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← A personalized classifier needs several hundred labeled examples before its predictions become reliable.
17 connected korrents · 17 moments on record from 5 Nov 2019 to 15 Sept 2026.
Everything filed under AI and science
AI and science
Everything filed under scaling laws
scaling laws
Everything filed under LLMs
LLMs
Everything filed under benchmarks
benchmarks
Everything filed under measuring intelligence
measuring intelligence
Everything filed under neural networks
neural networks
Everything filed under AI and human skill
AI and human skill 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: A personalized classifier needs several hundred labeled examples before its predictions become reliable. A personalized classifier needs several hundred labeled examples before its predictions become reliable. Last stated 8 months ago
23 Jan 2026
AW
Adam Wiggins — holds since 2026-01-23 — 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 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: Full fine-tuning of large language models has become too costly, making parameter-efficient fine-tuning methods necessary. — tap to centre the map on it Full fine-tuning of large language models has become too costly, making parameter-efficient fine-tuning methods necessary. Last stated 3 years ago
5 Dec 2023
SR
Sebastian Ruder — holds since 2023-12-05 — tap for who they are
Same subject: Using a neural network is likely to become easier than designing a custom-tuned prediction algorithm for many tasks. — tap to centre the map on it Using a neural network is likely to become easier than designing a custom-tuned prediction algorithm for many tasks. Last stated a year ago
4 Jul 2025
NE
Nelson Elhage — holds since 2025-07-04 — tap for who they are
Same subject: When pre-training data is limited, it is better to train a smaller model for multiple epochs than to train a larger model on unique data once. — tap to centre the map on it When pre-training data is limited, it is better to train a smaller model for multiple epochs than to train a larger model on unique data once. Last stated 3 years ago
1 Dec 2023
SR
Sebastian Ruder — holds since 2023-12-01 — tap for who they are
Same subject: What sits in a model's context is far clearer to it than its training data, which is trillions of tokens stirred into a soup of parameters. — tap to centre the map on it What sits in a model's context is far clearer to it than its training data, which is trillions of tokens stirred into a soup of parameters. Last stated 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: If this much effort goes into training the model, you had better be willing to keep training your own. — tap to centre the map on it If this much effort goes into training the model, you had better be willing to keep training your own. Last stated 4 months ago
3 Jun 2026
KH
Kelsey Hightower — holds since 2026-06-03 — tap for who they are
Same subject: Long-context evaluation benchmarks are likely contaminated by model training data, making them unreliable as a sole evaluation method — tap to centre the map on it Long-context evaluation benchmarks are likely contaminated by model training data, making them unreliable as a sole evaluation method Last stated a year ago
22 Jun 2025
EY
Eugene Yan — holds since 2025-06-22 — tap for who they are
Same subject: Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse. — tap to centre the map on it Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse. Last stated 12 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. — tap to centre the map on it A compound's AI origin story is not by itself a reason to expect it to do better in the clinic. Last stated 2 years ago
13 May 2024
DL
Derek Lowe — holds since 2024-05-13 — tap for who they are
Same subject: A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. — tap to centre the map on it A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck. Last stated 2 years ago
17 Sept 2024
AT
Alex Telford — holds since 2024-09-17 — tap for who they are
Same subject: A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. — tap to centre the map on it A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: A true artificial general intelligence cannot exist without being recognized as a moral subject. — tap to centre the map on it A true artificial general intelligence cannot exist without being recognized as a moral subject. Last stated a year ago
10 Jun 2025
SH
Samuel Hammond — holds since 2025-06-10 — tap for who they are
Same subject: A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. — tap to centre the map on it A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. Last stated 6 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. — tap to centre the map on it Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — 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 10 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 3 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
At the centre
A personalized classifier needs several hundred labeled examples before its predictions become reliable.
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
Full fine-tuning of large language models has become too costly, making parameter-efficient fine-tuning methods necessary.
Holds
Using a neural network is likely to become easier than designing a custom-tuned prediction algorithm for many tasks.
Holds
When pre-training data is limited, it is better to train a smaller model for multiple epochs than to train a larger model on unique data once.
Holds
What sits in a model's context is far clearer to it than its training data, which is trillions of tokens stirred into a soup of parameters.
Holds
If this much effort goes into training the model, you had better be willing to keep training your own.
Holds
Long-context evaluation benchmarks are likely contaminated by model training data, making them unreliable as a sole evaluation method
Holds
Large language models start in exactly the wrong place, because they try to get by without a goal and therefore without any sense of better or worse.
Holds
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
Holds
A computational discovery platform does not make a biotech fast, because the thousand other steps of drug development are still the bottleneck.
Holds
A computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.
Holds
A true artificial general intelligence cannot exist without being recognized as a moral subject.
Holds
A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model.
Holds
Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect.
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 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