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← That IQ tests are unbiased across groups is highly implausible and empirically unsupported.
17 connected korrents · 18 moments from 18 Oct 2007 to 12 Aug 2026.
Everything filed under documentation
documentation
Everything filed under ARC-AGI
ARC-AGI
Everything filed under scaling laws
scaling laws
Everything filed under AI and science
AI and science
Everything filed under behavioural science
behavioural science
Everything filed under benchmarks
benchmarks
Everything filed under Trauma
Trauma
Everything filed under America
America
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 Same subject Same subject Same subject Read this korrent: That IQ tests are unbiased across groups is highly implausible and empirically unsupported. That IQ tests are unbiased across groups is highly implausible and empirically unsupported. Last stated 19 years ago
18 Oct 2007
CS
Cosma Rohilla Shalizi — holds since 2007-10-18 — tap for who they are
Same subject: A performance test without an explanation of its limiting factor should be treated as unanalyzed and possibly bogus. — tap to centre the map on it A performance test without an explanation of its limiting factor should be treated as unanalyzed and possibly bogus. Last stated a year ago
3 Aug 2025
BG
Brendan Gregg — holds since 2025-08-03 — tap for who they are
Same subject: Failing to adjust raw data for known measurement biases produces inaccurate results. — tap to centre the map on it Failing to adjust raw data for known measurement biases produces inaccurate results. Last stated 3 months ago
27 Jul 2026
AD
Andrew Dessler — holds since 2026-07-27 — tap for who they are
Same subject: Debiasing has largely failed, and that is fine, because the biases identified are experience-built heuristics that only look like biases in hindsight. — tap to centre the map on it Debiasing has largely failed, and that is fine, because the biases identified are experience-built heuristics that only look like biases in hindsight. Last stated 4 years ago
9 Aug 2022
GK
Gary Klein — holds since 2022-08-09 — tap for who they are
Same subject: The study's small group sizes and unequal distribution increase the risk of unreliable or non-reproducible findings. — tap to centre the map on it The study's small group sizes and unequal distribution increase the risk of unreliable or non-reproducible findings. Last stated a year ago
25 Jul 2025
EB
Elisabeth Bik — holds since 2025-07-25 — tap for who they are
Same subject: AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools. — tap to centre the map on it AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools. Last stated 4 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: Coming into a project with a guess about what the data will show has, again and again, turned out to be wrong. — tap to centre the map on it Coming into a project with a guess about what the data will show has, again and again, turned out to be wrong. Last stated 6 months ago
8 May 2026
DR
David Reich — holds since 2026-05-08 — tap for who they are
Same subject: There is no single reasonable person in America, which makes the objective test for a true threat very hard to administer. — tap to centre the map on it There is no single reasonable person in America, which makes the objective test for a true threat very hard to administer. Last stated 3 years ago
27 Jun 2023
KW
Ken White — holds since 2023-06-27 — tap for who they are
Same subject: The real test of AGI is handing the system to a few hundred of the world's top experts and finding that none of them can locate an obvious flaw. — tap to centre the map on it The real test of AGI is handing the system to a few hundred of the world's top experts and finding that none of them can locate an obvious flaw. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: A company that deviates from open standards in its data exports without documenting the deviation creates an artificial vendor lock-in. — tap to centre the map on it A company that deviates from open standards in its data exports without documenting the deviation creates an artificial vendor lock-in. Last stated 2 years ago
3 Mar 2025
TS
Tobias Sjösten — holds since 2025-03-03 — tap for who they are
Same subject: A design proposal should describe the feature as though it already existed — once as a tutorial and once as reference documentation. — tap to centre the map on it A design proposal should describe the feature as though it already existed — once as a tutorial and once as reference documentation. Last stated 5 months ago
20 May 2026
AR
Alice Ryhl — holds since 2026-05-20 — tap for who they are
Same subject: A design system is only as good as its documentation. — tap to centre the map on it A design system is only as good as its documentation. Last stated 10 years ago
1 Sept 2016
MM
Mina Markham — holds since 2016-09-01 — 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 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 8 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 7 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: Combining an avocado and a chair into one image is evidence a model conceptually understands both, not that it memorised pictures of them. — tap to centre the map on it Combining an avocado and a chair into one image is evidence a model conceptually understands both, not that it memorised pictures of them. Last stated 3 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — 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: The Abstraction and Reasoning Corpus can measure human-like general fluid intelligence and enable fair comparisons between AI systems and humans. — tap to centre the map on it 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
At the centre
That IQ tests are unbiased across groups is highly implausible and empirically unsupported.
Holds Read this korrent →
A performance test without an explanation of its limiting factor should be treated as unanalyzed and possibly bogus.
Holds
Failing to adjust raw data for known measurement biases produces inaccurate results.
Holds
Debiasing has largely failed, and that is fine, because the biases identified are experience-built heuristics that only look like biases in hindsight.
Holds
The study's small group sizes and unequal distribution increase the risk of unreliable or non-reproducible findings.
Holds
AI learning to generate good conjectures will never show up as a benchmark being knocked down; it will show up as a shift in how mathematicians talk about the tools.
Holds
Coming into a project with a guess about what the data will show has, again and again, turned out to be wrong.
Holds
There is no single reasonable person in America, which makes the objective test for a true threat very hard to administer.
Holds
The real test of AGI is handing the system to a few hundred of the world's top experts and finding that none of them can locate an obvious flaw.
Holds
A company that deviates from open standards in its data exports without documenting the deviation creates an artificial vendor lock-in.
Holds
A design proposal should describe the feature as though it already existed — once as a tutorial and once as reference documentation.
Holds
A design system is only as good as its documentation.
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 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
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
Combining an avocado and a chair into one image is evidence a model conceptually understands both, not that it memorised pictures of them.
Holds
Passing ARC-AGI does not amount to achieving AGI: o3 still fails on some very easy tasks, indicating fundamental differences from human intelligence.
Holds
The Abstraction and Reasoning Corpus can measure human-like general fluid intelligence and enable fair comparisons between AI systems and humans.
Holds