Machine Learning Street Talk (MLST)

Dr. Ilia Shumailov - Former DeepMind AI Security Researcher, now building security tools for AI agentsEver wondered what happens when AI agents start talking to each other—or worse, when they start breaking things? Ilia Shumailov spent years at DeepMind thinking about exactly these problems, and he's here to explain why securing AI is way harder than you think.**SPONSOR MESSAGES**—Check out notebooklm for your research project, it's really powerfulhttps://notebooklm.google.com/—Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— We're racing toward a world where AI agents will handle our emails, manage our finances, and interact with sensitive data 24/7. But there is a problem. These agents are nothing like human employees. They never sleep, they can touch every endpoint in your system simultaneously, and they can generate sophisticated hacking tools in seconds. Traditional security measures designed for humans simply won't work.Dr. Ilia Shumailovhttps://x.com/iliaishackedhttps://iliaishacked.github.io/https://sequrity.ai/TRANSCRIPT:https://app.rescript.info/public/share/dVGsk8dz9_V0J7xMlwguByBq1HXRD6i4uC5z5r7EVGMTOC:00:00:00 - Introduction & Trusted Third Parties via ML00:03:45 - Background & Career Journey00:06:42 - Safety vs Security Distinction00:09:45 - Prompt Injection & Model Capability00:13:00 - Agents as Worst-Case Adversaries00:15:45 - Personal AI & CAML System Defense00:19:30 - Agents vs Humans: Threat Modeling00:22:30 - Calculator Analogy & Agent Behavior00:25:00 - IMO Math Solutions & Agent Thinking00:28:15 - Diffusion of Responsibility & Insider Threats00:31:00 - Open Source Security Concerns00:34:45 - Supply Chain Attacks & Trust Issues00:39:45 - Architectural Backdoors00:44:00 - Academic Incentives & Defense Work00:48:30 - Semantic Censorship & Halting Problem00:52:00 - Model Collapse: Theory & Criticism00:59:30 - Career Advice & Ross Anderson TributeREFS:Lessons from Defending Gemini Against Indirect Prompt Injectionshttps://arxiv.org/abs/2505.14534Defeating Prompt Injections by Design. Debenedetti, E., Shumailov, I., Fan, T., Hayes, J., Carlini, N., Fabian, D., Kern, C., Shi, C., Terzis, A., & Tramèr, F. https://arxiv.org/pdf/2503.18813Agentic Misalignment: How LLMs could be insider threatshttps://www.anthropic.com/research/agentic-misalignmentSTOP ANTHROPOMORPHIZING INTERMEDIATE TOKENS AS REASONING/THINKING TRACES!Subbarao Kambhampati et alhttps://arxiv.org/pdf/2504.09762Meiklejohn, S., Blauzvern, H., Maruseac, M., Schrock, S., Simon, L., & Shumailov, I. (2025). Machine learning models have a supply chain problem. https://arxiv.org/abs/2505.22778 Gao, Y., Shumailov, I., & Fawaz, K. (2025). Supply-chain attacks in machine learning frameworks. https://openreview.net/pdf?id=EH5PZW6aCrApache Log4j Vulnerability Guidancehttps://www.cisa.gov/news-events/news/apache-log4j-vulnerability-guidance Bober-Irizar, M., Shumailov, I., Zhao, Y., Mullins, R., & Papernot, N. (2022). Architectural backdoors in neural networks. https://arxiv.org/pdf/2206.07840Position: Fundamental Limitations of LLM Censorship Necessitate New ApproachesDavid Glukhov, Ilia Shumailov, ...https://proceedings.mlr.press/v235/glukhov24a.html AlphaEvolve MLST interview [Matej Balog, Alexander Novikov]https://www.youtube.com/watch?v=vC9nAosXrJw

We need AI systems to synthesise new knowledge, not just compress the data they see. Jeremy Berman, is a research scientist at Reflection AI and recent winner of the ARC-AGI v2 public leaderboard.**SPONSOR MESSAGES**—Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— Imagine trying to teach an AI to think like a human i.e. solving puzzles that are easy for us but stump even the smartest models. Jeremy's evolutionary approach—evolving natural language descriptions instead of python code like his last version—landed him at the top with about 30% accuracy on the ARCv2.We discuss why current AIs are like "stochastic parrots" that memorize but struggle to truly reason or innovate as well as big ideas like building "knowledge trees" for real understanding, the limits of neural networks versus symbolic systems, and whether we can train models to synthesize new ideas without forgetting everything else. Jeremy Berman:https://x.com/jerber888TRANSCRIPT:https://app.rescript.info/public/share/qvCioZeZJ4Q_NlR66m-hNUZnh-qWlUJcS15Wc2OGwD0TOC:Introduction and Overview [00:00:00]ARC v1 Solution [00:07:20]Evolutionary Python Approach [00:08:00]Trade-offs in Depth vs. Breadth [00:10:33]ARC v2 Improvements [00:11:45]Natural Language Shift [00:12:35]Model Thinking Enhancements [00:13:05]Neural Networks vs. Symbolism Debate [00:14:24]Turing Completeness Discussion [00:15:24]Continual Learning Challenges [00:19:12]Reasoning and Intelligence [00:29:33]Knowledge Trees and Synthesis [00:50:15]Creativity and Invention [00:56:41]Future Directions and Closing [01:02:30]REFS:Jeremy’s 2024 article on winning ARCAGI1-pubhttps://jeremyberman.substack.com/p/how-i-got-a-record-536-on-arc-agiGetting 50% (SoTA) on ARC-AGI with GPT-4o [Greenblatt]https://blog.redwoodresearch.org/p/getting-50-sota-on-arc-agi-with-gpt https://www.youtube.com/watch?v=z9j3wB1RRGA [his MLST interview]A Thousand Brains: A New Theory of Intelligence [Hawkins]https://www.amazon.com/Thousand-Brains-New-Theory-Intelligence/dp/1541675819https://www.youtube.com/watch?v=6VQILbDqaI4 [MLST interview]Francois Chollet + Mike Knoop’s labhttps://ndea.com/On the Measure of Intelligence [Chollet]https://arxiv.org/abs/1911.01547On the Biology of a Large Language Model [Anthropic]https://transformer-circuits.pub/2025/attribution-graphs/biology.html The ARChitects [won 2024 ARC-AGI-1-private]https://www.youtube.com/watch?v=mTX_sAq--zY Connectionism critique 1998 [Fodor/Pylshyn]https://uh.edu/~garson/F&P1.PDF Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis [Kumar/Stanley]https://arxiv.org/pdf/2505.11581 AlphaEvolve interview (also program synthesis)https://www.youtube.com/watch?v=vC9nAosXrJw ShinkaEvolve: Evolving New Algorithms with LLMs, Orders of Magnitude More Efficiently [Lange et al]https://sakana.ai/shinka-evolve/ Deep learning with Python Rev 3 [Chollet] - READ CHAPTER 19 NOW!https://deeplearningwithpython.io/

Professor Andrew Wilson from NYU explains why many common-sense ideas in artificial intelligence might be wrong. For decades, the rule of thumb in machine learning has been to fear complexity. The thinking goes: if your model has too many parameters (is "too complex") for the amount of data you have, it will "overfit" by essentially memorizing the data instead of learning the underlying patterns. This leads to poor performance on new, unseen data. This is known as the classic "bias-variance trade-off" i.e. a balancing act between a model that's too simple and one that's too complex.**SPONSOR MESSAGES**—Tufa AI Labs is an AI research lab based in Zurich. **They are hiring ML research engineers!** This is a once in a lifetime opportunity to work with one of the best labs in EuropeContact Benjamin Crouzier - https://tufalabs.ai/ —Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— Description Continued:Professor Wilson challenges this fundamental belief (fearing complexity). He makes a few surprising points:**Bigger Can Be Better**: massive models don't just get more flexible; they also develop a stronger "simplicity bias". So, if your model is overfitting, the solution might paradoxically be to make it even bigger.**The "Bias-Variance Trade-off" is a Misnomer**: Wilson claims you don't actually have to trade one for the other. You can have a model that is incredibly expressive and flexible while also being strongly biased toward simple solutions. He points to the "double descent" phenomenon, where performance first gets worse as models get more complex, but then surprisingly starts getting better again.**Honest Beliefs and Bayesian Thinking**: His core philosophy is that we should build models that honestly represent our beliefs about the world. We believe the world is complex, so our models should be expressive. But we also believe in Occam's razor—that the simplest explanation is often the best. He champions Bayesian methods, which naturally balance these two ideas through a process called marginalization, which he describes as an automatic Occam's razor.TOC:[00:00:00] Introduction and Thesis[00:04:19] Challenging Conventional Wisdom[00:11:17] The Philosophy of a Scientist-Engineer[00:16:47] Expressiveness, Overfitting, and Bias[00:28:15] Understanding, Compression, and Kolmogorov Complexity[01:05:06] The Surprising Power of Generalization[01:13:21] The Elegance of Bayesian Inference[01:33:02] The Geometry of Learning[01:46:28] Practical Advice and The Future of AIProf. Andrew Gordon Wilson:https://x.com/andrewgwilshttps://cims.nyu.edu/~andrewgw/https://scholar.google.com/citations?user=twWX2LIAAAAJ&hl=en https://www.youtube.com/watch?v=Aja0kZeWRy4 https://www.youtube.com/watch?v=HEp4TOrkwV4 TRANSCRIPT:https://app.rescript.info/public/share/H4Io1Y7Rr54MM05FuZgAv4yphoukCfkqokyzSYJwCK8Hosts:Dr. Tim Scarfe / Dr. Keith Duggar (MIT Ph.D)REFS:Deep Learning is Not So Mysterious or Different [Andrew Gordon Wilson]https://arxiv.org/abs/2503.02113Bayesian Deep Learning and a Probabilistic Perspective of Generalization [Andrew Gordon Wilson, Pavel Izmailov]https://arxiv.org/abs/2002.08791Compute-Optimal LLMs Provably Generalize Better With Scale [Marc Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson]https://arxiv.org/abs/2504.15208

In this episode, hosts Tim and Keith finally realize their long-held dream of sitting down with their hero, the brilliant neuroscientist Professor Karl Friston. The conversation is a fascinating and mind-bending journey into Professor Friston's life's work, the Free Energy Principle, and what it reveals about life, intelligence, and consciousness itself.**SPONSORS**Gemini CLI is an open-source AI agent that brings the power of Gemini directly into your terminal - https://github.com/google-gemini/gemini-cli--- Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!---cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst***They kick things off by looking back on the 20-year journey of the Free Energy Principle. Professor Friston explains it as a fundamental rule for survival: all living things, from a single cell to a human being, are constantly trying to make sense of the world and reduce unpredictability. It’s this drive to minimize surprise that allows things to exist and maintain their structure.This leads to a bigger question: What does it truly mean to be "intelligent"? The group debates whether intelligence is everywhere, even in a virus or a plant, or if it requires a certain level of complexity. Professor Friston introduces the idea of different "kinds" of things, suggesting that creatures like us, who can model themselves and think about the future, possess a unique and "strange" kind of agency that sets us apart.From intelligence, the discussion naturally flows to the even trickier concept of consciousness. Is it the same as intelligence? Professor Friston argues they are different. He explains that consciousness might emerge from deep, layered self-awareness—not just acting, but understanding that you are the one causing your actions and thinking about your place in the world.They also explore intelligence at different sizes. Is a corporation intelligent? What about the entire planet? Professor Friston suggests there might be a "Goldilocks zone" for intelligence. It doesn't seem to exist at the super-tiny atomic level or at the massive scale of planets and solar systems, but thrives in the complex middle-ground where we live.Finally, they tackle one of the most pressing topics of our time: Can we build a truly conscious AI? Professor Friston shares his doubts about whether our current computers are capable of a feat like that. He suggests that genuine consciousness might require a different kind of "mortal" computation, where the machine's physical body and its "mind" are inseparable, much like in biological creatures.TRANSCRIPT:https://app.rescript.info/public/share/FZkF8BO7HMt9aFfu2_q69WGT_ZbYZ1VVkC6RtU3eeOITOC:00:00:00: Introduction & Retrospective on the Free Energy Principle00:09:34: Strange Particles, Agency, and Consciousness00:37:45: The Scale of Intelligence: From Viruses to the Biosphere01:01:35: Modelling, Boundaries, and Practical Application01:21:12: Conclusion

We are joined by Cristopher Moore, a professor at the Santa Fe Institute with a diverse background in physics, computer science, and machine learning.The conversation begins with Cristopher, who calls himself a "frog" explaining that he prefers to dive deep into specific, concrete problems rather than taking a high-level "bird's-eye view". They explore why current AI models, like transformers, are so surprisingly effective. Cristopher argues it's because the real world isn't random; it's full of rich structures, patterns, and hierarchies that these models can learn to exploit, even if we don't fully understand how.**SPONSORS**Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!---cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy.Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst***Cristopher Moore:https://sites.santafe.edu/~moore/TOC:00:00:00 - Introduction00:02:05 - Meet Christopher Moore: A Frog in the World of Science00:05:14 - The Limits of Transformers and Real-World Data00:11:19 - Intelligence as Creative Problem-Solving00:23:30 - Grounding, Meaning, and Shared Reality00:31:09 - The Nature of Creativity and Aesthetics00:44:31 - Computational Irreducibility and Universality00:53:06 - Turing Completeness, Recursion, and Intelligence01:11:26 - The Universe Through a Computational Lens01:26:45 - Algorithmic Justice and the Need for TransparencyTRANSCRIPT: https://app.rescript.info/public/share/VRe2uQSvKZOm0oIBoDsrNwt46OMCqRnShVnUF3qyoFkFilmed at DISI (Diverse Intelligences Summer Institute)https://disi.org/REFS:The Nature of computation [Chris Moore]https://nature-of-computation.org/ Birds and Frogs [Freeman Dyson]https://www.ams.org/notices/200902/rtx090200212p.pdf Replica Theory [Parisi et al]https://arxiv.org/pdf/1409.2722 Janossy pooling [Fabian Fuchs]https://fabianfuchsml.github.io/equilibriumaggregation/ Cracking the cryptic [YT channel]https://www.youtube.com/c/CrackingTheCrypticSudoko Bench [Sakana]https://sakana.ai/sudoku-bench/Fractured entangled representations “phylogenetic locking in comment” [Kumar/Stanley]https://arxiv.org/pdf/2505.11581 (see our shows on this)The War Against Cliché: [Martin Amis]https://www.amazon.com/War-Against-Cliche-Reviews-1971-2000/dp/0375727167Rule 110 (CA)https://mathworld.wolfram.com/Rule150.htmlUniversality in Elementary Cellular Automata [Matt Cooke]https://wpmedia.wolfram.com/sites/13/2018/02/15-1-1.pdf Small Semi-Weakly Universal Turing Machines [Damien Woods] https://tilde.ini.uzh.ch/users/tneary/public_html/WoodsNeary-FI09.pdf COMPUTING MACHINERY AND INTELLIGENCE [Turing, 1950]https://courses.cs.umbc.edu/471/papers/turing.pdf Comment on Space Time as a causal set [Moore, 88]https://sites.santafe.edu/~moore/comment.pdf Recursion Theory on the Reals and Continuous-time Computation [Moore, 96]

Dr. Michael Timothy Bennett is a computer scientist who's deeply interested in understanding artificial intelligence, consciousness, and what it means to be alive. He's known for his provocative paper "What the F*** is Artificial Intelligence" which challenges conventional thinking about AI and intelligence.**SPONSOR MESSAGES***Prolific: Quality data. From real people. For faster breakthroughs.https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&utm_content=mb***Michael takes us on a journey through some of the biggest questions in AI and consciousness. He starts by exploring what intelligence actually is - settling on the idea that it's about "adaptation with limited resources" (a definition from researcher Pei Wang that he particularly likes).The discussion ranges from technical AI concepts to philosophical questions about consciousness, with Michael offering fresh perspectives that challenge Silicon Valley's "just scale it up" approach to AI. He argues that true intelligence isn't just about having more parameters or data - it's about being able to adapt efficiently, like biological systems do.TOC:1. Introduction & Paper Overview [00:01:34]2. Definitions of Intelligence [00:02:54]3. Formal Models (AIXI, Active Inference) [00:07:06]4. Causality, Abstraction & Embodiment [00:10:45]5. Computational Dualism & Mortal Computation [00:25:51]6. Modern AI, AGI Progress & Benchmarks [00:31:30]7. Hybrid AI Approaches [00:35:00]8. Consciousness & The Hard Problem [00:39:35]9. The Diverse Intelligences Summer Institute (DISI) [00:53:20]10. Living Systems & Self-Organization [00:54:17]11. Closing Thoughts [01:04:24]Michaels socials:https://michaeltimothybennett.com/https://x.com/MiTiBennettTranscript:https://app.rescript.info/public/share/4jSKbcM77Sf6Zn-Ms4hda7C4krRrMcQt0qwYqiqPTPIReferences:Bennett, M.T. "What the F*** is Artificial Intelligence"https://arxiv.org/abs/2503.23923Bennett, M.T. "Are Biological Systems More Intelligent Than Artificial Intelligence?" https://arxiv.org/abs/2405.02325Bennett, M.T. PhD Thesis "How To Build Conscious Machines"https://osf.io/preprints/thesiscommons/wehmg_v1Legg, S. & Hutter, M. (2007). "Universal Intelligence: A Definition of Machine Intelligence"Wang, P. "Defining Artificial Intelligence" - on non-axiomatic reasoning systems (NARS)Chollet, F. (2019). "On the Measure of Intelligence" - introduces the ARC benchmark and developer-aware generalizationHutter, M. (2005). "Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability"Chalmers, D. "The Hard Problem of Consciousness"Descartes, R. - Cartesian dualism and the pineal gland theory (historical context)Friston, K. - Free Energy Principle and Active Inference frameworkLevin, M. - Work on collective intelligence, cancer as information isolation, and "mind blindness"Hinton, G. (2022). "The Forward-Forward Algorithm" - introduces mortal computation conceptAlexander Ororbia & Friston - Formal treatment of mortal computationSutton, R. "The Bitter Lesson" - on search and learning in AIPearl, J. "The Book of Why" - causal inference and reasoningAlternative AGI ApproachesWang, P. - NARS (Non-Axiomatic Reasoning System)Goertzel, B. - Hyperon system and modular AGI architecturesBenchmarks & EvaluationHendrycks, D. - Humanities Last Exam benchmark (mentioned re: saturation)Filmed at:Diverse Intelligences Summer Institute (DISI) https://disi.org/

Deep dive with Dan Hendrycks, a leading AI safety researcher and co-author of the "Superintelligence Strategy" paper with former Google CEO Eric Schmidt and Scale AI CEO Alexandr Wang.*** SPONSOR MESSAGESGemini CLI is an open-source AI agent that brings the power of Gemini directly into your terminal - https://github.com/google-gemini/gemini-cliProlific: Quality data. From real people. For faster breakthroughs.https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&utm_content=script-gen***Hendrycks argues that society is making a fundamental mistake in how it views artificial intelligence. We often compare AI to transformative but ultimately manageable technologies like electricity or the internet. He contends a far better and more realistic analogy is nuclear technology. Like nuclear power, AI has the potential for immense good, but it is also a dual-use technology that carries the risk of unprecedented catastrophe.The Problem with an AI "Manhattan Project":A popular idea is for the U.S. to launch a "Manhattan Project" for AI—a secret, all-out government race to build a superintelligence before rivals like China. Hendrycks argues this strategy is deeply flawed and dangerous for several reasons:- It wouldn’t be secret. You cannot hide a massive, heat-generating data center from satellite surveillance.- It would be destabilizing. A public race would alarm rivals, causing them to start their own desperate, corner-cutting projects, dramatically increasing global risk.- It’s vulnerable to sabotage. An AI project can be crippled in many ways, from cyberattacks that poison its training data to physical attacks on its power plants. This is what the paper refers to as a "maiming attack."This vulnerability leads to the paper's central concept: Mutual Assured AI Malfunction (MAIM). This is the AI-era version of the nuclear-era's Mutual Assured Destruction (MAD). In this dynamic, any nation that makes an aggressive, destabilizing bid for a world-dominating AI must expect its rivals to sabotage the project to ensure their own survival. This deterrence, Hendrycks argues, is already the default reality we live in.A Better Strategy: The Three PillarsInstead of a reckless race, the paper proposes a more stable, three-part strategy modeled on Cold War principles:- Deterrence: Acknowledge the reality of MAIM. The goal should not be to "win" the race to superintelligence, but to deter anyone from starting such a race in the first place through the credible threat of sabotage.- Nonproliferation: Just as we work to keep fissile materials for nuclear bombs out of the hands of terrorists and rogue states, we must control the key inputs for catastrophic AI. The most critical input is advanced AI chips (GPUs). Hendrycks makes the powerful claim that building cutting-edge GPUs is now more difficult than enriching uranium, making this strategy viable.- Competitiveness: The race between nations like the U.S. and China should not be about who builds superintelligence first. Instead, it should be about who can best use existing AI to build a stronger economy, a more effective military, and more resilient supply chains (for example, by manufacturing more chips domestically).Dan says the stakes are high if we fail to manage this transition:- Erosion of Control- Intelligence Recursion- Worthless LaborHendrycks maintains that while the risks are existential, the future is not set. TOC:1 Measuring the Beast [00:00:00]2 Defining the Beast [00:11:34]3 The Core Strategy [00:38:20]4 Ideological Battlegrounds [00:53:12]5 Mechanisms of Control [01:34:45]TRANSCRIPT:https://app.rescript.info/public/share/cOKcz4pWRPjh7BTIgybd7PUr_vChUaY6VQW64No8XMs<truncated, see refs and larger description on YT version>

This episode features Shlomi Fuchter and Jack Parker Holder from Google DeepMind, who are unveiling a new AI called Genie 3. The host, Tim Scarfe, describes it as the most mind-blowing technology he has ever seen. We were invited to their offices to conduct the interview (not sponsored).Imagine you could create a video game world just by describing it. That's what Genie 3 does. It's an AI "world model" that learns how the real world works by watching massive amounts of video. Unlike a normal video game engine (like Unreal or the one for Doom) that needs to be programmed manually, Genie generates a realistic, interactive, 3D world from a simple text prompt.**SPONSOR MESSAGES***Prolific: Quality data. From real people. For faster breakthroughs.https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&utm_content=script-gen***Here’s a breakdown of what makes it so revolutionary:From Text to a Virtual World: You can type "a drone flying by a beautiful lake" or "a ski slope," and Genie 3 creates that world for you in about three seconds. You can then navigate and interact with it in real-time.It's Consistent: The worlds it creates have a reliable memory. If you look away from an object and then look back, it will still be there, just as it was. The guests explain that this consistency isn't explicitly programmed in; it's a surprising, "emergent" capability of the powerful AI model.A Huge Leap Forward: The previous version, Genie 2, was a major step, but it wasn't fast enough for real-time interaction and was much lower resolution. Genie 3 is 720p, interactive, and photorealistic, running smoothly for several minutes at a time.The Killer App - Training Robots: Beyond entertainment, the team sees Genie 3 as a game-changer for training AI. Instead of training a self-driving car or a robot in the real world (which is slow and dangerous), you can create infinite simulations. You can even prompt rare events to happen, like a deer running across the road, to teach an AI how to handle unexpected situations safely.The Future of Entertainment: this could lead to a "YouTube version 2" or a new form of VR, where users can create and explore endless, interconnected worlds together, like the experience machine from philosophy.While the technology is still a research prototype and not yet available to the public, it represents a monumental step towards creating true artificial worlds from the ground up.Jack Parker Holder [Research Scientist at Google DeepMind in the Open-Endedness Team]https://jparkerholder.github.io/Shlomi Fruchter [Research Director, Google DeepMind]https://shlomifruchter.github.io/TOC:[00:00:00] - Introduction: "The Most Mind-Blowing Technology I've Ever Seen"[00:02:30] - The Evolution from Genie 1 to Genie 2[00:04:30] - Enter Genie 3: Photorealistic, Interactive Worlds from Text[00:07:00] - Promptable World Events & Training Self-Driving Cars[00:14:21] - Guest Introductions: Shlomi Fuchter & Jack Parker Holder[00:15:08] - Core Concepts: What is a "World Model"?[00:19:30] - The Challenge of Consistency in a Generated World[00:21:15] - Context: The Neural Network Doom Simulation[00:25:25] - How Do You Measure the Quality of a World Model?[00:28:09] - The Vision: Using Genie to Train Advanced Robots[00:32:21] - Open-Endedness: Human Skill and Prompting Creativity[00:38:15] - The Future: Is This the Next YouTube or VR?[00:42:18] - The Next Step: Multi-Agent Simulations[00:52:51] - Limitations: Thinking, Computation, and the Sim-to-Real Gap[00:58:07] - Conclusion & The Future of Game EnginesREFS:World Models [David Ha, Jürgen Schmidhuber]https://arxiv.org/abs/1803.10122POEThttps://arxiv.org/abs/1901.01753[Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley]The Fractured Entangled Representation Hypothesishttps://arxiv.org/pdf/2505.11581TRANSCRIPT:https://app.rescript.info/public/share/Zk5tZXk6mb06yYOFh6nSja7Lg6_qZkgkuXQ-kl5AJqM

Prof. David Krakauer, President of the Santa Fe Institute argues that we are fundamentally confusing knowledge with intelligence, especially when it comes to AI.He defines true intelligence as the ability to do more with less—to solve novel problems with limited information. This is contrasted with current AI models, which he describes as doing less with more; they require astounding amounts of data to perform tasks that don't necessarily demonstrate true understanding or adaptation. He humorously calls this "really shit programming".David challenges the popular notion of "emergence" in Large Language Models (LLMs). He explains that the tech community's definition—seeing a sudden jump in a model's ability to perform a task like three-digit math—is superficial. True emergence, from a complex systems perspective, involves a fundamental change in the system's internal organization, allowing for a new, simpler, and more powerful level of description. He gives the example of moving from tracking individual water molecules to using the elegant laws of fluid dynamics. For LLMs to be truly emergent, we'd need to see them develop new, efficient internal representations, not just get better at memorizing patterns as they scale.Drawing on his background in evolutionary theory, David explains that systems like brains, and later, culture, evolved to process information that changes too quickly for genetic evolution to keep up. He calls culture "evolution at light speed" because it allows us to store our accumulated knowledge externally (in books, tools, etc.) and build upon it without corrupting the original.This leads to his concept of "exbodiment," where we outsource our cognitive load to the world through things like maps, abacuses, or even language itself. We create these external tools, internalize the skills they teach us, improve them, and create a feedback loop that enhances our collective intelligence.However, he ends with a warning. While technology has historically complemented our deficient abilities, modern AI presents a new danger. Because we have an evolutionary drive to conserve energy, we will inevitably outsource our thinking to AI if we can. He fears this is already leading to a "diminution and dilution" of human thought and creativity. Just as our muscles atrophy without use, he argues our brains will too, and we risk becoming mentally dependent on these systems.TOC:[00:00:00] Intelligence: Doing more with less[00:02:10] Why brains evolved: The limits of evolution[00:05:18] Culture as evolution at light speed[00:08:11] True meaning of emergence: "More is Different"[00:10:41] Why LLM capabilities are not true emergence[00:15:10] What real emergence would look like in AI[00:19:24] Symmetry breaking: Physics vs. Life[00:23:30] Two types of emergence: Knowledge In vs. Out[00:26:46] Causality, agency, and coarse-graining[00:32:24] "Exbodiment": Outsourcing thought to objects[00:35:05] Collective intelligence & the boundary of the mind[00:39:45] Mortal vs. Immortal forms of computation[00:42:13] The risk of AI: Atrophy of human thoughtDavid KrakauerPresident and William H. Miller Professor of Complex Systemshttps://www.santafe.edu/people/profile/david-krakauerREFS:Large Language Models and Emergence: A Complex Systems PerspectiveDavid C. Krakauer, John W. Krakauer, Melanie Mitchellhttps://arxiv.org/abs/2506.11135Filmed at the Diverse Intelligences Summer Institute:https://disi.org/

Dr. Maxwell Ramstead grills Guillaume Verdon (AKA “Beff Jezos”) who's the founder of Thermodynamic computing startup Extropic.Guillaume shares his unique path – from dreaming about space travel as a kid to becoming a physicist, then working on quantum computing at Google, to developing a radically new form of computing hardware for machine learning. He explains how he hit roadblocks with traditional physics and computing, leading him to start his company – building "thermodynamic computers." These are based on a new design for super-efficient chips that use the natural chaos of electrons (think noise and heat) to power AI tasks, which promises to speed up AND lower the costs of modern probabilistic techniques like sampling. He is driven by the pursuit of building computers that work more like your brain, which (by the way) runs on a banana and a glass of water! Guillaume talks about his alter ego, Beff Jezos, and the "Effective Accelerationism" (e/acc) movement that he initiated. Its objective is to speed up tech progress in order to “grow civilization” (as measured by energy use and innovation), rather than “slowing down out of fear”. Guillaume argues we need to embrace variance, exploration, and optimism to avoid getting stuck or outpaced by competitors like China. He and Maxwell discuss big ideas like merging humans with AI, decentralizing intelligence, and why boundless growth (with smart constraints) is “key to humanity's future”.REFS:1. John Archibald Wheeler - "It From Bit" Concept00:04:45 - Foundational work proposing that physical reality emerges from information at the quantum levelLearn more: https://cqi.inf.usi.ch/qic/wheeler.pdf 2. AdS/CFT Correspondence (Holographic Principle)00:05:15 - Theoretical physics duality connecting quantum gravity in Anti-de Sitter space with conformal field theoryhttps://en.wikipedia.org/wiki/Holographic_principle 3. Renormalization Group Theory00:06:15 - Mathematical framework for analyzing physical systems across different length scales https://www.damtp.cam.ac.uk/user/dbs26/AQFT/Wilsonchap.pdf 4. Maxwell's Demon and Information Theory00:21:15 - Thought experiment linking information processing to thermodynamics and entropyhttps://plato.stanford.edu/entries/information-entropy/ 5. Landauer's Principle00:29:45 - Fundamental limit establishing minimum energy required for information erasure https://en.wikipedia.org/wiki/Landauer%27s_principle 6. Free Energy Principle and Active Inference01:03:00 - Mathematical framework for understanding self-organizing systems and perception-action loopshttps://www.nature.com/articles/nrn2787 7. Max Tegmark - Information Bottleneck Principle01:07:00 - Connections between information theory and renormalization in machine learninghttps://arxiv.org/abs/1907.07331 8. Fisher's Fundamental Theorem of Natural Selection01:11:45 - Mathematical relationship between genetic variance and evolutionary fitnesshttps://en.wikipedia.org/wiki/Fisher%27s_fundamental_theorem_of_natural_selection 9. Tensor Networks in Quantum Systems00:06:45 - Computational framework for simulating many-body quantum systems https://arxiv.org/abs/1912.10049 10. Quantum Neural Networks00:09:30 - Hybrid quantum-classical models for machine learning applicationshttps://en.wikipedia.org/wiki/Quantum_neural_network 11. Energy-Based Models (EBMs)00:40:00 - Probabilistic framework for unsupervised learning based on energy functionshttps://www.researchgate.net/publication/200744586_A_tutorial_on_energy-based_learning 12. Markov Chain Monte Carlo (MCMC)00:20:00 - Sampling algorithm fundamental to modern AI and statistical physics https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo 13. Metropolis-Hastings Algorithm00:23:00 - Core sampling method for probability distributionshttps://arxiv.org/abs/1504.01896 ***SPONSOR MESSAGE***Google Gemini 2.5 Flash is a state-of-the-art language model in the Gemini app. Sign up at https://gemini.google.com