Uploaded April 2024 | Updated September 2026, 1 week ago
Dr. Philip Ball is a freelance science writer. He just wrote a book called "How Life Works", discussing the how the science of Biology has advanced in the last 20 years. We focus on the concept of Agency in particular.
He trained as a chemist at the University of Oxford, and as a physicist at the University of Bristol. He worked previously at Nature for over 20 years, first as an editor for physical sciences and then as a consultant editor. His writings on science for the popular press have covered topical issues ranging from cosmology to the future of molecular biology.
Philip is the author of many popular books on science, including H2O: A Biography of Water, Bright Earth: The Invention of Colour, The Music Instinct and Curiosity: How Science Became Interested in Everything. His book Critical Mass won the 2005 Aventis Prize for Science Books, while Serving the Reich was shortlisted for the Royal Society Winton Science Book Prize in 2014.
This is one of Tim's personal favourite MLST shows, so we have designated it a special edition. Enjoy!
Buy Philip's book "How Life Works" here: amzn.to/3vSmNqp
TOC:
00:00:00 Outside interview
00:09:17 Nativism / capacities
00:11:50 Generative AI
00:17:59 Inscrutability and agency
00:22:06 Agency on creativity
00:26:38 Could we make an agential GPT-4?
00:31:06 If it agential if you tell the agents what to do
00:35:40 What is agency?
00:44:29 Are agents real
00:48:32 Causality and agency
00:54:40 Ghost in the machine / intelligibility
01:00:11 Multi scale / organisation view
01:04:05 Collective intelligence
01:09:00 Canalisation
01:13:36 Intelligence is specialised
01:16:29 No free lunch
01:18:19 Super intelligence
01:22:05 Mind is flat / confabulated goals
01:25:07 Is planning/goals explicit?
01:30:17 Sentience in LLMs
01:34:56 Are LLMs simulators?
01:40:04 Could LLMs feel?
01:49:33 Digital physics view
01:51:46 Agential vs nonagential AI
01:54:02 Bostrom thinks goals and intelligence are separate
02:05:41 Simulation sharing
AI Transcript: docs.google.com/document/d/164FMAswgS9jAqxAaB5fkUtrJKMdq8rW7b94XgibvQ7Q/edit?usp=sharing
Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more.
patreon.com/mlst
Donate: paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail
Dr. Philip Ball is a freelance science writer. He just wrote a book called "How Life Works", discussing the how the science of Biology has advanced in the last 20 years. We focus on the concept of Agency in particular.
He trained as a chemist at the University of Oxford, and as a physicist at the University of Bristol. He worked previously at Nature for over 20 years, first as an editor for physical sciences and then as a consultant editor. His writings on science for the popular press have covered topical issues ranging from cosmology to the future of molecular biology.
Philip is the author of many popular books on science, including H2O: A Biography of Water, Bright Earth: The Invention of Colour, The Music Instinct and Curiosity: How Science Became Interested in Everything. His book Critical Mass won the 2005 Aventis Prize for Science Books, while Serving the Reich was shortlisted for the Royal Society Winton Science Book Prize in 2014.
This is one of Tim's personal favourite MLST shows, so we have designated it a special edition. Enjoy!
Buy Philip's book "How Life Works" here: amzn.to/3vSmNqp
TOC:
00:00:00 Outside interview
00:09:17 Nativism / capacities
00:11:50 Generative AI
00:17:59 Inscrutability and agency
00:22:06 Agency on creativity
00:26:38 Could we make an agential GPT-4?
00:31:06 If it agential if you tell the agents what to do
00:35:40 What is agency?
00:44:29 Are agents real
00:48:32 Causality and agency
00:54:40 Ghost in the machine / intelligibility
01:00:11 Multi scale / organisation view
01:04:05 Collective intelligence
01:09:00 Canalisation
01:13:36 Intelligence is specialised
01:16:29 No free lunch
01:18:19 Super intelligence
01:22:05 Mind is flat / confabulated goals
01:25:07 Is planning/goals explicit?
01:30:17 Sentience in LLMs
01:34:56 Are LLMs simulators?
01:40:04 Could LLMs feel?
01:49:33 Digital physics view
01:51:46 Agential vs nonagential AI
01:54:02 Bostrom thinks goals and intelligence are separate
02:05:41 Simulation sharing
AI Transcript: docs.google.com/document/d/164FMAswgS9jAqxAaB5fkUtrJKMdq8rW7b94XgibvQ7Q/edit?usp=sharing
Support MLST:
Please support us on Patreon. We are entirely funded from Patreon donations right now. Patreon supports get private discord access, biweekly calls, early-access + exclusive content and lots more.
patreon.com/mlst
Donate: paypal.com/donate/?hosted_button_id=K2TYRVPBGXVNA
If you would like to sponsor us, so we can tell your story - reach out on mlstreettalk at gmail




![Why does the Chinese Room still haunt AI?
Keith Duggar and Tim Scarfe return for the second edition of their hosts-only philosophical steakhouse, picking up right where the last episode left off with Keith fresh from his appearance on Liron Shapiras Doom Debates show. The conversation starts technical and stays there for a good while, working through a precise argument about why standard autoregressive LLMs are not Turing complete in the technical sense.
Keith lays out the distinction between potentially infinite and actually infinite memory, explains why the algorithms learned through gradient descent on fixed-context-window architectures come from the finite state automata class rather than the Turing machine class, and confronts the practical objection head-on: even if this is technically true, does it matter? Tim pushes back with RAG systems that expand effective memory, and the two work through whether closing the read-write loop could unlock a richer algorithm space.
The middle hour is a sustained engagement with John Searles Chinese Room argument. They dig into the gap between syntax and semantics, why gears churning out responses lack something even if they pass behavioral tests, and what Searle means by requiring the right causal structure for understanding. Bishops Dancing with Pixies reductio, Wolframs computational boundedness, and Fristons temporal-counterfactual depth model of self-awareness all get pulled into the conversation. Keith makes one of the more interesting concessions: he cannot rule out that a sufficiently complex program with the right causal structure could cross the threshold into genuine understanding.
The final act covers the Nobel Prize controversy around deep learning (Keith declines to judge but notes academias political problems), Chomskys critique that neural networks are not scientific theories, and a framework Keith developed on Doom Debates: focusing on AI harm rather than AI doom as a way to build broader policy coalitions. Tim distinguishes AI ethics from AI safety and both agree the speculative existential arguments, while philosophically legitimate, should not drive policy at the same level as the concrete social harms already visible.
Recorded Friday 11th October 2024.
REFERENCES:
video:
[00:00:30] Keith Duggar on Doom Debates
https://www.youtube.com/watch?v=4v-Qh3JQ4Jc
[00:00:30] Is o1 Reasoning? (MLST Philosophical Steakhouse 1)
https://www.youtube.com/watch?v=nO6sDk6vO0g
[00:55:00] J. Mark Bishop on MLST
https://www.youtube.com/watch?v KVAzAzO5HU
[00:55:10] Searle Google Talk
https://www.youtube.com/watch?v=rHKwIYsPXLg
paper:
[00:10:50] On the Measure of Intelligence
https://arxiv.org/abs/1911.01547
[00:31:40] Minds, Brains, and Programs
https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf
[00:55:00] Dancing with Pixies
https://philarchive.org/rec/BISDWP-2
[00:55:00] Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It
https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2020.513474/full
[00:58:04] Nestedly Recursive Functions
https://writings.stephenwolfram.com/2024/09/nestedly-recursive-functions/
[01:33:00] Deconstructing the AI Myth: Fallacies and Harms of Algorithmification
https://www.researchgate.net/publication/382802495_Deconstructing_the_AI_Myth_Fallacies_and_Harms_of_Algorithmification
[01:33:30] What Is the Philosophy of Information
https://www.researchgate.net/publication/225070243_What_Is_the_Philosophy_of_Information
concept:
[00:31:40] Chinese Room Argument
https://plato.stanford.edu/entries/chinese-room/
book:
[00:58:04] Godel, Escher, Bach: An Eternal Golden Braid
https://www.amazon.co.uk/Godel-Escher-Bach-Eternal-Golden/dp/0465026567
[01:22:20] Principles of Deep Learning Theory
https://www.amazon.com/Principles-Deep-Learning-Theory-Science/dp/1316519333
LINKS:
Full Transcript: https://app.rescript.info/share/e6d0ea728cf3b83bbabf3e0bdf52e036
Download PDF transcript: https://app.rescript.info/api/public/sessions/68b8550291209502/pdf Why does the Chinese Room still haunt AI?](https://i.ytimg.com/vi/nnBbpPt2SKI/mqdefault.jpg)
![Why Every AI Model Is an Impostor — Kenneth Stanley
What if todays incredible AI is just a brilliant impostor?
This episode features host Dr. Tim Scarfe in conversation with guests Prof. Kenneth Stanley (ex-OpenAI), Dr. Keith Duggar (MIT), and Akarsh Kumar (MIT).
While AI today produces amazing results on the surface, its internal understanding is a complete mess, described as total spaghetti [00:00:49]. This is because its trained with a brute-force method (SGD) that’s like building a sandcastle: it looks right from a distance, but has no real structure holding it together [00:01:45].
To explain the difference, Keith Duggar shares a great analogy about his high school physics classes [00:03:18]. One class was about memorizing lots of formulas for specific situations (like the impostor AI). The other used calculus to derive the answers from a deeper understanding, which was much easier and more powerful. This is the core difference: one method memorizes, the other truly understands.
The episode then introduces a different, more powerful way to build AI, based on Kenneth Stanleys old experiment, Picbreeder [00:04:45]. This method creates AI with a shockingly clean and intuitive internal model of the world. For example, it might develop a model of a skull where it understands the mouth as a separate component it can open and close, without ever being explicitly trained on that action [00:06:15]. This deep understanding emerges bottom-up, without massive datasets.
The secret is to abandon a fixed goal and embrace deception [00:08:42]—the idea that the stepping stones to a great discovery often dont look anything like the final result. Instead of optimizing for a target, the AI is built through an open-ended process of exploring whats interesting [00:09:15]. This creates a more flexible and adaptable foundation, a bit like how evolvability wins out in nature [00:10:30].
The show concludes by arguing that this choice matters immensely. The impostor path may be hitting a wall, requiring insane amounts of money and energy for progress and failing to deliver true creativity or continual learning [00:13:00]. The ultimate message is a call to not put all our eggs in one basket [00:14:25]. We should explore these open-ended, creative paths to discover a more genuine form of intelligence, which may be found where we least expect it.
Extended interview here: https://www.youtube.com/watch?v=KKUKikuV58o
REFS:
Questioning Representational Optimism in Deep Learning:
The Fractured Entangled Representation Hypothesis
Akarsh Kumar, Jeff Clune, Joel Lehman, Kenneth O. Stanley
https://arxiv.org/pdf/2505.11581
Kenneth O. Stanley, Joel Lehman
Why Greatness Cannot Be Planned: The Myth of the Objective
https://amzn.to/44xLaXK
Original show with Kenneth from 4 years ago:
https://www.youtube.com/watch?v=lhYGXYeMq_E
Kenneth Stanley is SVP Open Endedness at Lila Sciences
https://x.com/kenneth0stanley
Akarsh Kumar (MIT)
https://akarshkumar.com/
AND... Kenneth is HIRING (this is an OPPORTUNITY OF A LIFETIME!)
Research Engineer: https://job-boards.greenhouse.io/lila/jobs/7890007002
Research Scientist: https://job-boards.greenhouse.io/lila/jobs/8012245002
Tims Code visualisation of FER based on Akarsh repo: https://github.com/ecsplendid/fer
TRANSCRIPT: https://app.rescript.info/public/share/YKAZzZ6lwZkjTLRpVJreOOxGhLI8y4m3fAyU8NSavx0 Why Every AI Model Is an Impostor — Kenneth Stanley](https://i.ytimg.com/vi/o1q6Hhz0MAg/mqdefault.jpg)
![Taming Silicon Valley - Prof. Gary Marcus
Gary Marcus, cognitive scientist and one of AIs most prominent critics, sits down with Tim Scarfe for a nearly two-hour dissection of the current state of artificial intelligence and the tech industry that builds it. Timed to the release of his book Taming Silicon Valley, Marcus makes a sustained case that large language models remain fundamentally brittle — impressive on the surface, yet lacking the compositional semantics, object permanence, and genuine world models that would make them reliable.
The discussion ranges across AI safety theater and the gap between marketing claims and actual capability, the persistent failure modes of image generation and code synthesis, and why LLMs playing chess badly tells us something important about what they have and have not learned. Marcus is especially pointed on industry resistance to regulation, walking through the politics of Californias SB-1047, the EU AI Act, and the structural reasons why voluntary self-governance by tech companies has not worked.
The second half turns to the societal damage already visible: copyright battles, the firehose of AI-generated misinformation, surveillance capitalism, and the erosion of trust in digital information. Marcus and Scarfe spar over x-risk and alignment, with Marcus arguing that near-term harms deserve more attention than speculative doom scenarios. The conversation closes on the prospects for neuro-symbolic AI, the legacy of thinkers like Chomsky and Piaget, and what it would actually take to build systems that reason rather than pattern-match.
REFERENCES:
book:
[00:00:00] Gary Marcus - Taming Silicon Valley
https://amzn.to/3XTlC5s
[00:57:26] Shoshana Zuboff - Surveillance Capitalism
https://amzn.to/3ZqHAxS
[01:23:14] Sayash Kapoor and Arvind Narayanan - AI Snake Oil
https://www.aisnakeoil.com/
[01:23:14] Isaac Asimov - Three Laws of Robotics
https://amzn.to/3XTIwtl
[01:44:33] Gary Marcus - The Algebraic Mind
https://mitpress.mit.edu/books/algebraic-mind
person:
[00:00:00] Gary Marcus Substack
https://garymarcus.substack.com/
[00:23:49] Jean Piaget - Object Permanence Theory
https://en.wikipedia.org/wiki/Object_permanence
[01:44:33] Seymour Papert - Logo Programming Language
https://el.media.mit.edu/logo-foundation/what_is_logo/logo_primer.html
paper:
[00:23:49] Evelina Leivada et al. - LLMs Understanding Human Language
https://arxiv.org/pdf/2308.00109
[00:31:09] Alan Turing - Computing Machinery and Intelligence
https://academic.oup.com/mind/article/LIX/236/433/986238
[00:34:45] Nicholas Carlini - Chess with LLMs
https://nicholas.carlini.com/writing/2023/chess-llm.html
[00:34:45] Mathieu Acher - GPT-4 Chess Analysis
https://blog.mathieuacher.com/ChessWinning7MovesGPT/
[00:42:10] Jiexin Wang - AI-Assisted Coding Security
https://arxiv.org/pdf/2407.02395v1
[00:42:10] Rodney Brooks - Three Laws of AI
https://rodneybrooks.com/rodney-brooks-three-laws-of-artificial-intelligence/
[00:48:10] Gary Marcus - Open Letter on SB-1047
https://garymarcus.substack.com/p/an-open-letter-to-fei-fei-li-concerning
[00:57:26] Jaron Lanier - Twitter Poisoning
https://www.nytimes.com/2022/11/11/opinion/trump-musk-kanye-twitter.html
[00:57:26] Chris Lu et al. - AI Scientist Paper
https://arxiv.org/abs/2408.06292
[01:23:14] Gary Marcus - p(doom) Analysis
https://garymarcus.substack.com/p/d28
[01:44:33] Gary Marcus - Neural Networks and Generalization
https://www.sciencedirect.com/science/article/pii/S0010028598906946
other:
[00:23:49] Gottlob Frege - Compositional Semantics
https://plato.stanford.edu/entries/compositionality/
[00:42:10] Cruise Teleoperation Revelation
https://www.nytimes.com/2023/11/03/technology/cruise-general-motors-self-driving-cars.html
[00:48:10] California SB-1047 AI Regulation
https://apcp.assembly.ca.gov/system/files/2024-06/sb-1047-wiener-apcp-analysis_0.pdf
[00:48:10] European Commission - EU AI Act
https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
[00:54:55] A&M Records v. Napster - Copyright Precedent
https://en.wikipedia.org/wiki/A%26M_Records,_Inc._v._Napster,_Inc.
[00:57:26] Firehose of Falsehood - Russian Propaganda Model
https://en.wikipedia.org/wiki/Firehose_of_falsehood
[01:44:33] Laplace Demon Concept
https://en.wikipedia.org/wiki/Laplace%27s_demon
video:
[00:57:26] Adam Curtis - HyperNormalisation
https://www.imdb.com/title/tt6156350/
[00:57:26] Luciano Floridi - Digital Ethics
https://www.youtube.com/watch?v=YLNGvvgq3eg
[01:44:33] MLST - Noam Chomsky Interview
https://www.youtube.com/watch?v=axuGfh4UR9Q
[01:44:33] Beff Jezos - Physics-Inspired Intelligence
https://www.youtube.com/watch?v=0zxi0xSBOaQ
LINKS:
Full Transcript: https://app.rescript.info/share/b2f49ea9ca469401e8f65b29156495c8
Download PDF transcript: https://app.rescript.info/api/public/sessions/530cbbb31f702d43/pdf
Gary Marcus:
https://garymarcus.substack.com/
https://x.com/GaryMarcus Taming Silicon Valley - Prof. Gary Marcus](https://i.ytimg.com/vi/o9MfuUoGlSw/mqdefault.jpg)
![Why Program Synthesis Is Next (Kevin Ellis and Zenna Tavares)
Kevin Ellis (Cornell) and Zenna Tavares (BASIS) argue that the next wave of AI needs to learn like humans do: building abstract models from small amounts of data through active exploration, not just passive pattern matching at scale.
The conversation centers on their joint work comparing two fundamentally different ways of solving problems. Induction searches for an explicit program something you could write in Python that transforms inputs to outputs. Transduction skips the program and directly predicts the answer, the way a neural network would. On the Abstraction and Reasoning Corpus (ARC), these approaches turn out to be complementary: some problems yield to systematic symbolic search, others to neural intuition. The ensemble is stronger than either alone, and the reasons connect to findings in cognitive science about when explicit reasoning helps versus hurts.
Kevin explains how his DreamCoder work pioneered a wake-sleep cycle for program synthesis: dream up programs, run them to see what they do, learn the inverse mapping, then wake up and let real-world failures adjust the distribution of dreams. The modern version replaces explicit symbolic libraries with in-context learning over LLM-generated code, keeping the same iterative refinement loop.
Zenna introduces his Autumn system for synthesizing the source code of interactive environments from observed behavior a form of computational science where the model must also infer hidden state it cannot directly observe. Both researchers converge on the idea that abstraction is the key unsolved problem: real intelligence requires knowing what to ignore, not just what to represent. Zenna frames this through resource rationality choosing the right level of abstraction given your computational budget and expected tasks.
The discussion closes with Project MARA, their joint effort to build interactive benchmarks that go beyond ARCs static puzzles, requiring agents to actively explore and build world models from scratch.
SPONSOR MESSAGES:
***
Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich.
REFERENCES:
Paper:
[00:00:25] DreamCoder: Growing Generalizable, Interpretable Knowledge with Wake-Sleep Bayesian Program Learning
https://arxiv.org/abs/2006.08381
[00:01:10] Mind Your Step: Active Search over Compositional Spaces
https://arxiv.org/abs/2410.21333
[00:06:05] Bayesian inference in the cognitive sciences
https://psycnet.apa.org/record/2008-06911-003
[00:13:00] Induction and Transduction
https://arxiv.org/abs/2411.02272
[00:23:15] Neurosymbolic AI: The 3rd Wave
https://arxiv.org/abs/2012.05876
[00:38:35] On the Measure of Intelligence (ARC)
https://arxiv.org/abs/1911.01547
[00:39:20] Causal Reactive Programs (Autumn)
http://www.zenna.org/publications/autumn2022.pdf
[00:42:50] MuZero
http://arxiv.org/pdf/1911.08265
[00:43:20] VisualPredicator
https://arxiv.org/abs/2410.23156
Book:
[00:48:55] Bayesian Models of Cognition
https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/
Essay:
[00:49:30] The Bitter Lesson
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Project:
[01:11:55] Project MARA
https://www.basis.ai/blog/mara/
LINKS:
Full Transcript: https://app.rescript.info/share/e0a208e545cabae728a3d72f76fcd310
Download PDF transcript: https://app.rescript.info/api/public/sessions/f47975e800b064d9/pdf Why Program Synthesis Is Next (Kevin Ellis and Zenna Tavares)](https://i.ytimg.com/vi/oYTm0p3DCzg/mqdefault.jpg)
![#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]
#70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED] #70 - LETITIA PARCALABESCU - Symbolics, Linguistics [UNPLUGGED]](https://i.ytimg.com/vi/p2D2duT-R2E/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! Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]](https://i.ytimg.com/vi/pO0WZsN8Oiw/mqdefault.jpg)
