and Linguistics00:19:33 Do each AI ‘tribe' have a singular view of pursuing a particular approach in AI without acknowledging that it can have limitations later on?00:24:54 One paradigm in AI and Master Algorithm00:27:13 The Rise of the Connectionist00:28:00 What's next for AI?00:33:37 Is it possible to automate the trial and error process and have an algorithm where we learn how to learn?00:37:49 Is the evolutionary model doing anything for AI, and what are the obstacles in this line of thinking?00:41:53 How do we know whether a school of ideas is dead or simply dormant?00:43:01 How do you advance interdisciplinary learning within the different school of thoughts in AI?00:44:24 Thoughts on Geoff Hinton's work and back propagation00:46:22 Is there a guidebook to creating a unified theory?00:48:11 AGI, AI and humans00:51:01 Automating the Scientific Process00:52:26 Thoughts on the Future of AIShow Links:Guest ProfileAcademic ProfileProfile at the International Telecommunications UnionPedro Domingos on TwitterHis WorkPedro Domingos on Google ScholarThe Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our WorldMarkov Logic: An Interface Layer for Artificial Intelligence (Synthesis Lectures on Artificial Intelligence and Machine Learning)

ini topik campur aduk (lagi). Semalam saya membedah buku "The Master Algorithm" karangan Pedro Domingos. Kemudian keluh kesah (ramblings) saya terkait dengan situasi saat ini, yang mana orang lebih melihat ke dirinya sendiri.

In a lot of ways, artificial intelligence acts as our personal butlers — it filters our email, manages the temperature in our homes, finds the best commute, shapes our social media, runs our search engines, even flies our planes. But as AI gets involved in more and more aspects of our lives, there are nagging fears. Will AI replace us? Make humans irrelevant? Make some kind of terrible mistake, or even take over the world? On this episode, we hear from scientists and thinkers who argue that we should look at AI not as a threat or competition, but as an extension of our minds and abilities. They explain what AI is good at, and where humans have the upper hand. We look at AI in three different settings: medicine, work, and warfare, asking how it affects our present — and how it could shape our future. Also heard on this week’s episode: We meet an engineer who quit her dream job at Google because she was being asked to work on a project for the Department of Defense — and she says she didn’t want to be “part of a kill chain.” This excerpt from WHYY’s new podcast A.I. Nation, explores the ethical challenges surrounding the use of autonomous weapons. “The big danger to humanity is not that AI is too smart. It’s that it’s too stupid,” says Pedro Domingos, a professor of computer science at the University of Washington. He explains what exactly AI is, and why we often use this term for things that are not artificial intelligence. Domingos’ book is “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World.” Will a machine read your resume? Or maybe even interview you? Alex Engler, the AI and Democracy fellow at the Brookings Institution, answers questions about how AI is currently being used in the hiring process, and whether it can do a better job than humans at eliminating bias. Kate Darling, a researcher at the MIT Media Lab, explains why we should think of AI less as rivals — and more as pets and other animals. Her book is called “The New Breed: What Our History with Animals Reveals about Our Future with Robots.”

Pedro Domingos joins us to explain AI, machine learning, and how these are already changing the world around us. Pedro is a professor of computer science and author of The Master Algorithm: How the Quest for the Ultimate Learning Machine.

I first became familiar with Pedro's work through his 2015 book, The Master Algorithm. But as it turns out, his existence extends prior to my familiarity with him—which is what the bulk of what we explore in this conversation. Pedro is a professor at the University of Washington and a venerable AI researcher. He has a great quote about how as fields grow, their textbooks become larger. Then, as they mature, the textbooks become smaller again. I don't know if that's true. But it's a nice line, coming from a guy who wrote a book about getting AI down to a single master algorithm.

There is an inordinate amount of hype and fear around artificial intelligence these days, as a chorus of scholars, luminaries, media, and politicians nervously project that it could soon take our jobs and subjugate or even kills us off. Others are just as fanciful in hoping it is on the verge of solving all our problems. But the truth is AI isn’t nearly as advanced as most people imagine. What is the practical reality of AI today, and how should government approach AI policy to maximize its potential? To parse the hype, the hope, and the path forward for AI, Rob and Jackie sat down recently with Pedro Domingos, emeritus professor of computer science at the University of Washington and author of The Master Algorithm.Mentioned:Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015).Robert D. Atkinson, “The 2015 ITIF Luddite Award Nominees: The Worst of the Year’s Worst Innovation Killers” (ITIF, December 2015).Richard Dawkins, The Selfish Gene (Oxford University Press, 1990).Carl Benedikt Frey and Michael A. Osbourne, “The Future of Employment: How Susceptible Are Jobs to Computerisation?” (University of Oxford, September 17, 2013).Michael McLaughlin and Daniel Castro, “The Critics Were Wrong: NIST Data Shows the Best Facial Recognition Algorithms Are Neither Racist Nor Sexist” (ITIF, January 2020).“The Case for Killer Robots,” ITIF Innovation Files podcast with Robert Marks, August 10, 2020.

Did we invent computers, robots, and A.I. because we are fundamentally lazy? It may look like it, but that is not the technological driver. On the opposite side, it has to do with doing more, faster, more effectively, and going above and beyond our human capabilities.This same thing can be said of a carved stone used as a tool by our ancestors, the wheel, electricity, engines, airplanes, and of the most advanced technologies of today and tomorrow.Most of us do not realize it, but at the core of all the digital technology we use today, there are algorithms, and those are the things that are driving a big part of our everyday life — from buying online to moving around — but it's not just in cyberspace. Our whole day — from the moment you wake up to the moment you fall asleep—is suffused with machine learning.In this podcast, we discover what the engine is that drives Machine Learning and what its fuel is — spoiler alert: data, and a lot of it.Our guest is Professor Pedro Domingos from the University of Washington, who wrote a book called The Master Algorithm. As dystopian as that may sound, it is actually quite the opposite.  "The book is a thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own.In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask.In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible."We hope you will join us in the quest to discover what this Master Algorithm really means and what it can do for us. We do this constructively and positively, with hope and excitement for a better future, but with an eye open to the risk that may come if we cannot control its outcome — if and when AI can do much more than what it already can. Which is not that little.Let's go!GuestPedro Domingos, Professor of computer science at the University of Washington and author of 'The Master Algorithm' (@pmddomingos on Twitter)This Episode's SponsorsBlackCloak: https://itspm.ag/itspbcwebRSA Security: https://itspm.ag/itsprsawebResourcesView Pedro's work as a professor: https://homes.cs.washington.edu/~pedrod/View Pedro's book, The Master Algorithm, How the Quest for the Ultimate Learning Machine Will Remake Our World: https://www.basicbooks.com/titles/pedro-domingos/the-master-algorithm/9780465061921/To see and hear more The Cyber Society content on ITSPmagazine, visit:https://www.itspmagazine.com/the-cyber-societyTo see and hear more Redefining Technology stories on ITSPmagazine, visit:https://www.itspmagazine.com/redefining-technologyAre you interested in sponsoring an ITSPmagazine Channel?https://www.itspmagazine.com/podcast-series-sponsorships

For the 90th episode of NEOHUMAN, Agah is chatting with Dr. Pedro Domingos. Pedro is a Professor Emeritus of computer science and engineering at the University of Washington. He is a researcher in machine learning... The post 90: Dr. Pedro Domingos appeared first on LIVE IN LIMBO.

Today we have professor Pedro Domingos and we are going to talk about activism in machine learning, cancel culture, AI ethics and kernels. In Pedro's book the master algorithm, he segmented the AI community into 5 distinct tribes with 5 unique identities (and before you ask, no the irony of an anti-identitarian doing do was not lost on us!). Pedro recently published an article in Quillette called Beating Back Cancel Culture: A Case Study from the Field of Artificial Intelligence. Domingos has railed against political activism in the machine learning community and cancel culture. Recently Pedro was involved in a controversy where he asserted the NeurIPS broader impact statements are an ideological filter mechanism. Important Disclaimer: All views expressed are personal opinions. 00:00:00 Caveating 00:04:08 Main intro 00:07:44 Cancelling culture is a culture and intellectual weakness 00:12:26 Is cancel culture a post-modern religion? 00:24:46 Should we have gateways and gatekeepers? 00:29:30 Does everything require broader impact statements? 00:33:55 We are stifling diversity (of thought) not promoting it. 00:39:09 What is fair and how to do fair? 00:45:11 Models can introduce biases by compressing away minority data 00:48:36 Accurate but unequal soap dispensers 00:53:55 Agendas are not even self-consistent 00:56:42 Is vs Ought: all variables should be used for Is 01:00:38 Fighting back cancellation with cancellation? 01:10:01 Intent and degree matter in right vs wrong. 01:11:08 Limiting principles matter 01:15:10 Gradient descent and kernels 01:20:16 Training Journey matter more than Destination 01:24:36 Can training paths teach us about symmetry? 01:28:37 What is the most promising path to AGI? 01:31:29 Intelligence will lose its mystery

Dedicamos este episódio a explorar alguns casos notáveis de aplicação da Inteligência Artificial em Portugal. O objetivo da equipa não é o de conseguir cobrir todos os casos mas de explorar alguns casos em diferentes indústrias, empresas e academias que estão a aplicar ou investigar AI em Portugal. É a nossa esperança que seja um episódio que sirva para dar mais visibilidade aos casos de uso que temos em Portugal e que possa motivar mais empresas e indivíduos a investir nesta área. Deixamos aqui alguns links que são referidos no debate da equipa para que possam ler mais: Data Science Portugal: https://www.meetup.com/datascienceportugal/events/275548767/ Covid19pt-Data: https://github.com/dssg-pt/covid19pt-data Artigo de Pedro Domingos sobre Deep Learning: https://news.cs.washington.edu/2020/12/02/uncovering-secrets-of-the-black-box-pedro-domingos-author-of-the-master-algorithm-shines-new-light-on-the-inner-workings-of-deep-learning-models/ AI News: Artificial Intelligence in Healthcare: A Perspective from Portugal https://www.youtube.com/watch?v=RojF6gtjn8Q&ab_channel=EITHealthInnoStars Police use of Clearview AI's facial recognition increased 26% after Capitol raid https://artificialintelligence-news.com/2021/01/11/police-use-clearview-ai-facial-recognition-increased-26-capitol-raid/ AI powered 'virtual patient' allows remote medical training - Medical Device News by Guided Solutions https://news.guidedsolutions.co.uk/ai-powered-virtual-patient-allows-remote-medical-training/

Juntem-se a nós para mais uma conversa muito interessante sobre AI. Neste episódio decidimos abordar o tópico da deteção de anomalias, falando dos mitos e espectativas que de vez em quando, são inflacionadas nestas abordagens. Vamos partilhar alguns casos práticos sobre este tema, acompanhados com uma conversa descontraída sobre este tópico tão falado do ML. Links da IA News: Uncovering secrets of the “black box”: Pedro Domingos, author of “The Master Algorithm,” shares new work examining the inner workings of deep learning models: https://news.cs.washington.edu/2020/12/02/uncovering-secrets-of-the-black-box-pedro-domingos-author-of-the-master-algorithm-shines-new-light-on-the-inner-workings-of-deep-learning-models/ Chinese AI chipmaker Horizon endeavours to raise $700M to rival NVIDIA: https://artificialintelligence-news.com/2020/12/22/chinese-ai-chipmaker-horizon-raise-700m-rival-nvidia/ Making datasets inclusive from the ground up: https://www.marketplace.org/shows/marketplace-tech/making-datasets-inclusive-from-the-ground-up/

Welcome to the Christmas special community edition of MLST! We discuss some recent and interesting papers from Pedro Domingos (are NNs kernel machines?), Deepmind (can NNs out-reason symbolic machines?), Anna Rodgers - When BERT Plays The Lottery, All Tickets Are Winning, Prof. Mark Bishop (even causal methods won't deliver understanding), We also cover our favourite bits from the recent Montreal AI event run by Prof. Gary Marcus (including Rich Sutton, Danny Kahneman and Christof Koch). We respond to a reader mail on Capsule networks. Then we do a deep dive into Type Theory and Lambda Calculus with community member Alex Mattick. In the final hour we discuss inductive priors and label information density with another one of our discord community members. Panel: Dr. Tim Scarfe, Yannic Kilcher, Alex Stenlake, Dr. Keith Duggar Enjoy the show and don't forget to subscribe! 00:00:00 Welcome to Christmas Special! 00:00:44 SoTa meme 00:01:30 Happy Christmas! 00:03:11 Paper -- DeepMind - Outperforming neuro-symbolic models with NNs (Ding et al) 00:08:57 What does it mean to understand? 00:17:37 Paper - Prof. Mark Bishop Artificial Intelligence is stupid and causal reasoning wont fix it 00:25:39 Paper -- Pedro Domingos - Every Model Learned by Gradient Descent Is Approximately a Kernel Machine 00:31:07 Paper - Bengio - Inductive Biases for Deep Learning of Higher-Level Cognition 00:32:54 Anna Rodgers - When BERT Plays The Lottery, All Tickets Are Winning 00:37:16 Montreal AI event - Gary Marcus on reasoning 00:40:37 Montreal AI event -- Rich Sutton on universal theory of AI 00:49:45 Montreal AI event -- Danny Kahneman, System 1 vs 2 and Generative Models ala free energy principle 01:02:57 Montreal AI event -- Christof Koch - Neuroscience is hard 01:10:55 Markus Carr -- reader letter on capsule networks 01:13:21 Alex response to Marcus Carr 01:22:06 Type theory segment -- with Alex Mattick from Discord 01:24:45 Type theory segment -- What is Type Theory 01:28:12 Type theory segment -- Difference between functional and OOP languages 01:29:03 Type theory segment -- Lambda calculus 01:30:46 Type theory segment -- Closures 01:35:05 Type theory segment -- Term rewriting (confluency and termination) 01:42:02 MType theory segment -- eta term rewritig system - Lambda Calculus 01:54:44 Type theory segment -- Types / semantics 02:06:26 Type theory segment -- Calculus of constructions 02:09:27 Type theory segment -- Homotopy type theory 02:11:02 Type theory segment -- Deep learning link 02:17:27 Jan from Discord segment -- Chrome MRU skit 02:18:56 Jan from Discord segment -- Inductive priors (with XMaster96/Jan from Discord) 02:37:59 Jan from Discord segment -- Label information density (with XMaster96/Jan from Discord) 02:55:13 Outro

Convidado: Prof. Arlindo Oliveira Livros: A Revolução do Algoritmo Mestre de Pedro Domingos; Factfulness de Hans Rosling; Human Compatible de Stuart Russell; Iluminismo Agora de Steven Pinker; Inteligência Artificial de Arlindo Oliveira; Sapiens de Yuval Harari; Superinteligência de Nick Bostrom. Apresentação: 1:37 Perguntas: 21:23 - Escreveu mais de 150 artigos científicos e é autor de 3 livros. - Foi presidente do Instituto Superior Técnico e do INESC-ID, onde atualmente atua como professor e investigador, respetivamente. -Licenciou-se em Engenharia Eletrónica pelo Instituto Superior Técnico e completou o doutoramento em electrical engineering & computer sciences na UC Berkeley, Califórnia.

How the Quest For the Ultimate Learning Machine Will Remake Our World. According to Pedro Domingos, one of the greatest mysteries of the universe is not how it begins or ends, or what infinitesimal threads it’s woven from, it’s what goes on in a small child’s mind: how a pound of gray jelly can grow into a seat of consciousness. Even more astonishing is how little role parents play in teaching the brain to go through this transformation, as it largely does it all on its own. Today, scientists, computer engineers, and more are working towards a machine that can do exactly what the human brain does: learn. With all the technology of today, machines may one day even become smarter than the human brain. Computers can learn from large sets of data that we may not even realize is getting collected. This means that our future can be run by technology, changing the way we live and interact with each other. As you read, you’ll learn how machines will one day be like the human brain, how there is no such thing as a perfect algorithm, and how a Master Algorithm is on its way to being created. *** Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

How the Quest For the Ultimate Learning Machine Will Remake Our World. According to Pedro Domingos, one of the greatest mysteries of the universe is not how it begins or ends, or what infinitesimal threads it’s woven from, it’s what goes on in a small child’s mind: how a pound of gray jelly can grow into a seat of consciousness. Even more astonishing is how little role parents play in teaching the brain to go through this transformation, as it largely does it all on its own. Today, scientists, computer engineers, and more are working towards a machine that can do exactly what the human brain does: learn. With all the technology of today, machines may one day even become smarter than the human brain. Computers can learn from large sets of data that we may not even realize is getting collected. This means that our future can be run by technology, changing the way we live and interact with each other. As you read, you’ll learn how machines will one day be like the human brain, how there is no such thing as a perfect algorithm, and how a Master Algorithm is on its way to being created. *** Do you want more free audiobook summaries like this? Download our app for free at QuickRead.com/App and get access to hundreds of free book and audiobook summaries.

Cristina flexes her critical thinking muscles and investigates the legitimacy of why 10,000 steps a day is promoted as the magical number to stay healthy and fit. Adam looks at statistics to check claims that fatal anti-transgender violence is a national epidemic in the U.S. Lastly, Darren gives an overview of Pedro Domingos’ book, “The Master Algorithm”, a thought-provoking look at machine learning.

In this week's episode, I talk to Pedro Domingos, author of the bestselling book, The Master Algorithm, which is about the ongoing effort to unify machine-learning paradigms in a single model. But the conversation was much broader than that. Pedro believes strongly that the great powers are engaged in an AI arms race with America's Defense Advanced Research Projects Agency, or Darpa, pitted against China's military and industrial dynamo. We also talked about the future of democracy and authoritarianism in an AI-driven world.

David Korsunsky spent 15 years working for industry-leading technology firms, and in 2015 founded Heads Up Health, a San Francisco-based startup helping people to aggregate and learn from their own health information. The company can retrieve lab work from over 30,000 providers across the US, building a single health history and a timeline that can help to make sense of your current challenges. In this podcast, I’m talking with David about his mission to help 100 million people take control of their health. We talk about the Heads Up Health platform, which integrates with apps and devices and eliminates that dusty old pile of lab reports you weren’t sure what to do with.  David also shares his own story as a case study, demonstrating the value of having easy, mobile, shareable access to all of your health information. Here’s the outline of this interview with David Korsunsky: [00:01:09] Heads Up Health. [00:01:24] Robb Wolf's Podcast featuring Dave Korsunsky. [00:02:08] The story behind Heads up Health. [00:05:48] WellnessFx. [00:06:18] Applying engineering mindset to health. [00:11:36] Devices; Oura ring. [00:11:53] Elite HRV; CorSense device; Jason Moore. [00:13:30] MyFitnessPal, My Macros+, Cronometer; Keto-Mojo. [00:13:56] LEVL, Ketonix. [00:16:51] requestatest.com; Grace Liu; Ulta Labs. [00:17:17] Blood Chemistry Calculator. [00:18:20] DUTCH, OAT, Genova, Doctor's Data, BioHealth Labs. [00:22:50] Reference Ranges. [00:26:14] Dave Feldman; Podcast: How to Drop Your Cholesterol. [00:28:52] Tracking symptoms; seizures. [00:29:51] Potential applications of machine learning. [00:32:28] Elimination diet. [00:33:30] Video: Bryan's H. Pylori case study. [00:35:28] 23andme DNA testing. [00:36:49] Data-Driven Health Radio: Episode 20 - Carrie Brown. [00:37:26] Care team access. [00:39:18] Dexcom 5; Quantified Self; Freestyle Libre, Continuous Glucose Monitoring (CGM). [00:41:14] Dr. Simon Marshall, PhD. Podcasts: 1, 2, 3, 4. [00:42:40] Challenges to progress; Fast Healthcare Interoperability Resources (FHIR) movement. [00:43:12] Podcast: How to Teach Machines That Can Learn, with Pedro Domingos, PhD. [00:44:15] mint.com. [00:46:21] Amazon AWS for data storage. [00:47:53] Data-Driven Health Radio podcast. [00:49:44] How to get started on Heads up Health. [00:52:41] dave@headsuphealth.com.

Jerry Talton, leader of the Machine-Learning Services team at Slack, talks about picking up after a failed startup, design-thinking for machine learning, and important lessons from managing. Slack "Doggfooding" StarSpace General Management Course The No Asshole Rule- Robert Sutton Creativity, Inc.- Ed Catmull West Wing- Down in a Hole Giant Robots ep 256- Stay Hungry, Stay Learning 'A Few Useful Things to Know About Machine Learning'- Pedro Domingos 'Naive Bayes Models for Probability Estimation'- Lowd & Domingos Jerry on Twitter See open positions at thoughtbot! Become a Sponsor of Giant Robots!

Pedro Domingos, author of The Master Algorithm, on how AI will reshape businesses. 

Pedro Domingos joins Political Economy to discuss his new book "The Master Algorithm." The post https://www.aei.org/multimedia/ep-101-artificial-intelligence-and-the-hunt-for-the-master-algorithm/ (Ep. 101: Artificial intelligence and the hunt for the master algorithm— Political Economy with James Pethokoukis) appeared first on https://www.aei.org (American Enterprise Institute - AEI).

In this week’s episode, Kyle Polich interviews Pedro Domingos about his book, The Master Algorithm: How the quest for the ultimate learning machine will remake our world. In the book, Domingos describes what machine learning is doing for humanity, how it works and what it could do in the future. He also hints at the possibility of an ultimate learning algorithm, in which the machine uses it will be able to derive all knowledge — past, present, and future.

This week Matt and Adam review THE MASTER ALGORITHM by Pedro Domingos, a book about a universal algorithm capable of extracting ALL KNOWLEDGE from data. Seems useful, right? Your hosts are not convinced... If you'd like to support the show, consider buying us a coffee at https://www.buymeacoffee.com/randomtalkers. You can check out this episode and more on the Random Talkers YouTube channel: www.youtube.com/c/RandomTalkers. Peruse our old segments, leave an Internet comment, or maybe just admire Matt’s amazingly consistent outfit choices.

In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning machines. Second, we discuss how first-order logic can be used to represent common sense knowledge. Third, we describe a large database of common sense knowledge where the knowledge is represented using first-order logic which is free for researchers in machine learning. We provide a hyperlink to this free database of common sense knowledge. Fourth, we discuss some problems of first-order logic and explain how these problems can be resolved by transforming logical rules into probabilistic rules using Markov Logic Nets. And finally, we have another book review of the book “Markov Logic: An Interface Layer for Artificial Intelligence” by Pedro Domingos and Daniel Lowd which provides further discussion of the issues in this podcast. In this book review, we cover some additional important applications of Markov Logic Nets not covered in detail in this podcast such as: object labeling, social network link analysis, information extraction, and helping support robot navigation. Finally, at the end of the podcast we provide information about a free software program which you can use to build and evaluate your own Markov Logic Net! For more information check out: www.learningmachines101.com  

Dr. Gari Clifford, DPhil has been studying artificial intelligence (AI) and its utility in healthcare for two decades. He holds several prestigious positions in academia and is an Associate Professor of Biomedical Informatics at Emory University and an Associate Professor of Biomedical Engineering at Georgia Institute of Technology. We met him at the San Francisco Data Institute Conference in October where he chaired sessions on Machine Learning and Health. Gari recently held a competition challenging data scientists to develop predictive algorithms for the early detection of Atrial Fibrillation, using mobile ECG machines. He shares insight into the complexity of using AI to diagnose health conditions and offers a glimpse into the future of healthcare and medical information. Here’s the outline of this interview with Gari Clifford: [00:01:07] The road to machine learning and mobile health. [00:01:27] Lionel Tarassenko: neural networks and artificial intelligence. [00:03:36] San Francisco Data Institute Conference. [00:03:54] Jeremy Howard at fast.ai. [00:04:17] Director of Data Institute David Uminsky. [00:05:05] Dr. Roger Mark, Computing in Cardiology PhysioNet Challenges. [00:05:23] 2017 Challenge: Detecting atrial fibrillation in electrocardiograms. [00:05:44] Atrial Fibrillation. [00:06:08] KardiaMobile EKG monitor by AliveCor. [00:06:33] Random forests, support vector machines, heuristics, deep learning. [00:07:23] Experts don't always agree. [00:08:33] Labeling ECGs: AF, normal sinus rhythm, another rhythm, or noisy. [00:09:07] 20-30 experts are required to discern a stable diagnosis. [00:09:40] Podcast: Arrhythmias in Endurance Athletes, with Peter Backx, PhD. [00:11:17] Applying additional algorithm on top of all final algorithms: improved score from 83% to 87% accuracy. [00:11:38] Kaggle for machine learning competitions. [00:13:44] Overfitting an algorithm increases complexity, decreases utility. [00:15:01] 10,000 ECGs are not enough. [00:16:24] Podcast: How to Teach Machines That Can Learn with Dr. Pedro Domingos. [00:16:50] XGBoost. [00:19:18] Mechanical Turk. [00:20:08] QRS onset and T-wave offset. [00:21:31] Galaxy Zoo. [00:24:00] Podcast: Jason Moore of Elite HRV. [00:24:34] Andrew Ng. Paper: Rajpurkar, Pranav, et al. "Cardiologist-level arrhythmia detection with convolutional neural networks." arXiv preprint arXiv:1707.01836 (2017). [00:28:44] Detecting arrhythmias using other biomarkers. [00:30:41] Algorithms trained on specific patient populations not accurate for other populations. [00:31:24] Propensity matching. [00:31:55] Should we be sharing our medical data? [00:32:15] Privacy concerns associated with sharing medical data. [00:32:44] Mass scale research: possible with high-quality data across a large population. [00:33:04] Selling social media data in exchange for useful or entertaining software. [00:33:42] Who touched my medical data and why? [00:36:31] Siloing data, perhaps to protect the current industries. [00:37:03] Health Insurance Portability and Privacy Act (HIPPA). [00:37:34] Fast Healthcare Interoperability Resources (FHIR) protocol. [00:37:48] Microsoft HealthVault and Google Health. [00:38:46] Blockchain and 3blue1brown. [00:39:28] Where to go to learn more about Gari Clifford. [00:39:53] Presentation: Machine learning for FDA-approved consumer level point of care diagnostics – the wisdom of algorithm crowds: (the PhysioNet Computing in Cardiology Challenge 2017).

In this episode Byron and Pedro talk about the master algorithm, machine creativity, and the creation of new jobs in the wake of the AI revolution. Episode 23: A Conversation with Pedro Domingos

In this episode Byron and Pedro talk about the master algorithm, machine creativity, and the creation of new jobs in the wake of the AI revolution. Episode 23: A Conversation with Pedro Domingos

In this episode Byron and Pedro talk about the master algorithm, machine creativity, and the creation of new jobs in the wake of the AI revolution. Episode 23: A Conversation with Pedro Domingos

After spending years directing urinary and salivary hormone testing, analytical chemist Mark Newman set out to combine the best of both worlds with the DUTCH (Dried Urine Test for Comprehensive Hormones). For the past couple of years, we’ve been happily using the DUTCH as a tool for improving health and performance in athletes as part of our Elite Performance Program. In this interview, Mark discusses the recent expansion and improvement of the DUTCH to include the cortisol awakening response (CAR), and several markers related to hormone and neurotransmitter metabolism. Here’s the outline of this interview with Mark Newman: [00:00:54] DUTCH (Dried Urine Test for Comprehensive Hormones). [00:02:49] Cortisol clearance. Video: Tutorial on cortisol. [00:03:32] 8-Hydroxy-2-deoxyguanosine (8-OH-dG). [00:05:02] Obesity. [00:05:21] Cushing’s syndrome. [00:05:44] Fat sequesters hormones. [00:08:58] Thyroid and cortisol clearance. [00:09:20] Studies: 1, 2, and 3. [00:11:51] Circadian rhythm. [00:12:39] Cortisol awakening response (CAR). [00:14:31] Studies: References 1, 2, 3, 4, and 5.                  [00:16:34] Why you can't see the CAR with urine. [00:18:08] Correlations between glucose, c-peptide, and cortisol. [00:19:50] The CAR is a proxy. [00:21:30] Clinical implications of the CAR. [00:25:28] 8-OH-dG on PubMed. [00:26:43] Joergensen, Anders, et al. “Association between urinary excretion of cortisol and markers of oxidatively damaged DNA and RNA in humans.” PLoS One 6.6 (2011): e20795. [00:27:00] Melatonin is an antioxidant. [00:27:14] 4-OH oestrogen metabolite. Video: Estrogen Tutorial. [00:28:26] Will there be a full OAT? [00:28:53] Neurotransmitters. [00:29:57] Kynurenine pathway. Article: Electrons, Neurotoxins, NAD+, and Mitochondria by Tommy Wood MD, PhD. [00:31:01] NAD and vitamin B6, xanthurenic acid. [00:32:01] MMA, folate. [00:32:52] Article: New Research: Birth Control Pill, Depression and Autoimmunity by Kelly Brogan MD. [00:33:37] Hydroxymethylglutarate (HMG) is the precursor to Coenzyme Q10 (CoQ10) production. [00:35:41] Evidence-based markers. [00:37:09] Doing experiments, DIM. [00:39:14] Adding markers, value vs noise. [00:40:58] Great Plains OAT (Organic Acids Test). [00:41:15] Podcast: The Cortisol Awakening Response with Mark Newman, MS. [00:41:39] Machine Learning. Podcasts: How to Teach Machines That Can Learn with Dr. Pedro Domingos, PhD and How “Machine Learning” Can Predict Your Blood, Urine, Stool, Saliva & More! With Dr. Tommy Wood. [00:42:16] Mass spec, immunoassay test. [00:45:17] Predicting the CAR. [00:45:56] Linear correlations. [00:50:06] Receptor activity, house analogy. [00:51:10] Elite Performance Program and the 7-Minute Analysis. [00:52:11] Getting the DUTCH done. [00:53:50] The Institute for Functional Medicine (IFM). [00:55:07] The process of elimination. [00:55:43] Precision Analytical at dutchtest.com.

If there’s one thing we can learn from histroy it’s everything that there is to know. Or at least that’s the promise of machine learning. The master algorithm by Pedro Domingos tells us how machines that learn are starting to transform the world, bringing us driverless cars and perhaps even bloodless wars. Hear an interview with Domingos, a reading from the book, and the thoughts of Royal Society of Chemistry data scientist Colin Batchelor and Chemistry World’s digital content producer, Sam Tracey, who join host Emma Stoye.

Machine learning is fast becoming a part of our lives. From the order in which your search results and news feeds are ordered to the image classifiers and speech recognition features on your smartphone. Machine learning may even have had a hand in choosing your spouse or driving you to work. As with cars, only the mechanics need to understand what happens under the hood, but all drivers need to know how to operate the steering wheel. Listen to this podcast to learn how to interact with machines that can learn, and about the implications for humanity. My guest is Dr. Pedro Domingos, Professor of Computer Science at Washington University. He is the author or co-author of over 200 technical publications in machine learning and data mining, and the author of my new favourite book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Here’s the outline of this interview with Dr. Pedro Domingos, PhD: [00:01:55] Deep Learning. [00:02:21] Machine learning is affecting everyone's lives. [00:03:45] Recommender systems. [00:03:57] Ordering newsfeeds. [00:04:25] Text prediction and speech recognition in smart phones. [00:04:54] Accelerometers. [00:04:54] Selecting job applicants. [00:05:05] Finding a spouse. [00:05:35] OKCupid.com. [00:06:49] Robot scientists. [00:07:08] Artificially-intelligent Robot Scientist ‘Eve’ could boost search for new drugs. [00:08:38] Cancer research. [00:10:27] Central dogma of molecular biology. [00:10:34] DNA microarrays. [00:11:34] Robb Wolf at IHMC: Darwinian Medicine: Maybe there IS something to this evolution thing. [00:12:29] It costs more to find the data than to do the experiment again (ref?) [00:13:11] Making connections people could never make. [00:14:00] Jeremy Howard’s TED talk: The wonderful and terrifying implications of computers that can learn. [00:14:14] Pedro's TED talk: The Quest for the Master Algorithm. [00:15:49] Craig Venter: your immune system on the Internet. [00:16:44] Continuous blood glucose monitoring and Heart Rate Variability. [00:17:41] Our data: DUTCH, OAT, stool, blood. [00:19:21] Supervised and unsupervised learning. [00:20:11] Clustering dimensionality reduction, e.g. PCA and T-SNE. [00:21:44] Sodium to potassium ratio versus cortisol. [00:22:24] Eosinophils. [00:23:17] Clinical trials. [00:24:35] Tetiana Ivanova - How to become a Data Scientist in 6 months a hacker’s approach to career planning. [00:25:02] Deep Learning Book. [00:25:46] Maths as a barrier to entry. [00:27:09] Andrew Ng Coursera Machine Learning course. [00:27:28] Pedro's Data Mining course. [00:27:50] Theano and Keras. [00:28:02] State Farm Distracted Driver Detection Kaggle competition. [00:29:37] Nearest Neighbour algorithm. [00:30:29] Driverless cars. [00:30:41] Is a robot going to take my job? [00:31:29] Jobs will not be lost, they will be transformed [00:33:14] Automate your job yourself! [00:33:27] Centaur chess player. [00:35:32] ML is like driving, you can only learn by doing it. [00:35:52] A Few Useful Things to Know about Machine Learning. [00:37:00] Blood chemistry software. [00:37:30] We are the owners of our data. [00:38:49] Data banks and unions. [00:40:01] The distinction with privacy. [00:40:29] An ethical obligation to share. [00:41:46] Data vulcanisation. [00:42:40] Teaching the machine. [00:43:07] Chrome incognito mode. [00:44:13] Why can't we interact with the algorithm? [00:45:33] New P2 Instance Type for Amazon EC2 – Up to 16 GPUs. [00:46:01] Why now? [00:46:47] Research breakthroughs. [00:47:04] The amount of data. [00:47:13] Hardware. [00:47:31] GPUs, Moore’s law. [00:47:57] Economics. [00:48:32] Google TensorFlow. [00:49:05] Facebook Torch. [00:49:38] Recruiting. [00:50:58] The five tribes of machine learning: evolutionaries, connectionists, Bayesians, analogizers, symbolists. [00:51:55] Grand unified theory of ML. [00:53:40] Decision tree ensembles (Random Forests). [00:53:45] XGBoost. [00:53:54] Weka. [00:54:21] Alchemy: Open Source AI. [00:56:16] Still do a computer science degree. [00:56:54] Minor in probability and statistics.

Dr. Pedro Domingos, Professor at the University of Washington, joins us live from Intel Developer Forum in San Francisco. Dr. Domingos has conducted research in the field of machine learning for more than 20 years. In this interview, Dr. Domingos discusses the critical need for more people to better understand machine learning, progress towards a grand unified theory of machine learning, and how the world may be transformed by computers that are programmed to learn rather than programmed to do. For more information on Dr. Domingos' work, look for his book, "The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World" (http://amzn.to/2c8WrlG), and follow him on Twitter at https://twitter.com/pmddomingos.

In this interview with AI expert Pedro Domingos, you’ll learn about self-driving cars, where knowledge comes from, and the 5 schools of machine learning. GO PREMIUM: Support the podcast, get ad-free episodes, transcripts, and so much more: https://fs.blog/knowledge-project-premium/

What is machine learning? How is it transforming our lives and workplaces? What might the future hold? Pedro Domingos of the University of Washington and author of The Master Algorithm talks with EconTalk host Russ Roberts about the present and future of machine learning. Domingos stresses the iterative and ever-improving nature of machine learning. He is fundamentally an optimist about the potential of machine learning with ever-larger amounts of data to transform the human experience.

When Pedro Domingos was growing up in Lisbon, he loved to read and to learn. In fact, when he was 13 years old, he decided that he wanted to learn everything that there is to know. It did not take him long to decide that it might be a little more useful to, instead of knowing everything, to become deeply knowledgeable about a few topics. His university studies led him to computer science in the 1980s, just as personal computers were beginning to catch on. Today, Pedro is a professor at the University of Washington and the author of The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. In this book he describes the five schools of thought on machine learning and advocates for a grand unifying theory. In this episode of the Social Entrepreneur podcast, we discuss: The benefit of deep expertise in one field of knowledge, when balanced with a broad interests in other fields. The exponential growth of computer processing power. The equally explosive growth of software. The power of algorithms, and how three words, “and, or, not” are changing our world. How machine learning is a technology that builds itself. How machine learning becomes culturally acceptable. Examples of disruptions brought about my machine learning. The surprising impact of machine learning on knowledge workers. How the combination of machine learning with robotics is brining machine learning into the physical world. The business impact of being able to lease machine learning or even find algorithms for free on GitHub. Where machine learning is going as a practice. Resources: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World: http://amzn.to/1PJinUa Pedro Domingos on the web: http://pedrodomingos.org Pedro Domingos on Twitter: https://twitter.com/pmddomingos GitHub: https://github.com Give Well: http://www.givewell.org The Abdul Latif Jameel Poverty Action Lab at MIT: http://www.povertyactionlab.org

SHOW SUMMARY – Pedro Domingos joins us for a fascinating discussion on machine learning and his new book “The Master Algorithm: How the Quest for the Ultimate […]

Pedro Domingos is a professor of computer science at the University of Washington in Seattle. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. Professor Domingos is a fellow of the Association for the Advancement of Artificial Intelligence and his new book is The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Info at pedrodomingos.org

In episode 20 we chat with Pedro Domingos of the University of Washington, he's just published a book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. We get some insight into Linear Dynamical Systems which the Datta Lab at Harvard Medical School is doing some interesting work with. Plus, we take a listener question about using video games to generate labeled data (spoiler alert, it's an awesome idea!)We're in the final hours of our Fundraising Campaign and we need your help!