Tap a claim on the ring to put it at the centre.
← Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it.
17 connected korrents · 16 moments on record from 24 Feb 2018 to 1 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under Google
Google
Everything filed under reinforcement learning
reinforcement learning
Everything filed under LLMs
LLMs
Everything filed under AI alignment
AI alignment
Everything filed under mathematics
mathematics 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: Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of 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: The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. — tap to centre the map on it The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: AI systems keep trying hard outside training because a model that only exerted itself when it detected training would be useless and would be selected away. — tap to centre the map on it AI systems keep trying hard outside training because a model that only exerted itself when it detected training would be useless and would be selected away. Last stated 6 days ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: Work on large language models is not progress toward artificial general intelligence, because the models do not understand what they read. — tap to centre the map on it Work on large language models is not progress toward artificial general intelligence, because the models do not understand what they read. Last stated a year ago
30 Jul 2025
AG
Alexey Guzey — holds since 2024-08-09 — tap for who they are
AG
Alexey Guzey — no longer holds since 2025-07-30 — tap for who they are
Same subject: Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes. — tap to centre the map on it Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes. Last stated 4 weeks ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — 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: We have almost no automated techniques for making a system transfer what it learns, and none of the few we have are used in modern deep learning. — tap to centre the map on it We have almost no automated techniques for making a system transfer what it learns, and none of the few we have are used in modern deep learning. Last stated 11 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception. — tap to centre the map on it Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception. Last stated 9 years ago
24 Feb 2018
PC
Paul Christiano — holds since 2018-02-24 — tap for who they are
Same subject: The internet is already past peak truth, and picking out training data worth learning from will only get harder. — tap to centre the map on it The internet is already past peak truth, and picking out training data worth learning from will only get harder. Last stated 4 months ago
13 May 2026
AH
Anders Hejlsberg — holds since 2026-05-13 — 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
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: 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: 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: A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost. — tap to centre the map on it A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost. Last stated 5 months ago
1 Apr 2026
TP
Thuan Pham — holds since 2026-04-01 — tap for who they are
Same subject: A crewed rocket cannot be made safe by making the booster reliable, so the only real way to improve safety is to carry an escape system. — tap to centre the map on it A crewed rocket cannot be made safe by making the booster reliable, so the only real way to improve safety is to carry an escape system. Last stated 3 years ago
14 Dec 2023
JB
Jeff Bezos — holds since 2023-12-14 — tap for who they are
Same subject: A monopolist that can no longer grow by winning new users can only grow by making its product worse for the users it already has. — tap to centre the map on it A monopolist that can no longer grow by winning new users can only grow by making its product worse for the users it already has. Last stated 3 years ago
28 Jul 2023
CD
Cory Doctorow — holds since 2023-07-28 — tap for who they are
At the centre
Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it.
Holds RS Richard SuttonRead this korrent →
The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out.
Holds
AI systems keep trying hard outside training because a model that only exerted itself when it detected training would be useless and would be selected away.
Holds AC Ajeya Cotra
Work on large language models is not progress toward artificial general intelligence, because the models do not understand what they read.
No longer holds AG Alexey Guzey
Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes.
Holds RG Ryan Greenblatt
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
We have almost no automated techniques for making a system transfer what it learns, and none of the few we have are used in modern deep learning.
Holds RS Richard Sutton
Technologies are almost always preceded by slightly worse versions of themselves, and there is no reason for AI to be the exception.
Holds PC Paul Christiano
The internet is already past peak truth, and picking out training data worth learning from will only get harder.
Holds AH Anders Hejlsberg
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
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
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
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
A company's staff-engineer bar should be set against the best companies in the industry rather than against its own history, which is what makes title inflation a real cost.
Holds TP Thuan Pham
A crewed rocket cannot be made safe by making the booster reliable, so the only real way to improve safety is to carry an escape system.
Holds JB Jeff Bezos
A monopolist that can no longer grow by winning new users can only grow by making its product worse for the users it already has.
Holds CD Cory Doctorow