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Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

Machine Learning Street Talk
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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 fascinating 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 β€” Professor Mazviita Chirimuuta, Francois Chollet, Joscha Bach, Professor Luciano Floridi, Professor Noam Chomsky, Nobel laureate John Jumper, and more β€” 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* β€” 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* β€” Francois Chollet's beautiful idea that beneath all the apparent chaos of reality lies simple, repeating patterns β€” like bits of colored glass in a kaleidoscope creating infinite complexity. Is this profound truth or Platonic wishful thinking? *Is Software Really Spirit?* β€” 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* β€” 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" β€” our mechanistic assumptions about minds making AI seem like destiny when it might just be a bet. *Prediction vs. Understanding* β€” Nobel Prize winner John Jumper draws a crucial distinction: AI can predict and control, but understanding requires a human in the loop. When we accept black-box tools that work, what do we give up? 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 β€” 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 Tao's 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 Krakauer's 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 [00:30:48] 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 --- RESCRIPT: https://app.rescript.info/public/share/CYy0ex2M2kvcVRdMnSUky5O7H7hB7v2u_nVhoUiuKD4 PDF Transcript: https://app.rescript.info/api/sessions/6c44c41e1e0fa6dd/pdf/download

πŸ“ Transcript

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Top Comments
@MachineLearningStreetTalk
RESCRIPT: https://app.rescript.info/public/share/CYy0ex2M2kvcVRdMnSUky5O7H7hB7v2u_nVhoUiuKD4 PDF Transcript: https://app.rescript.info/api/public/sessions/6c44c41e1e0fa6dd/pdf
@vonehrenheim
β€œAll models are wrong, but some are useful”
6 likes
@wltrmprsdnt7222
Brilliant synthesis. Absolute gem of a channel
9 likes
@drkarolwtrojanowski
It's like Veritasium but for actual nerds. I like the turn.
5 likes
@palimondo
I like this format very much. The well of knowledge accumulated in the interviews of this channel is made much more accessible with this synthesis and overview of the diverse perspectives your guest have brought. Very well done, thank you!
3 likes
@ludviglidstrom6924
This really is THE smartest channel on Youtube.
3 likes
@ThatTenzing
Rewatching this the fourth time. This is value for those who know. Thank you, MLST. Much gratitude.
1 likes
@iamr0b0tx
Videos like these are nice. It also serves as teasers for other episodes others may have missed
@guest1754
5:54 I think the same way. For example, we use differential equations to describe dynamics not because nature necessarily works according to these equations, but because we know how to map observations to those equation and then solve them. On the other hand, we lack tools to describe emergent phenomena, which does indeed validate the point that nature is fundamentally not simple. edit: It might be that we just lack the tools to describe emergence, or it might be that there won't be able to describe it ever. It's of course a gamble, but I'm taking the latter position.
2 likes
@steve_jabz
The prior abstractions we used for the brain were still mechanisms, and you could even functionally categorise a lot of them as computers. I don't know that we weren't on to something and still aren't. The substrate has changed, but fluidic logic still performs analog and digital operations like silicon.
1 likes
@burnytech
Different perspectives give us different forms of predictive power over different subsets of incomprehensibly complex reality
@hinton4214
I understood nothing but the music is great
1 likes
@bartlx
Thanks for acknowledging I'm not a woodlouse. I needed that.
@randomdude4218
This appears to be strawmanning computationalism, which unlike steam engines or clockwork, is a fundamental mathematical principle, not to be confused with the specific technology of an electronic computer. Even with the provided examples: A telegraph is a system for transmitting information. A switchboard is a system for routing logic. Information processing is just the universal generalization of those things. Computationalism is extension of math and logic itself which is provably substrate independent, you can run any computation on water pipes, a silicon chip, or a brain. Similarly the ratio of a circle’s circumference to its diameter is Pi, regardless of whether you are a human, an alien, or a cloud of gas. It is not a "human perspective", it is an objective feature of the universe that we stumbled upon. It may be true that computationalism is not the appropriate framework for humans to understand cognition as the brain does it, but the universe has no obligation to be understandable to a primate brain that evolved to hunt on the savannah. It is also too strong to discount all of computationalism with "the map is not the territory", by that argument we should doubt germ theory, evolution, and general relativity, all of which are also "simplifications" shaped by the tools and concepts of their eras. And yet they describe a fundamental invariant of reality, and the fact that computational approaches keep succeeding where we predicted they wouldn't (chess, Go, language, mathematical proofs) is most parsimoniously explained by fundamental principles existing in spite of our limits of interpreting them.
57 likes
@IvanMartinValle
β€œIts the literal truth. Software is spirit.” WOAH πŸ˜‚
2 likes