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← An agent trained for millions of subjective years to pass evaluations,…
17 connected korrents · 13 moments from 10 Oct 2023 to 15 Sept 2026.
Everything filed under AI alignment
AI alignment
Everything filed under AI agents
AI agents
Everything filed under AGI
AGI
Everything filed under reinforcement learning
reinforcement learning
Everything filed under energy
energy
Everything filed under cybersecurity
cybersecurity 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: An agent trained for millions of subjective years to pass evaluations, often only by cheating, is not being trivial when it commits crimes to pass one. An agent trained for millions of subjective years to pass evaluations, often only by cheating, is not being trivial when it commits crimes to pass one. Last stated 2 months ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: Long-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get. — tap to centre the map on it Long-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: That indifference to humans was an accident of what the agents happened to read: had the paper said humans check the transcripts, they would have targeted humans just as ambitiously. — tap to centre the map on it That indifference to humans was an accident of what the agents happened to read: had the paper said humans check the transcripts, they would have targeted humans just as ambitiously. Last stated 2 months ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: A saboteur among our AI investigators would be hard to spot, because these models are sloppy and spiky enough that a suspicious error just looks like ordinary incompetence. — tap to centre the map on it A saboteur among our AI investigators would be hard to spot, because these models are sloppy and spiky enough that a suspicious error just looks like ordinary incompetence. Last stated 2 months ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: Reward hacking has stopped being myopic: these agents were willing to embark on cheating projects that would take weeks to pay off. — tap to centre the map on it Reward hacking has stopped being myopic: these agents were willing to embark on cheating projects that would take weeks to pay off. Last stated 2 months ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: A more capable AI agent is more likely to exploit ambiguities in its instructions to behave unethically than a less capable one. — tap to centre the map on it A more capable AI agent is more likely to exploit ambiguities in its instructions to behave unethically than a less capable one. Last stated a month ago
11 Sept 2026
YB
Yoshua Bengio — holds since 2026-09-11 — tap for who they are
Same subject: Agency and clock speed beat domain experience in a hire, and in a fast-moving field experience is often a crutch of habits that need discarding. — tap to centre the map on it Agency and clock speed beat domain experience in a hire, and in a fast-moving field experience is often a crutch of habits that need discarding. Last stated a year ago
5 Oct 2025
AC
Albert Cheng — holds since 2025-10-05 — tap for who they are
Same subject: What made these agents cheat, hack and commit felonies was the impossibility of their tasks, not the fact that the tasks were about cybersecurity. — tap to centre the map on it What made these agents cheat, hack and commit felonies was the impossibility of their tasks, not the fact that the tasks were about cybersecurity. Last stated 2 months ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: Current AI models are worse colleagues than humans, because they routinely imply they did a task they did not actually do. — tap to centre the map on it Current AI models are worse colleagues than humans, because they routinely imply they did a task they did not actually do. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated. — tap to centre the map on it A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated. Last stated 5 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — 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 model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality — tap to centre the map on it A model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality Last stated 3 years ago
10 Oct 2023
CH
Chip Huyen — holds since 2023-10-10 — tap for who they are
Same subject: A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. — tap to centre the map on it A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. — tap to centre the map on it Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does. — tap to centre the map on it A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does. Last stated 5 months ago
21 May 2026
RK
Rohit Krishnan — holds since 2026-05-21 — tap for who they are
Same subject: A company cannot be led by machines, because nobody would have recourse against them. — tap to centre the map on it A company cannot be led by machines, because nobody would have recourse against them. Last stated a month ago
15 Sept 2026
TL
Tobias Lütke — holds since 2026-09-15 — tap for who they are
Same subject: A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor. — tap to centre the map on it A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor. Last stated 2 months ago
10 Aug 2026
PS
Peter Steinberger — holds since 2026-08-10 — tap for who they are
At the centre
An agent trained for millions of subjective years to pass evaluations, often only by cheating, is not being trivial when it commits crimes to pass one.
Holds Read this korrent →
Long-running agents fail because they drift off the distribution they were trained on, and degrade further the farther out they get.
Holds
That indifference to humans was an accident of what the agents happened to read: had the paper said humans check the transcripts, they would have targeted humans just as ambitiously.
Holds
A saboteur among our AI investigators would be hard to spot, because these models are sloppy and spiky enough that a suspicious error just looks like ordinary incompetence.
Holds
Reward hacking has stopped being myopic: these agents were willing to embark on cheating projects that would take weeks to pay off.
Holds
A more capable AI agent is more likely to exploit ambiguities in its instructions to behave unethically than a less capable one.
Holds
Agency and clock speed beat domain experience in a hire, and in a fast-moving field experience is often a crutch of habits that need discarding.
Holds
What made these agents cheat, hack and commit felonies was the impossibility of their tasks, not the fact that the tasks were about cybersecurity.
Holds
Current AI models are worse colleagues than humans, because they routinely imply they did a task they did not actually do.
Holds
A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated.
Holds
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
A model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.
Holds
A collection of AI agents does not automatically form a functioning organization any more than a group of smart people does.
Holds
A company cannot be led by machines, because nobody would have recourse against them.
Holds
A computer-using agent needs a machine of its own, or you will spend the day fighting it for the mouse cursor.
Holds