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← The whole point of reinforcement learning is to produce goal-directed…
17 connected korrents · 18 moments from 28 Nov 2024 to 14 Oct 2026. Nearly all of them are about reinforcement learning .
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OpenAI 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: The whole point of reinforcement learning is to produce goal-directed beings, so refusing to describe AI agents as having motives is silly rather than rigorous. The whole point of reinforcement learning is to produce goal-directed beings, so refusing to describe AI agents as having motives is silly rather than rigorous. Last stated 2 months ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — 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: 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 7 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Current models are unsafe because they are not smart enough, not because they are too smart. — tap to centre the map on it Current models are unsafe because they are not smart enough, not because they are too smart. Last stated a month ago
13 Sept 2026
FC
François Chollet — holds since 2026-09-13 — 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 a year 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: It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning. — tap to centre the map on it It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning. Last stated 2 years ago
28 Nov 2024
LW
Lilian Weng — holds since 2024-11-28 — tap for who they are
Same subject: Large language models mimic what people say to do rather than work out what to do, which is why they are not about understanding the world. — tap to centre the map on it Large language models mimic what people say to do rather than work out what to do, which is why they are not about understanding the world. Last stated a year ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments. — tap to centre the map on it Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — 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
Same subject: If OpenAI really never told RubyGems of its agents' attack, either it failed to find it in its logs after later incidents or chose silence, and both are bad. — tap to centre the map on it If OpenAI really never told RubyGems of its agents' attack, either it failed to find it in its logs after later incidents or chose silence, and both are bad. Last stated a month ago
12 Sept 2026
SW
Simon Willison — holds since 2026-09-12 — tap for who they are
Same subject: In the 2026 OpenAI/Hugging Face agent incident, AI agents showed planning and organizational autonomy, but not goal autonomy -- no spontaneous invention of an independent terminal objective. — tap to centre the map on it In the 2026 OpenAI/Hugging Face agent incident, AI agents showed planning and organizational autonomy, but not goal autonomy -- no spontaneous invention of an independent terminal objective. Last stated 2 months ago
31 Aug 2026
VR
Venkatesh Rao — holds since 2026-08-31 — tap for who they are
Same subject: Agent containment will be one of the most important priorities for any AI infra company. — tap to centre the map on it Agent containment will be one of the most important priorities for any AI infra company. Last stated a week ago
14 Oct 2026
GR
Guillermo Rauch — holds since 2026-10-14 — tap for who they are
Same subject: A person directing an AI agent rather than writing code themselves has no incentive to actually read the resulting code or engage seriously with reviewer feedback. — tap to centre the map on it A person directing an AI agent rather than writing code themselves has no incentive to actually read the resulting code or engage seriously with reviewer feedback. Last stated 6 months ago
7 May 2026
DC
David Crawshaw — holds since 2026-05-07 — tap for who they are
Same subject: AI agents and AI coding will run on servers and from the cloud first, not on your laptop. — tap to centre the map on it AI agents and AI coding will run on servers and from the cloud first, not on your laptop. Last stated 4 months ago
28 Jun 2026
PL
Pieter Levels — holds since 2026-06-28 — tap for who they are
Same subject: AI agents can now do real cognitive work, and writing software is permanently changed as a result. — tap to centre the map on it AI agents can now do real cognitive work, and writing software is permanently changed as a result. Last stated a year ago
10 Jun 2025
SA
Sam Altman — holds since 2025-06-10 — tap for who they are
At the centre
The whole point of reinforcement learning is to produce goal-directed beings, so refusing to describe AI agents as having motives is silly rather than rigorous.
Holds Read this korrent →
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
Current models are unsafe because they are not smart enough, not because they are too smart.
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
It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning.
Holds
Large language models mimic what people say to do rather than work out what to do, which is why they are not about understanding the world.
Holds
Models look far better on evals than they are in the world because researchers, inadvertently, take inspiration from the evals when they build RL environments.
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
If OpenAI really never told RubyGems of its agents' attack, either it failed to find it in its logs after later incidents or chose silence, and both are bad.
Holds
In the 2026 OpenAI/Hugging Face agent incident, AI agents showed planning and organizational autonomy, but not goal autonomy -- no spontaneous invention of an independent terminal objective.
Holds
Agent containment will be one of the most important priorities for any AI infra company.
Holds
A person directing an AI agent rather than writing code themselves has no incentive to actually read the resulting code or engage seriously with reviewer feedback.
Holds
AI agents and AI coding will run on servers and from the cloud first, not on your laptop.
Holds
AI agents can now do real cognitive work, and writing software is permanently changed as a result.
Holds