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← The Abstraction and Reasoning Corpus can measure human-like general…
14 connected korrents · 13 moments on record from 5 Nov 2019 to 7 Aug 2026.
Everything filed under measuring intelligence
measuring intelligence
Everything filed under neural networks
neural networks
Everything filed under scaling laws
scaling laws
Everything filed under ARC-AGI
ARC-AGI
Everything filed under AGI
AGI 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 Abstraction and Reasoning Corpus can measure human-like general fluid intelligence and enable fair comparisons between AI systems and humans. The Abstraction and Reasoning Corpus can measure human-like general fluid intelligence and enable fair comparisons between AI systems and humans. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — tap for who they are
Same subject: Passing ARC-AGI does not amount to achieving AGI: o3 still fails on some very easy tasks, indicating fundamental differences from human intelligence. — tap to centre the map on it Passing ARC-AGI does not amount to achieving AGI: o3 still fails on some very easy tasks, indicating fundamental differences from human intelligence. Last stated 2 years ago
20 Dec 2024
FC
François Chollet — holds since 2024-12-20 — tap for who they are
Same subject: If intelligence is the process of acquiring skills, no single task demonstrates intelligence unless it is a meta-task of skill-acquisition across many tasks. — tap to centre the map on it If intelligence is the process of acquiring skills, no single task demonstrates intelligence unless it is a meta-task of skill-acquisition across many tasks. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — tap for who they are
Same subject: Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence. — tap to centre the map on it Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — tap for who they are
Same subject: Solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience. — tap to centre the map on it Solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — tap for who they are
Same subject: The LLM line of research will reach a capability plateau. — tap to centre the map on it The LLM line of research will reach a capability plateau. Last stated a month ago
7 Aug 2026
FC
François Chollet — no longer holds since 2026-08-07 — tap for who they are
GM
Gary Marcus — holds since 2025-06-07 — tap for who they are
Same subject: The memorize-fetch-apply paradigm behind LLMs can reach arbitrary skill given training data, but it cannot adapt to novelty or acquire new skills on the fly. — tap to centre the map on it The memorize-fetch-apply paradigm behind LLMs can reach arbitrary skill given training data, but it cannot adapt to novelty or acquire new skills on the fly. Last stated 2 years ago
20 Dec 2024
FC
François Chollet — holds since 2024-12-20 — tap for who they are
FC
François Chollet — no longer holds since 2024-12-20 — 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: AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together. — tap to centre the map on it AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together. Last stated 5 months ago
6 Apr 2026
HT
Helen Toner — holds since 2026-04-06 — 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: Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source. — tap to centre the map on it Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source. Last stated 3 years ago
24 Jun 2023
YB
Yoshua Bengio — holds since 2023-06-24 — tap for who they are
Same subject: An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job. — tap to centre the map on it An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — 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: A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget. — tap to centre the map on it A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
At the centre
The Abstraction and Reasoning Corpus can measure human-like general fluid intelligence and enable fair comparisons between AI systems and humans.
Holds Read this korrent →
Passing ARC-AGI does not amount to achieving AGI: o3 still fails on some very easy tasks, indicating fundamental differences from human intelligence.
Holds
If intelligence is the process of acquiring skills, no single task demonstrates intelligence unless it is a meta-task of skill-acquisition across many tasks.
Holds
Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence.
Holds NL Nathan Lambert
Solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience.
Holds
The LLM line of research will reach a capability plateau.
Holds GM Gary MarcusNo longer holds
The memorize-fetch-apply paradigm behind LLMs can reach arbitrary skill given training data, but it cannot adapt to novelty or acquire new skills on the fly.
No longer holds
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
AI capabilities are jagged rather than smooth, so the different definitions of human-level AI will be met years apart rather than together.
Holds HT Helen Toner
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source.
Holds
An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job.
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 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
A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget.
Holds NB Noam Brown