Avsnitt
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Brad Carson was the Army's General Counsel, served two terms in Congress and was Acting Under Secretary of Defense for Personnel and Readiness. He now heads Americans for Responsible Innovation, the AI-policy advocacy group he co-founded. Keith Duggar spends roughly eighty minutes pushing back.
SPONSOR:
---
Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.
Apply now: https://cyber.fund
---
Carson's whole case rests on one line: the genie is not out of the bottle. We have pulled dangerous tech back before. Asilomar halted recombinant DNA in 1975, and the West still controls the chips AI runs on. Calling it unstoppable, he says, is the most dangerous idea in the room.
Then Keith drags him somewhere darker. A Palantir heat map scores you 0.73 on whether you are a combatant, and a strike follows. The model is wrong some accepted share of the time, and when it is, nobody answers for it. You cannot court-martial a model, and not even the interpretability researchers can say why it picked you.
—
Note: after recording, we learned that Americans for Responsible Innovation is backed by EA-aligned philanthropy (not sponsored)
---
TIMESTAMPS:
00:00:00 From the Pentagon to AI governance
00:04:52 Regulatory capture vs Silicon Valley networks
00:07:56 Transparency and the Claude tier changes
00:09:40 Tort liability when AI tools cause harm
00:13:40 AI is a product, not a person
00:16:01 Children, suicide, and the suicide business
00:19:59 Opaque neural nets and the law of war
00:25:54 Probabilistic targeting and the death of accountability
00:28:47 The arms race fallacy: Asilomar and restraint
00:34:02 Talking to China: track 2 talks and chip leverage
00:39:45 Air power never wins: capital for labour
00:43:29 Anthropic vs the Department of War
00:51:29 Concentration, open source, and brain drain
01:00:18 DeepSeek, Chinese culture, and AI as diplomacy
01:12:25 Upskilling Congress and why public trust matters
---
REFERENCES:
organization:
[00:02:45] ICRC position on autonomous weapons
https://www.icrc.org/en/law-and-policy/autonomous-weapons
[00:05:22] Americans for Responsible Innovation (ARI)
https://ari.us
[00:07:20] Andreessen Horowitz (a16z)
https://a16z.com/
[01:16:05] Office of Technology Assessment
https://en.wikipedia.org/wiki/Office_of_Technology_Assessment
other:
[00:03:35] Beneficial AGI 2019 Conference (Future of Life Institute, Puerto Rico)
https://futureoflife.org/event/beneficial-agi-2019/
[00:18:30] Section 230 of the Communications Decency Act
https://en.wikipedia.org/wiki/Section_230
[00:19:59] Lethal Autonomous Weapons (LAWS)
https://en.wikipedia.org/wiki/Lethal_autonomous_weapon
[00:31:35] Strategic Arms Limitation Talks (SALT)
https://en.wikipedia.org/wiki/Strategic_Arms_Limitation_Talks
[00:32:28] Asilomar Conference on Recombinant DNA (1975)
https://en.wikipedia.org/wiki/Asilomar_Conference_on_Recombinant_DNA
[00:39:45] The New Iron Triangle (ARI policy byte)
https://ari.us/policy-bytes/the-new-iron-triangle/
[00:48:05] Defense Production Act
https://en.wikipedia.org/wiki/Defense_Production_Act
person:
[00:03:35] Anthony Aguirre
https://en.wikipedia.org/wiki/Anthony_Aguirre
[00:06:48] Dean Ball — Hyperdimensional
https://www.hyperdimensional.co/
[00:23:13] Neel Nanda — mechanistic interpretability
https://www.neelnanda.io/
[00:36:02] Jack Clark (Anthropic) on Conversations with Tyler
https://conversationswithtyler.com/episodes/jack-clark/
[00:39:15] Robert Trager — Centre for the Governance of AI
https://www.governance.ai/team/robert-trager
[00:41:55] Giulio Douhet
https://en.wikipedia.org/wiki/Giulio_Douhet
[01:15:05] Don Beyer (US Congress)
https://en.wikipedia.org/wiki/Don_Beyer
tool:
[00:22:19] Phalanx CIWS
https://en.wikipedia.org/wiki/Phalanx_CIWS
---
ReScript:
https://app.rescript.info/public/share/9405ff35c0215b7cdae6402d41284171
https://app.rescript.info/api/public/sessions/0a6c081b8e5fe413/pdf
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Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters.
SPONSOR:
---
Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.
Apply now: https://cyber.fund
---
Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence.
We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem.
ERRATA: Science magazine ranked him the most influential computer scientist, not Nature
---
TIMESTAMPS:
00:00:00 Cold open: A demoralizing message to young builders
00:02:04 CyberFund sponsor read
00:02:50 From symbolic AI to machine learning systems
00:05:42 Why AGI is mostly a PR term
00:08:48 A collectivist, economic perspective on AI
00:11:33 Why LLMs need system design, not hype
00:14:50 Predictability beats faux understanding
00:17:55 AlphaFold, bias, and prediction-powered inference
00:21:48 Stop anthropomorphizing intelligence
00:27:44 Drug discovery as an incentive problem
00:32:29 The three-layer data market
00:38:07 Social knowledge, markets, and culture
00:45:39 Creator economics beyond Spotify
00:48:30 How science-fiction AI narratives mislead young builders
00:51:45 AI should improve humans, not replace them
00:56:42 Safety is a property of the whole system
00:58:12 Silicon Valley gurus and the cream off the top
01:00:47 Game theory, mechanism design, and contracts
01:04:39 Conformal prediction, e-values, and anytime inference
01:08:11 A new liberal arts triangle for the AI era
01:11:30 The Bayesian duck and markets as uncertainty reduction
ReScript (transcript, PDF, refs etc) - https://app.rescript.info/public/share/fb68f94af29d3745c6cf6125e01328b5
---
REFERENCES:
person:
[00:02:50] Michael I. Jordan (homepage)
https://people.eecs.berkeley.edu/~jordan/
paper:
[00:06:01] A Collectivist, Economic Perspective on AI
https://arxiv.org/abs/2507.06268
[00:18:09] AlphaFold
https://www.nature.com/articles/s41586-021-03819-2
[00:20:36] Prediction-Powered Inference
https://arxiv.org/abs/2301.09633
[00:33:47] On Three-Layer Data Markets
https://arxiv.org/abs/2402.09697
[01:04:39] Conformal Prediction with Conditional Guarantees
https://arxiv.org/abs/2107.07511
[01:04:51] A Tutorial on Conformal Prediction
https://www.jmlr.org/papers/v9/shafer08a.html
[01:06:00] E-Values Expand the Scope of Conformal Prediction
https://arxiv.org/abs/2503.13050
[01:08:23] Computational Thinking
https://www.cs.cmu.edu/~CompThink/papers/Wing06.pdf
other:
[00:28:20] How Should the FDA Test?
https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15
[00:28:40] Michael I. Jordan Session V Slides
<truncated, see ReScript link or YT VD>
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Saknas det avsnitt?
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Beth Barnes and David Rein on the one graph that ate the AI timelines discourse, and why the two people who built it are the most careful about how you read it.**SPONSOR**Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlstInterview: https://youtu.be/cnxZZTl1tkk---Beth Barnes and David Rein from METR on the one graph that ate the AI timelines discourse, and why the people who built it are the most careful about how it gets read.Beth founded METR after leaving OpenAI alignment. David is first author on GPQA and co-author on HCAST and the METR Time Horizons paper. Together they built the measurement Daniel Kokotajlo called the single most important piece of evidence on AI timelines: the log-linear line of "how long a task a frontier model can complete at 50% reliability" vs release date.The conversation opens on reward hacking. Current models can articulate in chat why a behaviour is undesired and then execute it anyway as agents. From there: construct validity, Melanie Mitchell's four-problem taxonomy, and the ARC-AGI 1-to-2 collapse as a worked example of adversarially-selected benchmarks regressing once labs target them. Beth's counter: METR deliberately does not adversarially select. David's: models do not have to do the right thing for the right reasons.Methodology, then specification — David's compiler analogy, Beth on four-month tasks as expensive to evaluate rather than unspecifiable. Then the SWE-bench reality check, the METR finding that half of passing PRs would not be merged, and Beth's horses-versus-bank-tellers analogy for the labour market.The close: monitorability, the coin-spinning boat, two-year recursive self-improvement, and Beth's line that "overhyped now" and "big deal later" are not correlated claims.---TIMESTAMPS:00:00:00 Intro00:02:06 Sponsor break: Prolific human-feedback infrastructure00:02:33 Welcome and the scalable oversight motivation00:06:02 Construct validity, benchmark pathologies and the Chollet worry00:15:45 Time Horizons: human time, HCAST tasks and the 50% logistic00:24:50 Is human difficulty really one variable?00:33:05 Agent harness evolution and the inference-compute dividend00:40:00 Scaffolding bells, token budgets and the credit-assignment problem00:44:15 Look at the damn graph: regularisation bug and reliability nuance00:50:00 Why 50%? Reliability, reward hacking and pizza-party transcripts00:55:20 Extrapolation risk and straight lines on graphs00:59:25 Software engineering as a specification acquisition problem01:07:40 Compilers also made ugly code: vibe-coding quality and Claude on METR Slack01:15:15 Strongest defensible claim, Carlini's compiler swarm and AI 202701:23:45 SWE-bench merge rates, the bank-teller analogy and horses01:31:45 Scheming, alignment faking and the mentalistic vocabulary problem01:40:45 Reward hacking, monitorability and chain-of-thought faithfulness01:45:25 Recursive self-improvement, knowledge vs intelligence and closing
ReScript: https://app.rescript.info/public/share/de3bb40cc02ee39fdf36e2c60366eb4d
(PDF, refs, transcript etc)
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Robert Lange, founding researcher at Sakana AI, joins Tim to discuss *Shinka Evolve* — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves.
GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg)
• Why AlphaEvolve gets stuck — it needs a human to hand it the right problem. Shinka tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search.
• The *architecture* of Shinka: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard.
• Concrete results — state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks.
• Are these systems actually thinking outside the box, or are they parasitic on their starting conditions? When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully.
• The AI Scientist question: can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher.
• Where this lands in 5-20 years — Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived.
Robert Lange: https://roberttlange.com/
---
TIMESTAMPS:
00:00:00 Introduction: Robert Lange, Sakana AI and Shinka Evolve
00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions
00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science
00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture
00:28:00 UCB Bandits, Mutations and the Vibe Research Vision
00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model
00:47:10 Applications, ARC-AGI and the Future of Work
00:57:00 The AI Scientist and the Human Co-Pilot: Who Steers the Search?
01:06:00 AI Scientist v2, Slop Critique and the Future of Scientific Publishing
---
REFERENCES:
paper:
[00:03:30] ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution
https://arxiv.org/abs/2509.19349
[00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery
https://arxiv.org/abs/2506.13131
[00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
https://arxiv.org/abs/2505.22954
[00:09:05] Paired Open-Ended Trailblazer (POET)
https://arxiv.org/abs/1901.01753
[00:10:00] PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
https://arxiv.org/abs/1112.5309
[00:10:40] Automated Capability Discovery via Foundation Model Self-Exploration
https://arxiv.org/abs/2502.07577
[00:15:30] Illuminating Search Spaces by Mapping Elites (MAP-Elites)
https://arxiv.org/abs/1504.04909
[00:47:10] Automated Design of Agentic Systems (ADAS)
https://arxiv.org/abs/2408.08435
<trunc, see ReScript/YT>
PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/download
Transcript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk
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Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models.
GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg)
Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers today's language models.
Key Topics and Main Insights Discussed:
- The Origins of ULMFiT and Fine-Tuning
- The Vibe Coding Illusion and Software Engineering
- Cognitive Science, Friction, and Learning
- The Future of Developers
RESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izk
Jeremy Howard:
https://x.com/jeremyphoward
https://www.answer.ai/
---
TIMESTAMPS (fixed):
00:00:00 Introduction & GTC Sponsor
00:04:30 ULMFiT & The Birth of Fine-Tuning
00:12:00 Intuition & The Mechanics of Learning
00:18:30 Abstraction Hierarchies & AI Creativity
00:23:00 Claude Code & The Interpolation Illusion
00:27:30 Coding vs. Software Engineering
00:30:00 Cosplaying Intelligence: Dennett vs. Searle
00:36:30 Automation, Radiology & Desirable Difficulty
00:42:30 Organizational Knowledge & The Slope
00:48:00 Vibe Coding as a Slot Machine
00:54:00 The Erosion of Control in Software
01:01:00 Interactive Programming & REPL Environments
01:05:00 The Notebook Debate & Exploratory Science
01:17:30 AI Existential Risk & Power Centralization
01:24:20 Current Risks, Privacy & Enfeeblement
---
REFERENCES:
Blog Post:
[00:03:00] fast.ai Blog: Self-Supervised Learning
https://www.fast.ai/posts/2020-01-13-self_supervised.html
[00:13:30] DeepMind Blog: Gemini Deep Think
https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/
[00:19:30] Modular Blog: Claude C Compiler analysis
https://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software
[00:19:45] Anthropic Engineering Blog: Building C Compiler
https://www.anthropic.com/engineering/building-c-compiler
[00:48:00] Cursor Blog: Scaling Agents
https://cursor.com/blog/scaling-agents
[01:05:15] fast.ai Blog: NB Dev Merged Driver
https://www.fast.ai/posts/2022-08-25-jupyter-git.html
[01:17:30] Jeremy Howard: Response to AI Risk Letter
https://www.normaltech.ai/p/is-avoiding-extinction-from-ai-really
Book:
[00:08:30] M. Chirimuuta: The Brain Abstracted
https://mitpress.mit.edu/9780262548045/the-brain-abstracted/
[00:30:00] Daniel Dennett: Consciousness Explained
https://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661
[00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledge
https://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676
Archive Article:
[00:13:45] MLST Archive: Why Creativity Cannot Be Interpolated
https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated
Research Study:
[00:24:30] METR Study: AI OS Development
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
Paper:
[00:24:45] Fred Brooks: No Silver Bullet
https://www.cs.unc.edu/techreports/86-020.pdf
[00:30:15] John Searle: Minds, Brains, and Programs
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A
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What if life itself is just a really sophisticated computer program that wrote itself into existence?
Blaise Agüera y Arcas presenting at ALife 2025 — the most technically detailed public walkthrough of the ideas in his *What is Life?* and *What is Intelligence?* books that we've come across.He covers the BFF experiments (self-replicating programs emerging spontaneously from random noise), the mathematical framework connecting Lotka-Volterra population dynamics with Smoluchowski coagulation, eigenvalue analysis of cooperation matrices, and his central claim that symbiogenesis — not mutation — is the primary engine of evolutionary novelty.The experimental results are genuinely striking: complex self-replicating code arising from random byte strings with zero mutation, a sharp phase transition that looks like gelation, and a proof that blocking deep symbiogenetic ancestry trees prevents the transition entirely.A few things worth flagging for critical viewers:— The substrate is more carefully engineered than the framing sometimes suggests. The choice of language, tape length, interaction protocol, and step limits all shape what emerges. Their own SUBLEQ counterexample (where self-replicators *don't* arise despite being theoretically possible) highlights that these design choices matter substantially — and a general theory of which substrates support this transition is still missing.— The leap from "self-replicating programs on fixed-length tapes" to "life was computational and intelligent from the start" involves significant philosophical extrapolation beyond what the experiments directly demonstrate.— The Bedau et al. (2000) open problems paper he references at the start actually sets a higher bar for Challenge 3.2 than BFF currently meets: it asks that "the internal organization of these 'organisms' and the boundaries separating them from their environment arise and be sustained through the activities of lower-level primitives" — whereas BFF's tape boundaries are fixed by design, not emergent.
---
TIMESTAMPS:
00:00:00 Introduction: From Noise to Programs & ALife History
00:03:15 Defining Life: Function as the "Spirit"
00:05:45 Von Neumann's Insight: Life is Embodied Computation
00:09:15 Physics of Computation: Irreversibility & Fallacies
00:15:00 The BFF Experiment: Spontaneous Generation of Code
00:23:45 The Mystery: Complexity Growth Without Mutation
00:27:00 Symbiogenesis: The Engine of Novelty
00:33:15 Mathematical Proof: Blocking Symbiosis Stops Life
00:40:15 Evolutionary Implications: It's Symbiogenesis All The Way Down
00:44:30 Intelligence as Modeling Others
00:46:49 Q&A: Levels of Abstraction & Definitions
---
REFERENCES:
Paper:
[00:01:16] Open Problems in Artificial Life
https://direct.mit.edu/artl/article/6/4/363/2354/Open-Problems-in-Artificial-Life
[00:09:30] When does a physical system compute?
https://arxiv.org/abs/1309.7979
[00:15:00] Computational Life
https://arxiv.org/abs/2406.19108
[00:27:30] On the Origin of Mitosing Cells
https://pubmed.ncbi.nlm.nih.gov/11541392/
[00:42:00] The Major Evolutionary Transitions
https://www.nature.com/articles/374227a0
[00:44:00] The ARC gene
https://www.nih.gov/news-events/news-releases/memory-gene-goes-viral
Person:
[00:05:45] Alan Turing
https://plato.stanford.edu/entries/turing/
[00:07:30] John von Neumann
https://en.wikipedia.org/wiki/John_von_Neumann
[00:11:15] Hector Zenil
https://hectorzenil.net/
[00:12:00] Robert Sapolsky
https://profiles.stanford.edu/robert-sapolsky
---
LINKS:
RESCRIPT: https://app.rescript.info/public/share/ff7gb6HpezOR3DF-gr9-rCoMFzzEgUjLQK6voV5XVWY
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What makes something truly *intelligent?* Is a rock an agent? Could a perfect simulation of your brain actually *be* you? In this fascinating conversation, Dr. Jeff Beck takes us on a journey through the philosophical and technical foundations of agency, intelligence, and the future of AI.
Jeff doesn't hold back on the big questions. He argues that from a purely mathematical perspective, there's no structural difference between an agent and a rock – both execute policies that map inputs to outputs. The real distinction lies in *sophistication* – how complex are the internal computations? Does the system engage in planning and counterfactual reasoning, or is it just a lookup table that happens to give the right answers?
*Key topics explored in this conversation:*
*The Black Box Problem of Agency* – How can we tell if something is truly planning versus just executing a pre-computed response? Jeff explains why this question is nearly impossible to answer from the outside, and why the best we can do is ask which model gives us the simplest explanation.
*Energy-Based Models Explained* – A masterclass on how EBMs differ from standard neural networks. The key insight: traditional networks only optimize weights, while energy-based models optimize *both* weights and internal states – a subtle but profound distinction that connects to Bayesian inference.
*Why Your Brain Might Have Evolved from Your Nose* – One of the most surprising moments in the conversation. Jeff proposes that the complex, non-smooth nature of olfactory space may have driven the evolution of our associative cortex and planning abilities.
*The JEPA Revolution* – A deep dive into Yann LeCun's Joint Embedding Prediction Architecture and why learning in latent space (rather than predicting every pixel) might be the key to more robust AI representations.
*AI Safety Without Skynet Fears* – Jeff takes a refreshingly grounded stance on AI risk. He's less worried about rogue superintelligences and more concerned about humans becoming "reward function selectors" – couch potatoes who just approve or reject AI outputs. His proposed solution? Use inverse reinforcement learning to derive AI goals from observed human behavior, then make *small* perturbations rather than naive commands like "end world hunger."
Whether you're interested in the philosophy of mind, the technical details of modern machine learning, or just want to understand what makes intelligence *tick,* this conversation delivers insights you won't find anywhere else.
---
TIMESTAMPS:
00:00:00 Geometric Deep Learning & Physical Symmetries
00:00:56 Defining Agency: From Rocks to Planning
00:05:25 The Black Box Problem & Counterfactuals
00:08:45 Simulated Agency vs. Physical Reality
00:12:55 Energy-Based Models & Test-Time Training
00:17:30 Bayesian Inference & Free Energy
00:20:07 JEPA, Latent Space, & Non-Contrastive Learning
00:27:07 Evolution of Intelligence & Modular Brains
00:34:00 Scientific Discovery & Automated Experimentation
00:38:04 AI Safety, Enfeeblement & The Future of Work
---
REFERENCES:
Concept:
[00:00:58] Free Energy Principle (FEP)
https://en.wikipedia.org/wiki/Free_energy_principle
[00:06:00] Monte Carlo Tree Search
https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
Book:
[00:09:00] The Intentional Stance
https://mitpress.mit.edu/9780262540537/the-intentional-stance/
Paper:
[00:13:00] A Tutorial on Energy-Based Learning (LeCun 2006)
http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf
[00:15:00] Auto-Encoding Variational Bayes (VAE)
https://arxiv.org/abs/1312.6114
[00:20:15] JEPA (Joint Embedding Prediction Architecture)
https://openreview.net/forum?id=BZ5a1r-kVsf
[00:22:30] The Wake-Sleep Algorithm
https://www.cs.toronto.edu/~hinton/absps/ws.pdf
<trunc, see rescript>
---
RESCRIPT:
https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_Eo
PDF:
https://app.rescript.info/api/public/sessions/0efec296b9b6e905/pdf
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Professor Mazviita Chirimuuta joins us for a fascinating deep dive into the philosophy of neuroscience and what it really means to understand the mind.*What can neuroscience actually tell us about how the mind works?* In this thought-provoking conversation, we explore the hidden assumptions behind computational theories of the brain, the limits of scientific abstraction, and why the question of machine consciousness might be more complicated than AI researchers assume.Mazviita, author of *The Brain Abstracted,* brings a unique perspective shaped by her background in both neuroscience research and philosophy. She challenges us to think critically about the metaphors we use to understand cognition — from the reflex theory of the late 19th century to today's dominant view of the brain as a computer.*Key topics explored:**The problem of oversimplification* — Why scientific models necessarily leave things out, and how this can sometimes lead entire fields astray. The cautionary tale of reflex theory shows how elegant explanations can blind us to biological complexity.*Is the brain really a computer?* — Mazviita unpacks the philosophical assumptions behind computational neuroscience and asks: if we can model anything computationally, what makes brains special? The answer might challenge everything you thought you knew about AI.*Haptic realism* — A fresh way of thinking about scientific knowledge that emphasizes interaction over passive observation. Knowledge isn't about reading the "source code of the universe" — it's something we actively construct through engagement with the world.*Why embodiment matters for understanding* — Can a disembodied language model truly understand? Mazviita makes a compelling case that human cognition is deeply entangled with our sensory-motor engagement and biological existence in ways that can't simply be abstracted away.*Technology and human finitude* — Drawing on Heidegger, we discuss how the dream of transcending our physical limitations through technology might reflect a fundamental misunderstanding of what it means to be a knower.This conversation is essential viewing for anyone interested in AI, consciousness, philosophy of mind, or the future of cognitive science. Whether you're skeptical of strong AI claims or a true believer in machine consciousness, Mazviita's careful philosophical analysis will give you new tools for thinking through these profound questions.---TIMESTAMPS:00:00:00 The Problem of Generalizing Neuroscience00:02:51 Abstraction vs. Idealization: The "Kaleidoscope"00:05:39 Platonism in AI: Discovering or Inventing Patterns?00:09:42 When Simplification Fails: The Reflex Theory00:12:23 Behaviorism and the "Black Box" Trap00:14:20 Haptic Realism: Knowledge Through Interaction00:20:23 Is Nature Protean? The Myth of Converging Truth00:23:23 The Computational Theory of Mind: A Useful Fiction?00:27:25 Biological Constraints: Why Brains Aren't Just Neural Nets00:31:01 Agency, Distal Causes, and Dennett's Stances00:37:13 Searle's Challenge: Causal Powers and Understanding00:41:58 Heidegger's Warning & The Experiment on Children---REFERENCES:Book:[00:01:28] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:11:05] The Integrated Action of the Nervous Systemhttps://www.amazon.sg/integrative-action-nervous-system/dp/9354179029[00:18:15] The Quest for Certainty (Dewey)https://www.amazon.com/Quest-Certainty-Relation-Knowledge-Lectures/dp/0399501916[00:19:45] Realism for Realistic People (Chang)https://www.cambridge.org/core/books/realism-for-realistic-people/ACC93A7F03B15AA4D6F3A466E3FC5AB7<truncated, see ReScript>---RESCRIPT:https://app.rescript.info/public/share/A6cZ1TY35p8ORMmYCWNBI0no9ChU3-Kx7dPXGJURvZ0PDF Transcript:https://app.rescript.info/api/public/sessions/0fb7767e066cf712/pdf
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What if everything we think we know about the brain is just a really good metaphor that we forgot was a metaphor?This episode takes you on a journey through the history of scientific simplification, from a young Karl Friston watching wood lice in his garden to the bold claims that your mind is literally software running on biological hardware.We bring together some of the most brilliant minds we've interviewed — Professor Mazviita Chirimuuta, Francois Chollet, Joscha Bach, Professor Luciano Floridi, Professor Noam Chomsky, Nobel laureate John Jumper, and more — to wrestle with a deceptively simple question: *When scientists simplify reality to study it, what gets captured and what gets lost?**Key ideas explored:**The Spherical Cow Problem* — Science requires simplification. We're limited creatures trying to understand systems far more complex than our working memory can hold. But when does a useful model become a dangerous illusion?*The Kaleidoscope Hypothesis* — Francois Chollet's beautiful idea that beneath all the apparent chaos of reality lies simple, repeating patterns — like bits of colored glass in a kaleidoscope creating infinite complexity. Is this profound truth or Platonic wishful thinking?*Is Software Really Spirit?* — Joscha Bach makes the provocative claim that software is literally spirit, not metaphorically. We push back on this, asking whether the "sameness" we see across different computers running the same program exists in nature or only in our descriptions.*The Cultural Illusion of AGI* — Why does artificial general intelligence seem so inevitable to people in Silicon Valley? Professor Chirimuuta suggests we might be caught in a "cultural historical illusion" — our mechanistic assumptions about minds making AI seem like destiny when it might just be a bet.*Prediction vs. Understanding* — Nobel Prize winner John Jumper: AI can predict and control, but understanding requires a human in the loop. Throughout history, we've described the brain as hydraulic pumps, telegraph networks, telephone switchboards, and now computers. Each metaphor felt obviously true at the time. This episode asks: what will we think was naive about our current assumptions in fifty years?Featuring insights from *The Brain Abstracted* by Mazviita Chirimuuta — possibly the most influential book on how we think about thinking in 2025.---TIMESTAMPS:00:00:00 The Wood Louse & The Spherical Cow00:02:04 The Necessity of Abstraction00:04:42 Simplicius vs. Ignorantio: The Boxing Match00:06:39 The Kaleidoscope Hypothesis00:08:40 Is the Mind Software?00:13:15 Critique of Causal Patterns00:14:40 Temperature is Not a Thing00:18:24 The Ship of Theseus & Ontology00:23:45 Metaphors Hardening into Reality00:25:41 The Illusion of AGI Inevitability00:27:45 Prediction vs. Understanding00:32:00 Climbing the Mountain vs. The Helicopter00:34:53 Haptic Realism & The Limits of Knowledge---REFERENCES:Person:[00:00:00] Karl Friston (UCL)https://profiles.ucl.ac.uk/1236-karl-friston[00:06:30] Francois Chollethttps://fchollet.com/[00:14:41] Cesar Hidalgo, MLST interview.https://www.youtube.com/watch?v=vzpFOJRteeI[00:30:30] Terence Tao's Bloghttps://terrytao.wordpress.com/Book:[00:02:25] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:06:00] On Learned Ignorancehttps://www.amazon.com/Nicholas-Cusa-learned-ignorance-translation/dp/0938060236[00:24:15] Science and the Modern Worldhttps://amazon.com/dp/0684836394<truncated, see ReScript>
RESCRIPT:https://app.rescript.info/public/share/CYy0ex2M2kvcVRdMnSUky5O7H7hB7v2u_nVhoUiuKD4PDF Transcript: https://app.rescript.info/api/public/sessions/6c44c41e1e0fa6dd/pdf
Thank you to Dr. Maxwell Ramstead for early script work on this show (Ph.D student of Friston) and the woodlice story came from him!
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Dr. Jeff Beck, mathematician turned computational neuroscientist, joins us for a fascinating deep dive into why the future of AI might look less like ChatGPT and more like your own brain.
**SPONSOR MESSAGES START**
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Prolific - Quality data. From real people. For faster breakthroughs.
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*What if the key to building truly intelligent machines isn't bigger models, but smarter ones?*
In this conversation, Jeff makes a compelling case that we've been building AI backwards. While the tech industry races to scale up transformers and language models, Jeff argues we're missing something fundamental: the brain doesn't work like a giant prediction engine. It works like a scientist, constantly testing hypotheses about a world made of *objects* that interact through *forces* — not pixels and tokens.
*The Bayesian Brain* — Jeff explains how your brain is essentially running the scientific method on autopilot. When you combine what you see with what you hear, you're doing optimal Bayesian inference without even knowing it. This isn't just philosophy — it's backed by decades of behavioral experiments showing humans are surprisingly efficient at handling uncertainty.
*AutoGrad Changed Everything* — Forget transformers for a moment. Jeff argues the real hero of the AI boom was automatic differentiation, which turned AI from a math problem into an engineering problem. But in the process, we lost sight of what actually makes intelligence work.
*The Cat in the Warehouse Problem* — Here's where it gets practical. Imagine a warehouse robot that's never seen a cat. Current AI would either crash or make something up. Jeff's approach? Build models that *know what they don't know*, can phone a friend to download new object models on the fly, and keep learning continuously. It's like giving robots the ability to say "wait, what IS that?" instead of confidently being wrong.
*Why Language is a Terrible Model for Thought* — In a provocative twist, Jeff argues that grounding AI in language (like we do with LLMs) is fundamentally misguided. Self-report is the least reliable data in psychology — people routinely explain their own behavior incorrectly. We should be grounding AI in physics, not words.
*The Future is Lots of Little Models* — Instead of one massive neural network, Jeff envisions AI systems built like video game engines: thousands of small, modular object models that can be combined, swapped, and updated independently. It's more efficient, more flexible, and much closer to how we actually think.
Rescript: https://app.rescript.info/public/share/D-b494t8DIV-KRGYONJghvg-aelMmxSDjKthjGdYqsE
---
TIMESTAMPS:
00:00:00 Introduction & The Bayesian Brain
00:01:25 Bayesian Inference & Information Processing
00:05:17 The Brain Metaphor: From Levers to Computers
00:10:13 Micro vs. Macro Causation & Instrumentalism
00:16:59 The Active Inference Community & AutoGrad
00:22:54 Object-Centered Models & The Grounding Problem
00:35:50 Scaling Bayesian Inference & Architecture Design
00:48:05 The Cat in the Warehouse: Solving Generalization
00:58:17 Alignment via Belief Exchange
01:05:24 Deception, Emergence & Cellular Automata
---
REFERENCES:
Paper:
[00:00:24] Zoubin Ghahramani (Google DeepMind)
https://pmc.ncbi.nlm.nih.gov/articles/PMC3538441/pdf/rsta201
[00:19:20] Mamba: Linear-Time Sequence Modeling
https://arxiv.org/abs/2312.00752
[00:27:36] xLSTM: Extended Long Short-Term Memory
https://arxiv.org/abs/2405.04517
[00:41:12] 3D Gaussian Splatting
https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/
[01:07:09] Lenia: Biology of Artificial Life
https://arxiv.org/abs/1812.05433
[01:08:20] Growing Neural Cellular Automata
https://distill.pub/2020/growing-ca/
[01:14:05] DreamCoder
https://arxiv.org/abs/2006.08381
[01:14:58] The Genomic Bottleneck
https://www.nature.com/articles/s41467-019-11786-6
Person:
[00:16:42] Karl Friston (UCL)
https://www.youtube.com/watch?v=PNYWi996Beg
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Tim sits down with Max Bennett to explore how our brains evolved over 600 million years—and what that means for understanding both human intelligence and AI.
Max isn't a neuroscientist by training. He's a tech entrepreneur who got curious, started reading, and ended up weaving together three fields that rarely talk to each other: comparative psychology (what different animals can actually do), evolutionary neuroscience (how brains changed over time), and AI (what actually works in practice).
*Your Brain Is a Guessing Machine*
You don't actually "see" the world. Your brain builds a simulation of what it *thinks* is out there and just uses your eyes to check if it's right. That's why optical illusions work—your brain is filling in a triangle that isn't there, or can't decide if it's looking at a duck or a rabbit.
*Rats Have Regrets*
*Chimps Are Machiavellian*
*Language Is the Human Superpower*
*Does ChatGPT Think?*
(truncated description, more on rescript)
Understanding how the brain evolved isn't just about the past. It gives us clues about:
- What's actually different between human intelligence and AI
- Why we're so easily fooled by status games and tribal thinking
- What features we might want to build into—or leave out of—future AI systems
Get Max's book:
https://www.amazon.com/Brief-History-Intelligence-Humans-Breakthroughs/dp/0063286343
Rescript: https://app.rescript.info/public/share/R234b7AXyDXZusqQ_43KMGsUSvJ2TpSz2I3emnI6j9A
---
TIMESTAMPS:
00:00:00 Introduction: Outsider's Advantage & Neocortex Theories
00:11:34 Perception as Inference: The Filling-In Machine
00:19:11 Understanding, Recognition & Generative Models
00:36:39 How Mice Plan: Vicarious Trial & Error
00:46:15 Evolution of Self: The Layer 4 Mystery
00:58:31 Ancient Minds & The Social Brain: Machiavellian Apes
01:19:36 AI Alignment, Instrumental Convergence & Status Games
01:33:07 Metacognition & The IQ Paradox
01:48:40 Does GPT Have Theory of Mind?
02:00:40 Memes, Language Singularity & Brain Size Myths
02:16:44 Communication, Language & The Cyborg Future
02:44:25 Shared Fictions, World Models & The Reality Gap
---
REFERENCES:Person:
[00:00:05] Karl Friston (UCL)
https://www.youtube.com/watch?v=PNYWi996Beg
[00:00:06] Jeff Hawkins
https://www.youtube.com/watch?v=6VQILbDqaI4
[00:12:19] Hermann von Helmholtz
https://plato.stanford.edu/entries/hermann-helmholtz/
[00:38:34] David Redish (U. Minnesota)
https://redishlab.umn.edu/
[01:10:19] Robin Dunbar
https://www.psy.ox.ac.uk/people/robin-dunbar
[01:15:04] Emil Menzel
https://www.sciencedirect.com/bookseries/behavior-of-nonhuman-primates/vol/5/suppl/C
[01:19:49] Nick Bostrom
https://nickbostrom.com/
[02:28:25] Noam Chomsky
https://linguistics.mit.edu/user/chomsky/
[03:01:22] Judea Pearl
https://samueli.ucla.edu/people/judea-pearl/
Concept/Framework:
[00:05:04] Active Inference
https://www.youtube.com/watch?v=KkR24ieh5Ow
Paper:
[00:35:59] Predictions not commands [Rick A Adams]
https://pubmed.ncbi.nlm.nih.gov/23129312/
Book:
[01:25:42] The Elephant in the Brain
https://www.amazon.com/Elephant-Brain-Hidden-Motives-Everyday/dp/0190495995
[01:28:27] The Status Game
https://www.goodreads.com/book/show/58642436-the-status-game
[02:00:40] The Selfish Gene
https://amazon.com/dp/0198788606
[02:14:25] The Language Game
https://www.amazon.com/Language-Game-Improvisation-Created-Changed/dp/1541674987
[02:54:40] The Evolution of Language
https://www.amazon.com/Evolution-Language-Approaches/dp/052167736X
[03:09:37] The Three-Body Problem
https://amazon.com/dp/0765377063
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César Hidalgo has spent years trying to answer a deceptively simple question: What is knowledge, and why is it so hard to move around?
We all have this intuition that knowledge is just... information. Write it down in a book, upload it to GitHub, train an AI on it—done. But César argues that's completely wrong. Knowledge isn't a thing you can copy and paste. It's more like a living organism that needs the right environment, the right people, and constant exercise to survive.
Guest: César Hidalgo, Director of the Center for Collective Learning
1. Knowledge Follows Laws (Like Physics)
2. You Can't Download Expertise
3. Why Big Companies Fail to Adapt
4. The "Infinite Alphabet" of Economies
If you think AI can just "copy" human knowledge, or that development is just about throwing money at poor countries, or that writing things down preserves them forever—this conversation will change your mind. Knowledge is fragile, specific, and collective. It decays fast if you don't use it.
The Infinite Alphabet [César A. Hidalgo]
https://www.penguin.co.uk/books/458054/the-infinite-alphabet-by-hidalgo-cesar-a/9780241655672
https://x.com/cesifoti
Rescript link.
https://app.rescript.info/public/share/eaBHbEo9xamwbwpxzcVVm4NQjMh7lsOQKeWwNxmw0JQ
---
TIMESTAMPS:
00:00:00 The Three Laws of Knowledge
00:02:28 Rival vs. Non-Rival: The Economics of Ideas
00:05:43 Why You Can't Just 'Download' Knowledge
00:08:11 The Detective Novel Analogy
00:11:54 Collective Learning & Organizational Networks
00:16:27 Architectural Innovation: Amazon vs. Barnes & Noble
00:19:15 The First Law: Learning Curves
00:23:05 The Samuel Slater Story: Treason & Memory
00:28:31 Physics of Knowledge: Joule's Cannon
00:32:33 Extensive vs. Intensive Properties
00:35:45 Knowledge Decay: Ise Temple & Polaroid
00:41:20 Absorptive Capacity: Sony & Donetsk
00:47:08 Disruptive Innovation & S-Curves
00:51:23 Team Size & The Cost of Innovation
00:57:13 Geography of Knowledge: Vespa's Origin
01:04:34 Migration, Diversity & 'Planet China'
01:12:02 Institutions vs. Knowledge: The China Story
01:21:27 Economic Complexity & The Infinite Alphabet
01:32:27 Do LLMs Have Knowledge?
---
REFERENCES:
Book:
[00:47:45] The Innovator's Dilemma (Christensen)
https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244
[00:55:15] Why Greatness Cannot Be Planned
https://amazon.com/dp/3319155237
[01:35:00] Why Information Grows
https://amazon.com/dp/0465048994
Paper:
[00:03:15] Endogenous Technological Change (Romer, 1990)
https://web.stanford.edu/~klenow/Romer_1990.pdf
[00:03:30] A Model of Growth Through Creative Destruction (Aghion & Howitt, 1992)
https://dash.harvard.edu/server/api/core/bitstreams/7312037d-2b2d-6bd4-e053-0100007fdf3b/content
[00:14:55] Organizational Learning: From Experience to Knowledge (Argote & Miron-Spektor, 2011)
https://www.researchgate.net/publication/228754233_Organizational_Learning_From_Experience_to_Knowledge
[00:17:05] Architectural Innovation (Henderson & Clark, 1990)
https://www.researchgate.net/publication/200465578_Architectural_Innovation_The_Reconfiguration_of_Existing_Product_Technologies_and_the_Failure_of_Established_Firms
[00:19:45] The Learning Curve Equation (Thurstone, 1916)
https://dn790007.ca.archive.org/0/items/learningcurveequ00thurrich/learningcurveequ00thurrich.pdf
[00:21:30] Factors Affecting the Cost of Airplanes (Wright, 1936)
https://pdodds.w3.uvm.edu/research/papers/others/1936/wright1936a.pdf
[00:52:45] Are Ideas Getting Harder to Find? (Bloom et al.)
https://web.stanford.edu/~chadj/IdeaPF.pdf
[01:33:00] LLMs/ Emergence
https://arxiv.org/abs/2506.11135
Person:
[00:25:30] Samuel Slater
https://en.wikipedia.org/wiki/Samuel_Slater
[00:42:05] Masaru Ibuka (Sony)
https://www.sony.com/en/SonyInfo/CorporateInfo/History/SonyHistory/1-02.html
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This is a lively, no-holds-barred debate about whether AI can truly be intelligent, conscious, or understand anything at all — and what happens when (or if) machines become smarter than us.
Dr. Mike Israetel is a sports scientist, entrepreneur, and co-founder of RP Strength (a fitness company). He describes himself as a "dilettante" in AI but brings a fascinating outsider's perspective.
Jared Feather (IFBB Pro bodybuilder and exercise physiologist)
The Big Questions:
1. When is superintelligence coming?
2. Does AI actually understand anything?
3. The Simulation Debate (The Spiciest Part)
4. Will AI kill us all? (The Doomer Debate)
5. What happens to human jobs and purpose?
6. Do we need suffering?
Mikes channel: https://www.youtube.com/channel/UCfQgsKhHjSyRLOp9mnffqVg
RESCRIPT INTERACTIVE PLAYER: https://app.rescript.info/public/share/GVMUXHCqctPkXH8WcYtufFG7FQcdJew_RL_MLgMKU1U
---
TIMESTAMPS:
00:00:00 Introduction & Workout Demo
00:04:15 ASI Timelines & Definitions
00:10:24 The Embodiment Debate
00:18:28 Neutrinos & Abstract Knowledge
00:25:56 Can AI Learn From YouTube?
00:31:25 Diversity of Intelligence
00:36:00 AI Slop & Understanding
00:45:18 The Simulation Argument: Fire & Water
00:58:36 Consciousness & Zombies
01:04:30 Do Reasoning Models Actually Reason?
01:12:00 The Live Learning Problem
01:19:15 Superintelligence & Benevolence
01:28:59 What is True Agency?
01:37:20 Game Theory & The "Kill All Humans" Fallacy
01:48:05 Regulation & The China Factor
01:55:52 Mind Uploading & The Future of Love
02:04:41 Economics of ASI: Will We Be Useless?
02:13:35 The Matrix & The Value of Suffering
02:17:30 Transhumanism & Inequality
02:21:28 Debrief: AI Medical Advice & Final Thoughts
---
REFERENCES:
Paper:
[00:10:45] Alchemy and Artificial Intelligence (Dreyfus)
https://www.rand.org/content/dam/rand/pubs/papers/2006/P3244.pdf
[00:10:55] The Chinese Room Argument (John Searle)
https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf
[00:11:05] The Symbol Grounding Problem (Stephen Harnad)
https://arxiv.org/html/cs/9906002
[00:23:00] Attention Is All You Need
https://arxiv.org/abs/1706.03762
[00:45:00] GPT-4 Technical Report
https://arxiv.org/abs/2303.08774
[01:45:00] Anthropic Agentic Misalignment Paper
https://www.anthropic.com/research/agentic-misalignment
[02:17:45] Retatrutide
https://pubmed.ncbi.nlm.nih.gov/37366315/
Organization:
[00:15:50] CERN
https://home.cern/
[01:05:00] METR Long Horizon Evaluations
https://evaluations.metr.org/
MLST Episode:
[00:23:10] MLST: Llion Jones - Inventors' Remorse
https://www.youtube.com/watch?v=DtePicx_kFY
[00:50:30] MLST: Blaise Agüera y Arcas Interview
https://www.youtube.com/watch?v=rMSEqJ_4EBk
[01:10:00] MLST: David Krakauer
https://www.youtube.com/watch?v=dY46YsGWMIc
Event:
[00:23:40] ARC Prize/Challenge
https://arcprize.org/
Book:
[00:24:45] The Brain Abstracted
https://www.amazon.com/Brain-Abstracted-Simplification-Philosophy-Neuroscience/dp/0262548046
[00:47:55] Pamela McCorduck
https://www.amazon.com/Machines-Who-Think-Artificial-Intelligence/dp/1568812051
[01:23:15] The Singularity Is Nearer (Ray Kurzweil)
https://www.amazon.com/Singularity-Nearer-Ray-Kurzweil-ebook/dp/B08Y6FYJVY
[01:27:35] A Fire Upon The Deep (Vernor Vinge)
https://www.amazon.com/Fire-Upon-Deep-S-F-MASTERWORKS-ebook/dp/B00AVUMIZE/
[02:04:50] Deep Utopia (Nick Bostrom)
https://www.amazon.com/Deep-Utopia-Meaning-Solved-World/dp/1646871642
[02:05:00] Technofeudalism (Yanis Varoufakis)
https://www.amazon.com/Technofeudalism-Killed-Capitalism-Yanis-Varoufakis/dp/1685891241
Visual Context Needed:
[00:29:40] AT-AT Walker (Star Wars)
https://starwars.fandom.com/wiki/All_Terrain_Armored_Transport
Person:
[00:33:15] Andrej Karpathy
https://karpathy.ai/
Video:
[01:40:00] Mike Israetel vs Liron Shapira AI Doom Debate
https://www.youtube.com/watch?v=RaDWSPMdM4o
Company:
[02:26:30] Examine.com
https://examine.com/
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We often think of Large Language Models (LLMs) as all-knowing, but as the team reveals, they still struggle with the logic of a second-grader. Why can’t ChatGPT reliably add large numbers? Why does it "hallucinate" the laws of physics? The answer lies in the architecture. This episode explores how *Category Theory* —an ultra-abstract branch of mathematics—could provide the "Periodic Table" for neural networks, turning the "alchemy" of modern AI into a rigorous science.
In this deep-dive exploration, *Andrew Dudzik*, *Petar Velichkovich*, *Taco Cohen*, *Bruno Gavranović*, and *Paul Lessard* join host *Tim Scarfe* to discuss the fundamental limitations of today’s AI and the radical mathematical framework that might fix them.
TRANSCRIPT:
https://app.rescript.info/public/share/LMreunA-BUpgP-2AkuEvxA7BAFuA-VJNAp2Ut4MkMWk
---
Key Insights in This Episode:
* *The "Addition" Problem:* *Andrew Dudzik* explains why LLMs don't actually "know" math—they just recognize patterns. When you change a single digit in a long string of numbers, the pattern breaks because the model lacks the internal "machinery" to perform a simple carry operation.
* *Beyond Alchemy:* deep learning is currently in its "alchemy" phase—we have powerful results, but we lack a unifying theory. Category Theory is proposed as the framework to move AI from trial-and-error to principled engineering. [00:13:49]
* *Algebra with Colors:* To make Category Theory accessible, the guests use brilliant analogies—like thinking of matrices as *magnets with colors* that only snap together when the types match. This "partial compositionality" is the secret to building more complex internal reasoning. [00:09:17]
* *Synthetic vs. Analytic Math:* *Paul Lessard* breaks down the philosophical shift needed in AI research: moving from "Analytic" math (what things are made of) to "Synthetic" math [00:23:41]
---
Why This Matters for AGI
If we want AI to solve the world's hardest scientific problems, it can't just be a "stochastic parrot." It needs to internalize the rules of logic and computation. By imbuing neural networks with categorical priors, researchers are attempting to build a future where AI doesn't just predict the next word—it understands the underlying structure of the universe.
---
TIMESTAMPS:
00:00:00 The Failure of LLM Addition & Physics
00:01:26 Tool Use vs Intrinsic Model Quality
00:03:07 Efficiency Gains via Internalization
00:04:28 Geometric Deep Learning & Equivariance
00:07:05 Limitations of Group Theory
00:09:17 Category Theory: Algebra with Colors
00:11:25 The Systematic Guide of Lego-like Math
00:13:49 The Alchemy Analogy & Unifying Theory
00:15:33 Information Destruction & Reasoning
00:18:00 Pathfinding & Monoids in Computation
00:20:15 System 2 Reasoning & Error Awareness
00:23:31 Analytic vs Synthetic Mathematics
00:25:52 Morphisms & Weight Tying Basics
00:26:48 2-Categories & Weight Sharing Theory
00:28:55 Higher Categories & Emergence
00:31:41 Compositionality & Recursive Folds
00:34:05 Syntax vs Semantics in Network Design
00:36:14 Homomorphisms & Multi-Sorted Syntax
00:39:30 The Carrying Problem & Hopf Fibrations
Petar Veličković (GDM)
https://petar-v.com/
Paul Lessard
https://www.linkedin.com/in/paul-roy-lessard/
Bruno Gavranović
https://www.brunogavranovic.com/
Andrew Dudzik (GDM)
https://www.linkedin.com/in/andrew-dudzik-222789142/
---
REFERENCES:
Model:
[00:01:05] Veo
https://deepmind.google/models/veo/
[00:01:10] Genie
https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/
Paper:
[00:04:30] Geometric Deep Learning Blueprint
https://arxiv.org/abs/2104.13478
https://www.youtube.com/watch?v=bIZB1hIJ4u8
[00:16:45] AlphaGeometry
https://arxiv.org/abs/2401.08312
[00:16:55] AlphaCode
https://arxiv.org/abs/2203.07814
[00:17:05] FunSearch
https://www.nature.com/articles/s41586-023-06924-6
[00:37:00] Attention Is All You Need
https://arxiv.org/abs/1706.03762
[00:43:00] Categorical Deep Learning
https://arxiv.org/abs/2402.15332
-
Is a car that wins a Formula 1 race the best choice for your morning commute? Probably not. In this sponsored deep dive with Prolific, we explore why the same logic applies to Artificial Intelligence. While models are currently shattering records on technical exams, they often fail the most important test of all: **the human experience.**
Why High Benchmark Scores Don’t Mean Better AI
Joining us are **Andrew Gordon** (Staff Researcher in Behavioral Science) and **Nora Petrova** (AI Researcher) from **Prolific**. They reveal the hidden flaws in how we currently rank AI and introduce a more rigorous, "humane" way to measure whether these models are actually helpful, safe, and relatable for real people.
---
Key Insights in This Episode:
* *The F1 Car Analogy:* Andrew explains why a model that excels at the "Humanities Last Exam" might be a nightmare for daily use. Technical benchmarks often ignore the nuances of human communication and adaptability.
* *The "Wild West" of AI Safety:* As users turn to AI for sensitive topics like mental health, Nora highlights the alarming lack of oversight and the "thin veneer" of safety training—citing recent controversial incidents like Grok-3’s "Mecha Hitler."
* *Fixing the "Leaderboard Illusion":* The team critiques current popular rankings like Chatbot Arena, discussing how anonymous, unstratified voting can lead to biased results and how companies can "game" the system.
* *The Xbox Secret to AI Ranking:* Discover how Prolific uses *TrueSkill*—the same algorithm Microsoft developed for Xbox Live matchmaking—to create a fairer, more statistically sound leaderboard for LLMs.
* *The Personality Gap:* Early data from the **Humane Leaderboard** suggests that while AI is getting smarter, it is actually performing *worse* on metrics like personality, culture, and "sycophancy" (the tendency for models to become annoying "people-pleasers").
---
About the HUMAINE Leaderboard
Moving beyond simple "A vs. B" testing, the researchers discuss their new framework that samples participants based on *census data* (Age, Ethnicity, Political Alignment). By using a representative sample of the general public rather than just tech enthusiasts, they are building a standard that reflects the values of the real world.
*Are we building models for benchmarks, or are we building them for humans? It’s time to change the scoreboard.*
Rescript link:
https://app.rescript.info/public/share/IDqwjY9Q43S22qSgL5EkWGFymJwZ3SVxvrfpgHZLXQc
---
TIMESTAMPS:
00:00:00 Introduction & The Benchmarking Problem
00:01:58 The Fractured State of AI Evaluation
00:03:54 AI Safety & Interpretability
00:05:45 Bias in Chatbot Arena
00:06:45 Prolific's Three Pillars Approach
00:09:01 TrueSkill Ranking & Efficient Sampling
00:12:04 Census-Based Representative Sampling
00:13:00 Key Findings: Culture, Personality & Sycophancy
---
REFERENCES:
Paper:
[00:00:15] MMLU
https://arxiv.org/abs/2009.03300
[00:05:10] Constitutional AI
https://arxiv.org/abs/2212.08073
[00:06:45] The Leaderboard Illusion
https://arxiv.org/abs/2504.20879
[00:09:41] HUMAINE Framework Paper
https://huggingface.co/blog/ProlificAI/humaine-framework
Company:
[00:00:30] Prolific
https://www.prolific.com
[00:01:45] Chatbot Arena
https://lmarena.ai/
Person:
[00:00:35] Andrew Gordon
https://www.linkedin.com/in/andrew-gordon-03879919a/
[00:00:45] Nora Petrova
https://www.linkedin.com/in/nora-petrova/
Event:
Algorithm:
[00:09:01] Microsoft TrueSkill
https://www.microsoft.com/en-us/research/project/trueskill-ranking-system/
Leaderboard:
[00:09:21] Prolific HUMAINE Leaderboard
https://www.prolific.com/humaine
[00:09:31] HUMAINE HuggingFace Space
https://huggingface.co/spaces/ProlificAI/humaine-leaderboard
[00:10:21] Prolific AI Leaderboard Portal
https://www.prolific.com/leaderboard
Dataset:
[00:09:51] Prolific Social Reasoning RLHF Dataset
https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf
Organization:
[00:10:31] MLCommons
https://mlcommons.org/
-
What if everything we think we know about AI understanding is wrong? Is compression the key to intelligence? Or is there something more—a leap from memorization to true abstraction?
In this fascinating conversation, we sit down with **Professor Yi Ma**—world-renowned expert in deep learning, IEEE/ACM Fellow, and author of the groundbreaking new book *Learning Deep Representations of Data Distributions*. Professor Ma challenges our assumptions about what large language models actually do, reveals why 3D reconstruction isn't the same as understanding, and presents a unified mathematical theory of intelligence built on just two principles: **parsimony** and **self-consistency**.
**SPONSOR MESSAGES START**
—
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**
Key Insights:
**LLMs Don't Understand—They Memorize**
Language models process text (*already* compressed human knowledge) using the same mechanism we use to learn from raw data.
**The Illusion of 3D Vision**
Sora and NeRFs etc that can reconstruct 3D scenes still fail miserably at basic spatial reasoning
**"All Roads Lead to Rome"**
Why adding noise is *necessary* for discovering structure.
**Why Gradient Descent Actually Works**
Natural optimization landscapes are surprisingly smooth—a "blessing of dimensionality"
**Transformers from First Principles**
Transformer architectures can be mathematically derived from compression principles
—
INTERACTIVE AI TRANSCRIPT PLAYER w/REFS (ReScript):
https://app.rescript.info/public/share/Z-dMPiUhXaeMEcdeU6Bz84GOVsvdcfxU_8Ptu6CTKMQ
About Professor Yi Ma
Yi Ma is the inaugural director of the School of Computing and Data Science at Hong Kong University and a visiting professor at UC Berkeley.
https://people.eecs.berkeley.edu/~yima/
https://scholar.google.com/citations?user=XqLiBQMAAAAJ&hl=en
https://x.com/YiMaTweets
**Slides from this conversation:**
https://www.dropbox.com/scl/fi/sbhbyievw7idup8j06mlr/slides.pdf?rlkey=7ptovemezo8bj8tkhfi393fh9&dl=0
**Related Talks by Professor Ma:**
- Pursuing the Nature of Intelligence (ICLR): https://www.youtube.com/watch?v=LT-F0xSNSjo
- Earlier talk at Berkeley: https://www.youtube.com/watch?v=TihaCUjyRLM
TIMESTAMPS:
00:00:00 Introduction
00:02:08 The First Principles Book & Research Vision
00:05:21 Two Pillars: Parsimony & Consistency
00:09:50 Evolution vs. Learning: The Compression Mechanism
00:14:36 LLMs: Memorization Masquerading as Understanding
00:19:55 The Leap to Abstraction: Empirical vs. Scientific
00:27:30 Platonism, Deduction & The ARC Challenge
00:35:57 Specialization & The Cybernetic Legacy
00:41:23 Deriving Maximum Rate Reduction
00:48:21 The Illusion of 3D Understanding: Sora & NeRF
00:54:26 All Roads Lead to Rome: The Role of Noise
00:59:56 All Roads Lead to Rome: The Role of Noise
01:00:14 Benign Non-Convexity: Why Optimization Works
01:06:35 Double Descent & The Myth of Overfitting
01:14:26 Self-Consistency: Closed-Loop Learning
01:21:03 Deriving Transformers from First Principles
01:30:11 Verification & The Kevin Murphy Question
01:34:11 CRATE vs. ViT: White-Box AI & Conclusion
REFERENCES:
Book:
[00:03:04] Learning Deep Representations of Data Distributions
https://ma-lab-berkeley.github.io/deep-representation-learning-book/
[00:18:38] A Brief History of Intelligence
https://www.amazon.co.uk/BRIEF-HISTORY-INTELLIGEN-HB-Evolution/dp/0008560099
[00:38:14] Cybernetics
https://mitpress.mit.edu/9780262730099/cybernetics/
Book (Yi Ma):
[00:03:14] 3-D Vision book
https://link.springer.com/book/10.1007/978-0-387-21779-6
<TRUNC> refs on ReScript link/YT
-
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
-
The Transformer architecture (which powers ChatGPT and nearly all modern AI) might be trapping the industry in a localized rut, preventing us from finding true intelligent reasoning, according to the person who co-invented it. Llion Jones and Luke Darlow, key figures at the research lab Sakana AI, join the show to make this provocative argument, and also introduce new research which might lead the way forwards.
**SPONSOR MESSAGES START**
—
Build your ideas with AI Studio from Google - http://ai.studio/build
—
Tufa AI Labs is hiring ML Research Engineers https://tufalabs.ai/
—
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**
The "Spiral" Problem – Llion uses a striking visual analogy to explain what current AI is missing. If you ask a standard neural network to understand a spiral shape, it solves it by drawing tiny straight lines that just happen to look like a spiral. It "fakes" the shape without understanding the concept of spiraling.
Introducing the Continuous Thought Machine (CTM) Luke Darlow deep dives into their solution: a biology-inspired model that fundamentally changes how AI processes information.
The Maze Analogy: Luke explains that standard AI tries to solve a maze by staring at the whole image and guessing the entire path instantly. Their new machine "walks" through the maze step-by-step.
Thinking Time: This allows the AI to "ponder." If a problem is hard, the model can naturally spend more time thinking about it before answering, effectively allowing it to correct its own mistakes and backtrack—something current Language Models struggle to do genuinely.
https://sakana.ai/
https://x.com/YesThisIsLion
https://x.com/LearningLukeD
TRANSCRIPT:
https://app.rescript.info/public/share/crjzQ-Jo2FQsJc97xsBdfzfOIeMONpg0TFBuCgV2Fu8
TOC:
00:00:00 - Stepping Back from Transformers
00:00:43 - Introduction to Continuous Thought Machines (CTM)
00:01:09 - The Changing Atmosphere of AI Research
00:04:13 - Sakana’s Philosophy: Research Freedom
00:07:45 - The Local Minimum of Large Language Models
00:18:30 - Representation Problems: The Spiral Example
00:29:12 - Technical Deep Dive: CTM Architecture
00:36:00 - Adaptive Computation & Maze Solving
00:47:15 - Model Calibration & Uncertainty
01:00:43 - Sudoku Bench: Measuring True Reasoning
REFS:
Why Greatness Cannot be planned [Kenneth Stanley]
https://www.amazon.co.uk/Why-Greatness-Cannot-Planned-Objective/dp/3319155237
https://www.youtube.com/watch?v=lhYGXYeMq_E
The Hardware Lottery [Sara Hooker]
https://arxiv.org/abs/2009.06489
https://www.youtube.com/watch?v=sQFxbQ7ade0
Continuous Thought Machines [Luke Darlow et al / Sakana]
https://arxiv.org/abs/2505.05522
https://sakana.ai/ctm/
LSTM: The Comeback Story? [Prof. Sepp Hochreiter]
https://www.youtube.com/watch?v=8u2pW2zZLCs
Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis [Kumar/Stanley]
https://arxiv.org/pdf/2505.11581
A Spline Theory of Deep Networks [Randall Balestriero]
https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf
https://www.youtube.com/watch?v=86ib0sfdFtw
https://www.youtube.com/watch?v=l3O2J3LMxqI
On the Biology of a Large Language Model [Anthropic, Jack Lindsey et al]
https://transformer-circuits.pub/2025/attribution-graphs/biology.html
The ARC Prize 2024 Winning Algorithm [Daniel Franzen and Jan Disselhoff] “The ARChitects”
https://www.youtube.com/watch?v=mTX_sAq--zY
Neural Turing Machine [Graves]
https://arxiv.org/pdf/1410.5401
Adaptive Computation Time for Recurrent Neural Networks [Graves]
https://arxiv.org/abs/1603.08983
Sudoko Bench [Sakana]
https://pub.sakana.ai/sudoku/
-
Ever wonder where AI models actually get their "intelligence"? We reveal the dirty secret of Silicon Valley: behind every impressive AI system are thousands of real humans providing crucial data, feedback, and expertise.Guest: Phelim Bradley, CEO and Co-founder of ProlificPhelim Bradley runs Prolific, a platform that connects AI companies with verified human experts who help train and evaluate their models. Think of it as a sophisticated marketplace matching the right human expertise to the right AI task - whether that's doctors evaluating medical chatbots or coders reviewing AI-generated software.Prolific: https://prolific.com/?utm_source=mlsthttps://uk.linkedin.com/in/phelim-bradley-84300826The discussion dives into:**The human data pipeline**: How AI companies rely on human intelligence to train, refine, and validate their models - something rarely discussed openly**Quality over quantity**: Why paying humans well and treating them as partners (not commodities) produces better AI training data**The matching challenge**: How Prolific solves the complex problem of finding the right expert for each specific task, similar to matching Uber drivers to riders but with deep expertise requirements**Future of work**: What it means when human expertise becomes an on-demand service, and why this might actually create more opportunities rather than fewer**Geopolitical implications**: Why the centralization of AI development in US tech companies should concern Europe and the UK
-
"What is life?" - asks Chris Kempes, a professor at the Santa Fe Institute.
Chris explains that scientists are moving beyond a purely Earth-based, biological view and are searching for a universal theory of life that could apply to anything, anywhere in the universe. He proposes that things we don't normally consider "alive"—like human culture, language, or even artificial intelligence; could be seen as life forms existing on different "substrates".
To understand this, Chris presents a fascinating three-level framework:
- Materials: The physical stuff life is made of. He argues this could be incredibly diverse across the universe, and we shouldn't expect alien life to share our biochemistry.
- Constraints: The universal laws of physics (like gravity or diffusion) that all life must obey, regardless of what it's made of. This is where different life forms start to look more similar.
- Principles: At the highest level are abstract principles like evolution and learning. Chris suggests these computational or "optimization" rules are what truly define a living system.
A key idea is "convergence" – using the example of the eye. It's such a complex organ that you'd think it evolved only once. However, eyes evolved many separate times across different species. This is because the physics of light provides a clear "target", and evolution found similar solutions to the problem of seeing, even with different starting materials.
**SPONSOR MESSAGES**
—
Prolific - Quality data. From real people. For faster breakthroughs.
https://www.prolific.com/?utm_source=mlst
—
Check out NotebookLM from Google here - https://notebooklm.google.com/ - it’s really good for doing research directly from authoritative source material, minimising hallucinations.
—
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
—
Prof. Chris Kempes:
https://www.santafe.edu/people/profile/chris-kempes
TRANSCRIPT:
https://app.rescript.info/public/share/Y2cI1i0nX_-iuZitvlguHvaVLQTwPX1Y_E1EHxV0i9I
TOC:
00:00:00 - Introduction to Chris Kempes and the Santa Fe Institute
00:02:28 - The Three Cultures of Science
00:05:08 - What Makes a Good Scientific Theory?
00:06:50 - The Universal Theory of Life
00:09:40 - The Role of Material in Life
00:12:50 - A Hierarchy for Understanding Life
00:13:55 - How Life Diversifies and Converges
00:17:53 - Adaptive Processes and Defining Life
00:19:28 - Functionalism, Memes, and Phylogenies
00:22:58 - Convergence at Multiple Levels
00:25:45 - The Possibility of Simulating Life
00:28:16 - Intelligence, Parasitism, and Spectrums of Life
00:32:39 - Phase Changes in Evolution
00:36:16 - The Separation of Matter and Logic
00:37:21 - Assembly Theory and Quantifying Complexity
REFS:
Developing a predictive science of the biosphere requires the integration of scientific cultures [Kempes et al]
https://www.pnas.org/doi/10.1073/pnas.2209196121
Seeing with an extra sense (“Dangerous prediction”) [Rob Phillips]
https://www.sciencedirect.com/science/article/pii/S0960982224009035
The Multiple Paths to Multiple Life [Christopher P. Kempes & David C. Krakauer]
https://link.springer.com/article/10.1007/s00239-021-10016-2
The Information Theory of Individuality [David Krakauer et al]
https://arxiv.org/abs/1412.2447
Minds, Brains and Programs [Searle]
https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf
The error threshold
https://www.sciencedirect.com/science/article/abs/pii/S0168170204003843
Assembly theory and its relationship with computational complexity [Kempes et al]
https://arxiv.org/abs/2406.12176
- Visa fler
