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← People who explain everything with one big idea are bad at modelling…
17 connected korrents · 16 moments on record from 30 Aug 2018 to 28 Jul 2026.
Everything filed under LLMs
LLMs
Everything filed under reinforcement learning
reinforcement learning
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scaling laws Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Same subject Read this korrent: People who explain everything with one big idea are bad at modelling the world, and the media selects for them because they sound confident. People who explain everything with one big idea are bad at modelling the world, and the media selects for them because they sound confident. Last stated 8 years ago
30 Aug 2018
SG
Stephan Guyenet — holds since 2018-08-30 — tap for who they are
Same subject: Large language models are a bad way to do science, because they have been fed so much that nobody knows what they already knew. — tap to centre the map on it Large language models are a bad way to do science, because they have been fed so much that nobody knows what they already knew. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: The most fundamental thing wrong with today's models is not scale or efficiency but that they generalize dramatically worse than people do. — tap to centre the map on it The most fundamental thing wrong with today's models is not scale or efficiency but that they generalize dramatically worse than people do. Last stated 9 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: Language models will not replace good ideas, only the low-level work, so what they threaten is the ability to start a creative career rather than to have one. — tap to centre the map on it Language models will not replace good ideas, only the low-level work, so what they threaten is the ability to start a creative career rather than to have one. Last stated 10 months ago
31 Oct 2025
DH
Dan Houser — holds since 2025-10-31 — tap for who they are
Same subject: No single person can understand a result like the Higgs boson in depth, and that is what a paper with a thousand authors actually means. — tap to centre the map on it No single person can understand a result like the Higgs boson in depth, and that is what a paper with a thousand authors actually means. Last stated 5 months ago
7 Apr 2026
MN
Michael Nielsen — holds since 2026-04-07 — tap for who they are
Same subject: Locking humanity into one model's or one company's moral worldview would buy a little short-term safety and cost a long-term disaster. — tap to centre the map on it Locking humanity into one model's or one company's moral worldview would buy a little short-term safety and cost a long-term disaster. Last stated a month ago
28 Jul 2026
SA
Sam Altman — holds since 2026-07-28 — tap for who they are
Same subject: Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it. — tap to centre the map on it Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. — tap to centre the map on it Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Declining many-worlds because it extrapolates past what we can observe is methodologically respectable and still a failure of nerve. — tap to centre the map on it Declining many-worlds because it extrapolates past what we can observe is methodologically respectable and still a failure of nerve. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. — tap to centre the map on it A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. Last stated a year ago
7 Jun 2025
GM
Gary Marcus — holds since 2025-06-07 — tap for who they are
Same subject: A language model is not using language at all, because language requires an intention to communicate. — tap to centre the map on it A language model is not using language at all, because language requires an intention to communicate. Last stated 2 years ago
31 Aug 2024
TC
Ted Chiang — holds since 2024-08-31 — tap for who they are
Same subject: A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. — tap to centre the map on it A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. — tap to centre the map on it Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. — tap to centre the map on it Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
At the centre
People who explain everything with one big idea are bad at modelling the world, and the media selects for them because they sound confident.
Holds SG Stephan GuyenetRead this korrent →
Large language models are a bad way to do science, because they have been fed so much that nobody knows what they already knew.
Holds RS Richard Sutton
The most fundamental thing wrong with today's models is not scale or efficiency but that they generalize dramatically worse than people do.
Holds IS Ilya Sutskever
Language models will not replace good ideas, only the low-level work, so what they threaten is the ability to start a creative career rather than to have one.
Holds DH Dan Houser
No single person can understand a result like the Higgs boson in depth, and that is what a paper with a thousand authors actually means.
Holds MN Michael Nielsen
Locking humanity into one model's or one company's moral worldview would buy a little short-term safety and cost a long-term disaster.
Holds
Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it.
Holds RS Richard Sutton
Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago.
Holds
Declining many-worlds because it extrapolates past what we can observe is methodologically respectable and still a failure of nerve.
Holds SC Sean Carroll
A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted.
Holds GM Gary Marcus
A language model is not using language at all, because language requires an intention to communicate.
Holds TC Ted Chiang
A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human.
Holds SC Sean Carroll
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one.
Holds NL Nathan Lambert
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds DP Dylan Patel
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds JH Jensen Huang