University of Washington

NASA’s Artemis II mission, which will send humans around the moon for the first time in over five decades, could launch as early as March. This is part of a larger campaign to establish a long-term presence on the moon and eventually prepare for human space flight to Mars. Meanwhile, China also has a goal of landing humans on the moon by 2030, setting up a kind of modern space race. One reason for the rush: It's like a game of finders keepers, said Saadia Pekkanen, a professor focused on space law and policy at the University of Washington.

Saadia Pekkanen

Professor of Space Law and Policy

University of Washington

Today, we're joined by Yejin Choi, professor and senior fellow at Stanford University in the Computer Science Department and the Institute for Human-Centered AI (HAI). In this conversation, we explore Yejin’s recent work on making small language models reason more effectively. We discuss how high-quality, diverse data plays a central role in closing the intelligence gap between small and large models, and how combining synthetic data generation, imitation learning, and reinforcement learning can unlock stronger reasoning capabilities in smaller models. Yejin explains the risks of homogeneity in model outputs and mode collapse highlighted in her “Artificial Hivemind” paper, and its impacts on human creativity and knowledge. We also discuss her team's novel approaches, including reinforcement learning as a pre-training objective, where models are incentivized to “think” before predicting the next token, and "Prismatic Synthesis," a gradient-based method for generating diverse synthetic math data while filtering overrepresented examples. Additionally, we cover the societal implications of AI and the concept of pluralistic alignment—ensuring AI reflects the diverse norms and values of humanity. Finally, Yejin shares her mission to democratize AI beyond large organizations and offers her predictions for the coming year. The complete show notes for this episode can be found at https://twimlai.com/go/761.

Yejin Choi

Professor & Senior Fellow

Stanford UniversityUniversity of WashingtonAllen Institute for AI

Pedro Domingos, author of the bestselling book "The Master Algorithm," introduces his latest work: Tensor Logic - a new programming language he believes could become the fundamental language for artificial intelligence. Think of it like this: Physics found its language in calculus. Circuit design found its language in Boolean logic. Pedro argues that AI has been missing its language - until now. **SPONSOR MESSAGES START** — Build your ideas with AI Studio from Google - http://ai.studio/build — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — **END** Current AI is split between two worlds that don't play well together: Deep Learning (neural networks, transformers, ChatGPT) - great at learning from data, terrible at logical reasoning Symbolic AI (logic programming, expert systems) - great at logical reasoning, terrible at learning from messy real-world data Tensor Logic unifies both. It's a single language where you can: Write logical rules that the system can actually learn and modify Do transparent, verifiable reasoning (no hallucinations) Mix "fuzzy" analogical thinking with rock-solid deduction INTERACTIVE TRANSCRIPT: https://app.rescript.info/public/share/NP4vZQ-GTETeN_roB2vg64vbEcN7isjJtz4C86WSOhw TOC: 00:00:00 - Introduction 00:04:41 - What is Tensor Logic? 00:09:59 - Tensor Logic vs PyTorch & Einsum 00:17:50 - The Master Algorithm Connection 00:20:41 - Predicate Invention & Learning New Concepts 00:31:22 - Symmetries in AI & Physics 00:35:30 - Computational Reducibility & The Universe 00:43:34 - Technical Details: RNN Implementation 00:45:35 - Turing Completeness Debate 00:56:45 - Transformers vs Turing Machines 01:02:32 - Reasoning in Embedding Space 01:11:46 - Solving Hallucination with Deductive Modes 01:16:17 - Adoption Strategy & Migration Path 01:21:50 - AI Education & Abstraction 01:24:50 - The Trillion-Dollar Waste REFS Tensor Logic: The Language of AI [Pedro Domingos] https://arxiv.org/abs/2510.12269 The Master Algorithm [Pedro Domingos] https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543 Einsum is All you Need (TIM ROCKTÄSCHEL) https://rockt.ai/2018/04/30/einsum https://www.youtube.com/watch?v=6DrCq8Ry2cw Autoregressive Large Language Models are Computationally Universal (Dale Schuurmans et al - GDM) https://arxiv.org/abs/2410.03170 Memory Augmented Large Language Models are Computationally Universal [Dale Schuurmans] https://arxiv.org/pdf/2301.04589 On the computational power of NNs [95/Siegelmann] https://binds.cs.umass.edu/papers/1995_Siegelmann_JComSysSci.pdf Sebastian Bubeck https://www.reddit.com/r/OpenAI/comments/1oacp38/openai_researcher_sebastian_bubeck_falsely_claims/ I am a strange loop - Hofstadter https://www.amazon.co.uk/Am-Strange-Loop-Douglas-Hofstadter/dp/0465030793 Stephen Wolfram https://www.youtube.com/watch?v=dkpDjd2nHgo The Complex World: An Introduction to the Foundations of Complexity Science [David C. Krakauer] https://www.amazon.co.uk/Complex-World-Introduction-Foundations-Complexity/dp/1947864629 Geometric Deep Learning https://www.youtube.com/watch?v=bIZB1hIJ4u8 Andrew Wilson (NYU) https://www.youtube.com/watch?v=M-jTeBCEGHc Yi Ma https://www.patreon.com/posts/yi-ma-scientific-141953348 Roger Penrose - road to reality https://www.amazon.co.uk/Road-Reality-Complete-Guide-Universe/dp/0099440687 Artificial Intelligence: A Modern Approach [Russel and Norvig] https://www.amazon.co.uk/Artificial-Intelligence-Modern-Approach-Global/dp/1292153962

Pedro Domingos

Professor

University of Washington

The rapidly evolving field of protein design is revealing solutions to some of the world’s greatest problems, whether it's blocking a virus, breaking down a pollutant or creating brand-new materials. In conversation with TED’s Whitney Pennington Rodgers, biochemist David Baker explores his team’s Nobel Prize-winning work using AI to design new proteins with functions never before seen in nature — achieving breakthroughs that have fundamentally changed the future of science. (This conversation was part of an exclusive TED Membership event. TED Membership is the best way to support and engage with the big ideas you love from TED. To learn more, visit ted.com/membership.) Hosted on Acast. See acast.com/privacy for more information.

David Baker

Biochemist and Professor

University of WashingtonInstitute for Protein Design

Emily  Bender is a computational linguistics professor at the University of Washington. Alex Hanna is the Director of Research at the Distributed AI Research Institute. Bender and Hanna join Big Technology to discuss what their new book, “The AI‑Con," which they describe as the layered ways today’s language‑model boom obscures environmental costs, labor harms, and shaky science. Tune in to hear a lively back‑and‑forth on whether chatbots are useful tools or polished parlor tricks. We also cover benchmark gaming, data‑center water use, doomerism, and more. Hit play for a candid debate that will leave you smarter about where generative AI really stands — and what comes next. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack? Here’s 25% off for the first year, which includes membership to our subscriber Discord: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: bigtechnologypodcast@gmail.com Learn more about your ad choices. Visit megaphone.fm/adchoices

Emily M. Bender

Professor

University of Washington

Alex Hanna

Director of Research

Distributed AI Research Institute

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. 📚 About Pedro Domingos: Pedro is a professor at the University of Washington and author of the bestselling book The Master Algorithm, which explores how the unification of AI's "five tribes" could produce the ultimate learning algorithm. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) The Five Tribes of AI Explained (02:23) The Origins of The Master Algorithm (08:22) Designing with Bit Strings: Radios, Robots & More (10:46) Fitness Functions vs Reward Functions in AI (15:51) What Is Reasoning by Analogy in AI? (18:38) Kernel Machines and Support Vector Machines Explained (22:23) Case-Based Reasoning and Real-World Use Cases (27:38) Are AI Tribes Still Siloed or Finally Collaborating? (32:42) Why AI Needs a Deeply Unified Master Algorithm (36:40) Creativity vs Reliability in AI (39:14) Can AI Achieve Scientific Breakthroughs? (41:26) Why Reasoning by Analogy Is AI's Missing Link (45:10) Evolutionaries: The Most Distant Tribe in AI (48:41) Will Quantum Computing Help AI Reach AGI? (53:15) Are We Close to the Master Algorithm? (57:44) Tech Leaders, Hype & the Reality of AGI (01:04:06) The AGI Spectrum: Where We Are & What's Missing (01:06:18) Pedro's Research Focus

Pedro Domingos

Professor

University of Washington

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

Pedro Domingos

Professor

University of Washington

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?

Pedro Domingos

Professor

University of Washington

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

Pedro Domingos

Professor

University of Washington

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

Pedro Domingos

Professor

University of Washington

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

Pedro Domingos

Professor

University of Washington

Today, we're joined by Marius Memmel, a PhD student at the University of Washington, to discuss his research on sim-to-real transfer approaches for developing autonomous robotic agents in unstructured environments. Our conversation focuses on his recent ASID and URDFormer papers. We explore the complexities presented by real-world settings like a cluttered kitchen, data acquisition challenges for training robust models, the importance of simulation, and the challenge of bridging the sim2real gap in robotics. Marius introduces ASID, a framework designed to enable robots to autonomously generate and refine simulation models to improve sim-to-real transfer. We discuss the role of Fisher information as a metric for trajectory sensitivity to physical parameters and the importance of exploration and exploitation phases in robot learning. Additionally, we cover URDFormer, a transformer-based model that generates URDF documents for scene and object reconstruction to create realistic simulation environments. The complete show notes for this episode can be found at https://twimlai.com/go/695.

Marius Memmel

PhD Student

University of Washington

Today we’re joined by Luke Zettlemoyer, professor at University of Washington and a research manager at Meta. In our conversation with Luke, we cover multimodal generative AI, the effect of data on models, and the significance of open source and open science. We explore the grounding problem, the need for visual grounding and embodiment in text-based models, the advantages of discretization tokenization in image generation, and his paper Scaling Laws for Generative Mixed-Modal Language Models, which focuses on simultaneously training LLMs on various modalities. Additionally, we cover his papers on Self-Alignment with Instruction Backtranslation, and LIMA: Less Is More for Alignment. The complete show notes for this episode can be found at twimlai.com/go/650.

Luke Zettlemoyer

Professor and Research Manager

University of WashingtonMeta

Today we’re joined by Su-In Lee, a professor at the Paul G. Allen School of Computer Science And Engineering at the University Of Washington. In our conversation, Su-In details her talk from the ICML 2023 Workshop on Computational Biology which focuses on developing explainable AI techniques for the computational biology and clinical medicine fields. Su-In discussed the importance of explainable AI contributing to feature collaboration, the robustness of different explainability approaches, and the need for interdisciplinary collaboration between the computer science, biology, and medical fields. We also explore her recent paper on the use of drug combination therapy, challenges with handling biomedical data, and how they aim to make meaningful contributions to the healthcare industry by aiding in cause identification and treatments for Cancer and Alzheimer's diseases. The complete show notes for this episode can be found at twimlai.com/go/642.

Su-In Lee

Professor

University of Washington

Yejin Choi joins Host Pieter Abbeel to discuss how we can teach AI common sense and morality and what ChatGPT can't do yet. Subscribe to the Robot Brains Podcast today | Visit therobotbrains.ai and follow us on YouTube at TheRobotBrainsPodcast and Twitter @therobotbrains. Hosted on Acast. See acast.com/privacy for more information.

Yejin Choi

Professor & Senior Fellow

Stanford UniversityUniversity of WashingtonAllen Institute for AI

Today on the flagship podcast of questionable .mkv files:  02:46 - The Verge's David Pierce chats with Matt Johnson, director of the upcoming movie BlackBerry about what tech movies get wrong, why the BlackBerry really died, and how to portray the rise and fall of a top-of-the-world gadget. BlackBerry director Matt Johnson on why the iPhone won and why most tech movies suck 30:38 - David and Vergecast producer Andru Marino try to find out why it's so hard to find director's commentary on streaming services and the obstacles movie fans go through to listen to them. Where’s the director’s commentary on streaming? 57:25 - David talks with the directors and producer of the movie Missing about a new genre of movies that take place entirely on a computer screen, and how they get made. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Matt Johnson

Director

B(BlackBerry (film)University of Washington

Computer scientist Yejin Choi is here to demystify the current state of massive artificial intelligence systems like ChatGPT, highlighting three key problems with cutting-edge large language models (including some funny instances of them failing at basic commonsense reasoning.) She welcomes us into a new era in which AI is becoming almost like a new intellectual species -- and identifies the benefits of building smaller AI systems trained on human norms and values. (Followed by a Q&A with head of TED Chris Anderson) Hosted on Acast. See acast.com/privacy for more information.

Yejin Choi

Professor & Senior Fellow

Stanford UniversityUniversity of WashingtonAllen Institute for AI

Today on the flagship podcast of zombie kisses: 02:02 - The Verge’s managing editor Alex Cranz chats with film & TV reporter Charles Pulliam-Moore about HBO’s The Last of Us and how it handles the video game adaptation. [Spoilers for episode 1 + 2] 22:40 - Historian and author of the book The Code: Silicon Valley and the Remaking of America Margaret O'Mara talks about how the lack of non-compete clauses shaped Silicon Valley. 38:30 - We answer your questions left on our Vergecast Hotline! Thunderbolt docks, end-to-end encryption, and smart assistants. Email us at vergecast@theverge.com or call us at 866-VERGE11, we'd love to hear from you. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Margaret O'Mara

Historian and Author

University of Washington

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

Pedro Domingos

Professor

University of Washington

The US economy may not be in a recession, but Silicon Valley, which had a mega-boom throughout the 2010s, is in a downturn. Tech stocks have tanked and almost every day there are new reports about industry layoffs. So what happens next? What happens to its unique corporate culture? What happens to management and employees? On this episode, we speak with Margaret O'Mara, a professor at the University of Washington and the author of the book The Code: Silicon Valley and the Remaking of America. We talk about the history of Silicon Valley's upside-down moments and how the industries that have dominated the region have changed over time, particularly as government money comes in and out of the picture. See omnystudio.com/listener for privacy information.

Margaret O'Mara

Historian and Author

University of Washington

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

Pedro Domingos

Professor

University of Washington

Esther and Matt are graduate students in computer science at the University of Washington, where they study community networks. Esther explains how open-source, community-owned and -operated LTE networks are a good solution for expanding public internet access and ensuring digital equity. Matt walks the team through Citizens Broadband Radio Service (CBRS), a shared wireless spectrum that allows users to build their own LTE networks. Chris Webb of the Black Brilliance Research Project lays out how a digital stewardship program in Detroit helped inspire his work. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Esther Jang

PhD Researcher

University of Washington

Matt Johnson

Director

University of WashingtonB(BlackBerry (film)

Today we’re joined by Yejin Choi, a professor at the University of Washington. We had the pleasure of catching up with Yejin after her keynote interview at the recent Stanford HAI “Foundational Models” workshop. In our conversation, we explore her work at the intersection of natural language generation and common sense reasoning, including how she defines common sense, and what the current state of the world is for that research. We discuss how this could be used for creative storytelling, how transformers could be applied to these tasks, and we dig into the subfields of physical and social common sense reasoning. Finally, we talk through the future of Yejin’s research and the areas that she sees as most promising going forward.  If you enjoyed this episode, check out our conversation on AI Storytelling Systems with Mark Riedl. The complete show notes for today’s episode can be found at twimlai.com/go/518.

Yejin Choi

Professor & Senior Fellow

Stanford UniversityUniversity of WashingtonAllen Institute for AI

In this episode, Emily and Lukas dive into the problems with bigger and bigger language models, the difference between form and meaning, the limits of benchmarks, and why it's important to name the languages we study. Show notes (links to papers and transcript): http://wandb.me/gd-emily-m-bender --- Emily M. Bender is a Professor of Linguistics at and Faculty Director of the Master's Program in Computational Linguistics at University of Washington. Her research areas include multilingual grammar engineering, variation (within and across languages), the relationship between linguistics and computational linguistics, and societal issues in NLP. --- Timestamps: 0:00 Sneak peek, intro 1:03 Stochastic Parrots 9:57 The societal impact of big language models 16:49 How language models can be harmful 26:00 The important difference between linguistic form and meaning 34:40 The octopus thought experiment 42:11 Language acquisition and the future of language models 49:47 Why benchmarks are limited 54:38 Ways of complementing benchmarks 1:01:20 The #BenderRule 1:03:50 Language diversity and linguistics 1:12:49 Outro

Emily M. Bender

Professor

University of Washington

Path-breaking work that translates an amputee’s thoughts into finger motions, and even commands in video games, holds open the possibility of humans controlling just about anything digital with their minds. Using GPUs, a group of researchers trained an AI neural decoder able to run on a compact, power-efficient NVIDIA Jetson Nano system on module (SOM) to translate 46-year-old Shawn Findley’s thoughts into individual finger motions. And if that breakthrough weren’t enough, the team then plugged Findley into a PC running Far Cry 5 and Raiden IV, where he had his game avatar move, jump — even fly a virtual helicopter — using his mind. It’s a demonstration that not only promises to give amputees more natural and responsive control over their prosthetics. It could one day give users almost superhuman capabilities. The effort is detailed in a draft paper, or pre-print, titled “A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control.” It details an extraordinary cross-disciplinary collaboration behind a system that, in effect, allows humans to control just about anything digital with thoughts. Jules Anh Tuan Nguyen, the paper’s lead author and now a postdoctoral researcher at the University of Minnesota, spoke with NVIDIA AI Podcast host Noah Kravitz about his efforts to allow amputees to control their prosthetic limb — right down to the finger motions — with their minds. blogs.nvidia.com/blog/2021/08/10/lending-a-helping-hand-jules-anh-tuan-nguyen-on-building-a-neuroprosthetic

Jules Anh Tuan Nguyen

Researcher

University of Washington

Today we’re joined by Emily M. Bender, Professor at the University of Washington, and AI Researcher, Margaret Mitchell.  Emily and Meg, as well as Timnit Gebru and Angelina McMillan-Major, are co-authors on the paper On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. As most of you undoubtedly know by now, there has been much controversy surrounding, and fallout from, this paper. In this conversation, our main priority was to focus on the message of the paper itself. We spend some time discussing the historical context for the paper, then turn to the goals of the paper, discussing the many reasons why the ever-growing datasets and models are not necessarily the direction we should be going.  We explore the cost of these training datasets, both literal and environmental, as well as the bias implications of these models, and of course the perpetual debate about responsibility when building and deploying ML systems. Finally, we discuss the thin line between AI hype and useful AI systems, and the importance of doing pre-mortems to truly flesh out any issues you could potentially come across prior to building models, and much much more.  The complete show notes for this episode can be found at twimlai.com/go/467.

Emily M. Bender

Professor

University of Washington

Margaret Mitchell

Chief Ethics Scientist

Hugging FaceGoogle

Today we’re joined by Vered Shwartz, a Postdoctoral Researcher at both the Allen Institute for AI and the Paul G. Allen School of Computer Science & Engineering at the University of Washington. In our conversation with Vered, we explore her NLP research, where she focuses on teaching machines common sense reasoning in natural language. We discuss training using GPT models and the potential use of multimodal reasoning and incorporating images to augment the reasoning capabilities. Finally, we talk through some other noteworthy research in this field, how she deals with biases in the models, and Vered's future plans for incorporating some of the newer techniques into her future research. The complete show notes for this episode can be found at https://twimlai.com/go/461.

Vered Shwartz

Postdoctoral Researcher

Allen Institute for AIUniversity of Washington

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

Pedro Domingos

Professor

University of Washington

Today we close out our NeurIPS series joined by Aravind Rajeswaran, a PhD Student in machine learning and robotics at the University of Washington. At NeurIPS, Aravind presented his paper MOReL: Model-Based Offline Reinforcement Learning. In our conversation, we explore model-based reinforcement learning, and if models are a “prerequisite” to achieve something analogous to transfer learning. We also dig into MOReL and the recent progress in offline reinforcement learning, the differences in developing MOReL models and traditional RL models, and the theoretical results they’re seeing from this research. The complete show notes for this episode can be found at twimlai.com/go/442

Aravind Rajeswaran

PhD Student

University of Washington

Today we’re joined by Emily M. Bender, Professor of Linguistics at the University of Washington. Our discussion covers a lot of ground, but centers on the question, "Is Linguistics Missing from NLP Research?" We explore if we would be making more progress, on more solid foundations, if more linguists were involved in NLP research, or is the progress we're making (e.g. with deep learning models like Transformers) just fine?

Emily M. Bender

Professor

University of Washington