Uploaded September 2024 | Updated September 2026, 1 week ago
Dr. Tim Scarfe and Dr. Keith Duggar discuss OpenAI's new models and their capabilities. They critically analyse claims about AI reasoning, explore the limitations of current language models, and debate the nature of intelligence and computation. Throughout, they emphasize the importance of human oversight in AI applications and discuss potential future developments in the field.
MLST is sponsored by Brave:
The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at brave.com/api.
We edited about an hour off this conversation, see full one on patreon - patreon.com/posts/tim-and-keith-on-112091132
TOC:
00:00:00 1. Introduction and AI hype cycles
00:02:09 2. Computational limits of AI systems
00:03:57 3. Neural Networks vs. Turing Machines
00:11:55 4. Computational models in AI
00:13:03 5. What is Reasoning?
00:21:08 6. Chain-of-thought prompting
00:26:02 7. AI code generation and complexity
00:34:24 8. AI assistance vs. human problem-solving
00:35:04 9. Limitations of AI in reasoning and problem-solving
00:46:27 10. Knowledge acquisition and inference in AI
00:53:36 11. Comparing AI and human reasoning capabilities
00:58:58 12. LLMs as cognitive tools
01:00:32 13. Testing o1-preview on a logic puzzle
01:20:48 14. AI-assisted coding: strengths and limitations
Dr. Tim Scarfe and Dr. Keith Duggar discuss OpenAI's new models and their capabilities. They critically analyse claims about AI reasoning, explore the limitations of current language models, and debate the nature of intelligence and computation. Throughout, they emphasize the importance of human oversight in AI applications and discuss potential future developments in the field.
MLST is sponsored by Brave:
The Brave Search API covers over 20 billion webpages, built from scratch without Big Tech biases or the recent extortionate price hikes on search API access. Perfect for AI model training and retrieval augmentated generation. Try it now - get 2,000 free queries monthly at brave.com/api.
We edited about an hour off this conversation, see full one on patreon - patreon.com/posts/tim-and-keith-on-112091132
TOC:
00:00:00 1. Introduction and AI hype cycles
00:02:09 2. Computational limits of AI systems
00:03:57 3. Neural Networks vs. Turing Machines
00:11:55 4. Computational models in AI
00:13:03 5. What is Reasoning?
00:21:08 6. Chain-of-thought prompting
00:26:02 7. AI code generation and complexity
00:34:24 8. AI assistance vs. human problem-solving
00:35:04 9. Limitations of AI in reasoning and problem-solving
00:46:27 10. Knowledge acquisition and inference in AI
00:53:36 11. Comparing AI and human reasoning capabilities
00:58:58 12. LLMs as cognitive tools
01:00:32 13. Testing o1-preview on a logic puzzle
01:20:48 14. AI-assisted coding: strengths and limitations
![#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]](https://i.ytimg.com/vi/p2D2duT-R2E/mqdefault.jpg "#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]
#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]")
![Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]](https://i.ytimg.com/vi/pO0WZsN8Oiw/mqdefault.jpg "Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]
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 weve 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. Were 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 Chollets 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 hard 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, weve 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 Cow
00:02:04 The Necessity of Abstraction
00:04:42 Simplicius vs. Ignorantio: The Boxing Match
00:06:39 The Kaleidoscope Hypothesis
00:08:40 Is the Mind Software?
00:13:15 Critique of Causal Patterns
00:14:40 Temperature is Not a Thing
00:18:24 The Ship of Theseus & Ontology
00:23:45 Metaphors Hardening into Reality
00:25:41 The Illusion of AGI Inevitability
00:27:45 Prediction vs. Understanding
00:32:00 Climbing the Mountain vs. The Helicopter
00:34:53 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 Chollet
https://fchollet.com/
[00:14:41] Cesar Hidalgo, MLST interview.
https://www.youtube.com/watch?v=vzpFOJRteeI
[00:30:30] Terence Taos Blog
https://terrytao.wordpress.com/
Book:
[00:02:25] The Brain Abstracted
https://mitpress.mit.edu/9780262548045/the-brain-abstracted/
[00:06:00] On Learned Ignorance
https://www.amazon.com/Nicholas-Cusa-learned-ignorance-translation/dp/0938060236
[00:24:15] Science and the Modern World
https://amazon.com/dp/0684836394
Interview.:
[00:02:43] The Brain Abstracted Patreon interview
https://www.patreon.com/posts/brain-abstracted-124479979
Interview:
[00:04:18] David Krakauers presentation on intelligence.
https://www.youtube.com/watch?v=dY46YsGWMIc
[00:06:45] Machine Learning Street Talk interview with Francois Chollet.
https://www.youtube.com/watch?v=JTU8Ha4Jyfc
[00:09:32] Joscha Bach Patreon interview.
https://www.patreon.com/posts/joscha-bach-deep-141884561
[00:18:24] Luciano Floridi, MLST interview.
https://www.youtube.com/watch?v=YLNGvvgq3eg
[00:25:02] Jeff Beck, MLST interview.
https://www.youtube.com/watch?v=9suqiofCiwM
[00:28:00] John Jumper
https://www.patreon.com/posts/john-jumper-on-144557652
[00:30:48] Noam Chomsky, MLST interview.
https://www.youtube.com/watch?v=axuGfh4UR9Q
[00:31:59] Anna Ciaunica
https://www.patreon.com/posts/dr-anna-ciaunica-144509970
[00:33:12] Mike Israetel debate on functionalism.
https://www.youtube.com/watch?v=4yYcN_mFi18
Company/Org:
[00:09:25] Neuralink
https://neuralink.com/
Paper:
[00:23:45] A Logical Calculus of Ideas Immanent in Nervous Activity
https://link.springer.com/article/10.1007/BF02478259
[00:28:00] Highly Accurate Protein Structure Prediction with AlphaFold
https://www.nature.com/articles/s41586-021-03819-2
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!")
![Google Researcher Shows Life Emerges From Code [Blaise Agüera y Arcas]](https://i.ytimg.com/vi/rMSEqJ_4EBk/mqdefault.jpg "Google Researcher Shows Life Emerges From Code [Blaise Agüera y Arcas]
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? Its literally a computer program, and the ribosomes in your cells are tiny universal computers building you according to those instructions.
**SPONSOR MESSAGES**
—
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https://www.prolific.com/?utm_source=mlst
—
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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
—
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 Neumanns 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
Dont 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
How something can be said about Telling More Than We Can Know [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: Failures to understand how everyday objects work [Rebeca Lawson]
https://link.springer.com/content/pdf/10.3758/bf03195929.pdf
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction [Kushin Mukherjee, Judith Fan et al]
https://arxiv.org/pdf/2312.03035
Brain-Score [Martin Schrimpf]
https://www.biorxiv.org/content/10.1101/407007v1.full.pdf
Feature Visualization [Chris Olah]
https://distill.pub/2017/feature-visualization/
THE REVERSAL CURSE [Lukas Berglund]
https://arxiv.org/pdf/2309.12288")
