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.

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**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.

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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

Blaise Agüera y Arcas explores some mind-bending ideas about what intelligence and life really are—and why they might be more similar than we think (filmed at ALIFE conference, 2025 - https://2025.alife.org/).

Life and intelligence are both fundamentally computational (he says). From the very beginning, living things have been running programs. Your DNA? It's literally a computer program, and the ribosomes in your cells are tiny universal computers building you according to those instructions.

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Blaise argues that there is more to evolution than random mutations (like most people think). The secret to increasing complexity is *merging* i.e. when different organisms or systems come together and combine their histories and capabilities.

Blaise describes his "BFF" experiment where random computer code spontaneously evolved into self-replicating programs, showing how purpose and complexity can emerge from pure randomness through computational processes.

https://en.wikipedia.org/wiki/Blaise_Ag%C3%BCera_y_Arcas

https://x.com/blaiseaguera?lang=en

TRANSCRIPT:

https://app.rescript.info/public/share/VX7Gktfr3_wIn4Bj7cl9StPBO1MN4R5lcJ11NE99hLg

TOC:

00:00:00 Introduction - New book "What is Intelligence?"

00:01:45 Life as computation - Von Neumann's insights

00:12:00 BFF experiment - How purpose emerges

00:26:00 Symbiogenesis and evolutionary complexity

00:40:00 Functionalism and consciousness

00:49:45 AI as part of collective human intelligence

00:57:00 Comparing AI and human cognition

REFS:

What is intelligence [Blaise Agüera y Arcas]

https://whatisintelligence.antikythera.org/ [Read free online, interactive rich media]

https://mitpress.mit.edu/9780262049955/what-is-intelligence/ [MIT Press]

Large Language Models and Emergence: A Complex Systems Perspective

https://arxiv.org/abs/2506.11135

Our first Noam Chomsky MLST interview

https://www.youtube.com/watch?v=axuGfh4UR9Q

Chance and Necessity [Jacques Monod]

https://monoskop.org/images/9/99/Monod_Jacques_Chance_and_Necessity.pdf

Wonderful Life: The Burgess Shale and the History of Nature [Stephen Jay Gould]

https://www.amazon.co.uk/Wonderful-Life-Burgess-Nature-History/dp/0099273454

The major evolutionary transitions [E Szathmáry, J M Smith]

https://wiki.santafe.edu/images/0/0e/Szathmary.MaynardSmith_1995_Nature.pdf

Don't Sleep, There Are Snakes: Life and Language in the Amazonian Jungle [Dan Everett]

https://www.amazon.com/Dont-Sleep-There-Are-Snakes/dp/0307386120

The Nature of Technology: What It Is and How It Evolves [W. Brian Arthur]

https://www.amazon.com/Nature-Technology-What-How-Evolves-ebook/dp/B002RI9W16/

The MANIAC [Benjamin Labatut]

https://www.amazon.com/MANIAC-Benjam%C3%ADn-Labatut/dp/1782279814

When We Cease to Understand the World [Benjamin Labatut]

https://www.amazon.com/When-We-Cease-Understand-World/dp/1681375664/

The Boys in the Boat [Dan Brown]

https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478

[Petter Johansson] (Split brain)

https://www.lucs.lu.se/fileadmin/user_upload/lucs/2011/01/Johansson-et-al.-2006-How-Something-Can-Be-Said-About-Telling-More-Than-We-Can-Know.pdf

If Anyone Builds It, Everyone Dies [Eliezer Yudkowsky, Nate Soares]

https://www.amazon.com/Anyone-Builds-Everyone-Dies-Superhuman/dp/0316595640

The science of cycology

https://link.springer.com/content/pdf/10.3758/bf03195929.pdf

We sat down with Sara Saab (VP of Product at Prolific) and Enzo Blindow (VP of Data and AI at Prolific) to explore the critical role of human evaluation in AI development and the challenges of aligning AI systems with human values. Prolific is a human annotation and orchestration platform for AI used by many of the major AI labs. This is a sponsored show in partnership with Prolific.

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While technologists want to remove humans from the loop for speed and efficiency, these non-deterministic AI systems actually require more human oversight than ever before. Prolific's approach is to put "well-treated, verified, diversely demographic humans behind an API" - making human feedback as accessible as any other infrastructure service.

When AI models like Grok 4 achieve top scores on technical benchmarks but feel awkward or problematic to use in practice, it exposes the limitations of our current evaluation methods. The guests argue that optimizing for benchmarks may actually weaken model performance in other crucial areas, like cultural sensitivity or natural conversation.

We also discuss Anthropic's research showing that frontier AI models, when given goals and access to information, independently arrived at solutions involving blackmail - without any prompting toward unethical behavior. Even more concerning, the more sophisticated the model, the more susceptible it was to this "agentic misalignment."

Enzo and Sarah present Prolific's "Humane" leaderboard as an alternative to existing benchmarking systems. By stratifying evaluations across diverse demographic groups, they reveal that different populations have vastly different experiences with the same AI models.

Looking ahead, the guests imagine a world where humans take on coaching and teaching roles for AI systems - similar to how we might correct a child or review code. This also raises important questions about working conditions and the evolution of labor in an AI-augmented world. Rather than replacing humans entirely, we may be moving toward more sophisticated forms of human-AI collaboration.

As AI tech becomes more powerful and general-purpose, the quality of human evaluation becomes more critical, not less. We need more representative evaluation frameworks that capture the messy reality of human values and cultural diversity.

Visit Prolific:

https://www.prolific.com/

Sara Saab (VP Product):

https://uk.linkedin.com/in/sarasaab

Enzo Blindow (VP Data & AI):

https://uk.linkedin.com/in/enzoblindow

TRANSCRIPT:

https://app.rescript.info/public/share/xZ31-0kJJ_xp4zFSC-bunC8-hJNkHpbm7Lg88RFcuLE

TOC:

[00:00:00] Intro & Background

[00:03:16] Human-in-the-Loop Challenges

[00:17:19] Can AIs Understand?

[00:32:02] Benchmarking & Vibes

[00:51:00] Agentic Misalignment Study

[01:03:00] Data Quality vs Quantity

[01:16:00] Future of AI Oversight

REFS:

Anthropic Agentic Misalignment

https://www.anthropic.com/research/agentic-misalignment

Value Compass

https://arxiv.org/pdf/2409.09586

Reasoning Models Don’t Always Say What They Think (Anthropic)

https://www.anthropic.com/research/reasoning-models-dont-say-think

https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdf

Apollo research - science of evals blog post

https://www.apolloresearch.ai/blog/we-need-a-science-of-evals

Leaderboard Illusion

https://www.youtube.com/watch?v=9W_OhS38rIE MLST video

The Leaderboard Illusion [2025]

Shivalika Singh et al

https://arxiv.org/abs/2504.20879

(Truncated, full list on YT)

Dr. Ilia Shumailov - Former DeepMind AI Security Researcher, now building security tools for AI agents

Ever 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.

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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 Shumailov

https://x.com/iliaishacked

https://iliaishacked.github.io/

https://sequrity.ai/

TRANSCRIPT:

https://app.rescript.info/public/share/dVGsk8dz9_V0J7xMlwguByBq1HXRD6i4uC5z5r7EVGM

TOC:

00:00:00 - Introduction & Trusted Third Parties via ML

00:03:45 - Background & Career Journey

00:06:42 - Safety vs Security Distinction

00:09:45 - Prompt Injection & Model Capability

00:13:00 - Agents as Worst-Case Adversaries

00:15:45 - Personal AI & CAML System Defense

00:19:30 - Agents vs Humans: Threat Modeling

00:22:30 - Calculator Analogy & Agent Behavior

00:25:00 - IMO Math Solutions & Agent Thinking

00:28:15 - Diffusion of Responsibility & Insider Threats

00:31:00 - Open Source Security Concerns

00:34:45 - Supply Chain Attacks & Trust Issues

00:39:45 - Architectural Backdoors

00:44:00 - Academic Incentives & Defense Work

00:48:30 - Semantic Censorship & Halting Problem

00:52:00 - Model Collapse: Theory & Criticism

00:59:30 - Career Advice & Ross Anderson Tribute

REFS:

Lessons from Defending Gemini Against Indirect Prompt Injections

https://arxiv.org/abs/2505.14534

Defeating 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/2503.18813

Agentic Misalignment: How LLMs could be insider threats

https://www.anthropic.com/research/agentic-misalignment

STOP ANTHROPOMORPHIZING INTERMEDIATE TOKENS AS REASONING/THINKING TRACES!

Subbarao Kambhampati et al

https://arxiv.org/pdf/2504.09762

Meiklejohn, 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=EH5PZW6aCr

Apache Log4j Vulnerability Guidance

https://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/2206.07840

Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches

David 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.

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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=mlst

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—

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 AI

Prof. Andrew Gordon Wilson:

https://x.com/andrewgwils

https://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/H4Io1Y7Rr54MM05FuZgAv4yphoukCfkqokyzSYJwCK8

Hosts:

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/2503.02113

Bayesian Deep Learning and a Probabilistic Perspective of Generalization [Andrew Gordon Wilson, Pavel Izmailov]

https://arxiv.org/abs/2002.08791

Compute-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 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=mlst

Submit 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_ZbYZ1VVkC6RtU3eeOI

TOC:

00:00:00: Introduction & Retrospective on the Free Energy Principle

00:09:34: Strange Particles, Agency, and Consciousness

00:37:45: The Scale of Intelligence: From Viruses to the Biosphere

01:01:35: Modelling, Boundaries, and Practical Application

01:21:12: 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=mlst

Submit investment deck: https://cyber.fund/contact?utm_source=mlst

***

Cristopher Moore:

https://sites.santafe.edu/~moore/

TOC:

00:00:00 - Introduction

00:02:05 - Meet Christopher Moore: A Frog in the World of Science

00:05:14 - The Limits of Transformers and Real-World Data

00:11:19 - Intelligence as Creative Problem-Solving

00:23:30 - Grounding, Meaning, and Shared Reality

00:31:09 - The Nature of Creativity and Aesthetics

00:44:31 - Computational Irreducibility and Universality

00:53:06 - Turing Completeness, Recursion, and Intelligence

01:11:26 - The Universe Through a Computational Lens

01:26:45 - Algorithmic Justice and the Need for Transparency

TRANSCRIPT: https://app.rescript.info/public/share/VRe2uQSvKZOm0oIBoDsrNwt46OMCqRnShVnUF3qyoFk

Filmed 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/CrackingTheCryptic

Sudoko 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/0375727167

Rule 110 (CA)

https://mathworld.wolfram.com/Rule150.html

Universality 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]