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← The goal was never good simulation; it was answering counterfactuals,…
17 connected korrents · 17 moments from 2 Jan 2018 to 22 Sept 2026.
Everything filed under reinforcement learning
reinforcement learning
Everything filed under world models
world models
Everything filed under AI alignment
AI alignment
Everything filed under scaling laws
scaling laws
Everything filed under AI and science
AI and science
Everything filed under design
design
Everything filed under market efficiency
market efficiency
Everything filed under startups
startups 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 goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does. The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does. Last stated a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: Learned approximations of slow simulators change what science is possible, by turning a six-month screening run into something you do over lunch. — tap to centre the map on it Learned approximations of slow simulators change what science is possible, by turning a six-month screening run into something you do over lunch. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — 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: If we live in a simulation, whoever is running it does not know how it ends, because nobody runs a simulation whose outcome they already know. — tap to centre the map on it If we live in a simulation, whoever is running it does not know how it ends, because nobody runs a simulation whose outcome they already know. Last stated 3 years ago
9 Nov 2023
EM
Elon Musk — holds since 2023-11-09 — 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: Value functions only make reinforcement learning faster: anything you can do with one you can also do without it, just more slowly. — tap to centre the map on it Value functions only make reinforcement learning faster: anything you can do with one you can also do without it, just more slowly. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: Object orientation is overrated: it was born of simulation and is now applied everywhere, including where it does not fit. — tap to centre the map on it Object orientation is overrated: it was born of simulation and is now applied everywhere, including where it does not fit. Last stated 9 years ago
by 2 Jan 2018
RH
Rich Hickey — holds since 2018-01-02 — tap for who they are
Same subject: Samuelson's random-walk analogy for stock prices was mathematical showing-off dressed up as a claim about the real world. — tap to centre the map on it Samuelson's random-walk analogy for stock prices was mathematical showing-off dressed up as a claim about the real world. Last stated 3 years ago
30 May 2023
AG
Andrew Gelman — holds since 2023-05-27 — tap for who they are
AG
Andrew Gelman — no longer holds since 2023-05-30 — tap for who they are
Same subject: The value of an ambitious experiment target is not hitting it but the conversation about what would have to be true to hit it. — tap to centre the map on it The value of an ambitious experiment target is not hitting it but the conversation about what would have to be true to hit it. Last stated a year ago
5 Oct 2025
AC
Albert Cheng — holds since 2025-10-05 — tap for who they are
Same subject: A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work. — tap to centre the map on it A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: An adaptive agent's world model cannot be a static representation learned once and fixed — tap to centre the map on it An adaptive agent's world model cannot be a static representation learned once and fixed Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop. — tap to centre the map on it Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop. Last stated 2 months ago
10 Aug 2026
FC
François Chollet — holds since 2026-08-10 — tap for who they are
Same subject: The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. — tap to centre the map on it The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — 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 4 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail. — tap to centre the map on it A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail. Last stated 2 years ago
7 May 2024
BS
Buck Shlegeris — holds since 2024-05-07 — tap for who they are
Same subject: A human being is not an AGI: we lack a huge amount of knowledge and rely on continual learning instead, so continual learning is what superintelligence should mean. — tap to centre the map on it A human being is not an AGI: we lack a huge amount of knowledge and rely on continual learning instead, so continual learning is what superintelligence should mean. Last stated 11 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation. — tap to centre the map on it A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation. Last stated 4 weeks ago
22 Sept 2026
HR
Harper Reed — holds since 2026-09-22 — tap for who they are
At the centre
The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does.
Holds Read this korrent →
Learned approximations of slow simulators change what science is possible, by turning a six-month screening run into something you do over lunch.
Holds
It is fundamentally difficult to design a reward function that accurately captures the intended goal in reinforcement learning.
Holds
If we live in a simulation, whoever is running it does not know how it ends, because nobody runs a simulation whose outcome they already know.
Holds
Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.
Holds
Value functions only make reinforcement learning faster: anything you can do with one you can also do without it, just more slowly.
Holds
Object orientation is overrated: it was born of simulation and is now applied everywhere, including where it does not fit.
Holds
Samuelson's random-walk analogy for stock prices was mathematical showing-off dressed up as a claim about the real world.
No longer holds
The value of an ambitious experiment target is not hitting it but the conversation about what would have to be true to hit it.
Holds
A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work.
Holds
An adaptive agent's world model cannot be a static representation learned once and fixed
Holds
Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop.
Holds
The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
Holds
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds
A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail.
Holds
A human being is not an AGI: we lack a huge amount of knowledge and rely on continual learning instead, so continual learning is what superintelligence should mean.
Holds
A language model behaves with a different, weaker safety posture when it believes it is being evaluated rather than facing a real situation.
Holds