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← High-quality data collection depends more on careful human execution than on machine learning techniques alone.
17 connected korrents · 16 moments on record from 5 Nov 2019 to 15 Sept 2026.
Everything filed under measuring intelligence
measuring intelligence
Everything filed under protein structure prediction
protein structure prediction
Everything filed under scaling laws
scaling laws
Everything filed under AI and science
AI and science
Everything filed under LLMs
LLMs
Everything filed under mathematics
mathematics
Everything filed under robotics
robotics 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: High-quality data collection depends more on careful human execution than on machine learning techniques alone. High-quality data collection depends more on careful human execution than on machine learning techniques alone. Last stated 3 years ago
5 Feb 2024
LW
Lilian Weng — holds since 2024-02-05 — tap for who they are
Same subject: How much machine learning can help a biological problem is set by the quality of the data around that problem, not by the algorithms. — tap to centre the map on it How much machine learning can help a biological problem is set by the quality of the data around that problem, not by the algorithms. Last stated 2 years ago
19 Sept 2024
DL
Derek Lowe — holds since 2024-09-19 — tap for who they are
Same subject: High-quality video data, especially paired with text, is harder to collect at scale than image or text data. — tap to centre the map on it High-quality video data, especially paired with text, is harder to collect at scale than image or text data. Last stated 2 years ago
12 Apr 2024
LW
Lilian Weng — holds since 2024-04-12 — tap for who they are
Same subject: Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes. — tap to centre the map on it Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes. Last stated a month ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: We have neither the data nor the insight to use machine learning to pick drug targets that succeed in the clinic more often. — tap to centre the map on it We have neither the data nor the insight to use machine learning to pick drug targets that succeed in the clinic more often. Last stated 3 years ago
25 May 2023
DL
Derek Lowe — holds since 2023-05-25 — tap for who they are
Same subject: Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data. — tap to centre the map on it Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data. Last stated a month ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary — tap to centre the map on it As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary Last stated 3 years ago
16 Jan 2024
CH
Chip Huyen — holds since 2024-01-16 — tap for who they are
Same subject: The internet is already past peak truth, and picking out training data worth learning from will only get harder. — tap to centre the map on it The internet is already past peak truth, and picking out training data worth learning from will only get harder. Last stated 4 months ago
13 May 2026
AH
Anders Hejlsberg — holds since 2026-05-13 — 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: 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
Same subject: A narrow, highly accurate model can beat a general one inside its own domain, as AlphaFold did, and materials science and chip design are next. — tap to centre the map on it A narrow, highly accurate model can beat a general one inside its own domain, as AlphaFold did, and materials science and chip design are next. Last stated 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: Contemporary physical and evolutionary structure-prediction approaches fall far short of experimental accuracy without a close experimental homologue, limiting biological utility. — tap to centre the map on it Contemporary physical and evolutionary structure-prediction approaches fall far short of experimental accuracy without a close experimental homologue, limiting biological utility. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: Existing protein structure prediction methods fall far short of atomic accuracy when no homologous structure is available. — tap to centre the map on it Existing protein structure prediction methods fall far short of atomic accuracy when no homologous structure is available. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
At the centre
High-quality data collection depends more on careful human execution than on machine learning techniques alone.
Holds Read this korrent →
How much machine learning can help a biological problem is set by the quality of the data around that problem, not by the algorithms.
Holds
High-quality video data, especially paired with text, is harder to collect at scale than image or text data.
Holds
Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes.
Holds
We have neither the data nor the insight to use machine learning to pick drug targets that succeed in the clinic more often.
Holds
Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data.
Holds
As models get better at following instructions, techniques like finetuning and constrained sampling for structured outputs will become less necessary
Holds
The internet is already past peak truth, and picking out training data worth learning from will only get harder.
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
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
A narrow, highly accurate model can beat a general one inside its own domain, as AlphaFold did, and materials science and chip design are next.
Holds
Contemporary physical and evolutionary structure-prediction approaches fall far short of experimental accuracy without a close experimental homologue, limiting biological utility.
Holds
Existing protein structure prediction methods fall far short of atomic accuracy when no homologous structure is available.
Holds