Tap a claim on the ring to put it at the centre.
← Understanding a machine learning model and understanding the data it operates on are inseparable tasks.
17 connected korrents · 17 moments from 16 Jan 2015 to 21 Oct 2026.
Everything filed under AI and science
AI and science
Everything filed under mathematics
mathematics
Everything filed under LLMs
LLMs
Everything filed under protein structure prediction
protein structure prediction
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under optimism
optimism
Everything filed under AI agents
AI agents 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: Understanding a machine learning model and understanding the data it operates on are inseparable tasks. Understanding a machine learning model and understanding the data it operates on are inseparable tasks. Last stated 12 years ago
16 Jan 2015
CO
Christopher Olah — holds since 2015-01-16 — 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 3 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — 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: The subdiscipline of Explainable AI may eventually solve the problem of why machine learning systems make incorrect decisions. — tap to centre the map on it The subdiscipline of Explainable AI may eventually solve the problem of why machine learning systems make incorrect decisions. Last stated 4 years ago
by 11 Nov 2022
AH
Adrian Holovaty — holds since 2022-11-11 — tap for who they are
Same subject: AI models can produce competent-looking software but cannot judge which problems are worth solving or which solutions are right. — tap to centre the map on it AI models can produce competent-looking software but cannot judge which problems are worth solving or which solutions are right. Last stated 5 months ago
26 May 2026
CB
Carlos Alexandro Becker — holds since 2026-05-26 — tap for who they are
Same subject: The way to find what a model can do is to hand it tasks slightly harder than you believe it can handle. — tap to centre the map on it The way to find what a model can do is to hand it tasks slightly harder than you believe it can handle. Last stated 3 months ago
27 Jul 2026
BC
Boris Cherny — holds since 2026-07-27 — tap for who they are
Same subject: High-quality data collection depends more on careful human execution than on machine learning techniques alone. — tap to centre the map on it 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: Eventually AI models, perhaps millions working together, may be able to do the work of whole human organizations. — tap to centre the map on it Eventually AI models, perhaps millions working together, may be able to do the work of whole human organizations. Last stated a year ago
16 Jun 2025
JK
Jared Kaplan — holds since 2025-06-16 — tap for who they are
Same subject: Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. — tap to centre the map on it Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. Last stated 3 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — 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 3 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: Deep learning has effectively solved the problem of predicting a protein's 3D structure from its sequence. — tap to centre the map on it Deep learning has effectively solved the problem of predicting a protein's 3D structure from its sequence. Last stated a year ago
29 Jun 2025
EH
Elliot Hershberg — holds since 2025-06-29 — tap for who they are
Same subject: A persistently rising dividend yield eventually forces capital appreciation as the market rerates the stock. — tap to centre the map on it A persistently rising dividend yield eventually forces capital appreciation as the market rerates the stock. Last stated 8 months ago
3 Mar 2026
SB
Sanjay Bakshi — holds since 2026-03-03 — tap for who they are
Same subject: A proof and an explanation are different things, and a theorem can stay an unsolved expository problem long after it is proved. — tap to centre the map on it A proof and an explanation are different things, and a theorem can stay an unsolved expository problem long after it is proved. Last stated 4 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: A stream of AI-written papers with any error rate at all becomes insufferable, because finding the error costs more than the paper is worth even at ninety-nine percent. — tap to centre the map on it A stream of AI-written papers with any error rate at all becomes insufferable, because finding the error costs more than the paper is worth even at ninety-nine percent. Last stated 4 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute. — tap to centre the map on it A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute. Last stated a month ago
22 Sept 2026
TC
Tyler Cowen — holds since 2026-09-22 — tap for who they are
Same subject: A sealed machine that redesigns itself will eventually redesign everything that touches its seal. — tap to centre the map on it A sealed machine that redesigns itself will eventually redesign everything that touches its seal. Last stated 2 weeks ago
21 Oct 2026
BM
Bruno Maçães — holds since 2026-10-21 — tap for who they are
Same subject: AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started. — tap to centre the map on it AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started. Last stated 3 months ago
3 Aug 2026
CP
Chamath Palihapitiya — holds since 2026-08-03 — tap for who they are
At the centre
Understanding a machine learning model and understanding the data it operates on are inseparable tasks.
Holds Read this korrent →
Machine learning is a shallow field compared with mathematics: even its most important ideas can be explained in a couple of minutes.
Holds
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
The subdiscipline of Explainable AI may eventually solve the problem of why machine learning systems make incorrect decisions.
Holds
AI models can produce competent-looking software but cannot judge which problems are worth solving or which solutions are right.
Holds
The way to find what a model can do is to hand it tasks slightly harder than you believe it can handle.
Holds
High-quality data collection depends more on careful human execution than on machine learning techniques alone.
Holds
Eventually AI models, perhaps millions working together, may be able to do the work of whole human organizations.
Holds
Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful.
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
Deep learning has effectively solved the problem of predicting a protein's 3D structure from its sequence.
Holds
A persistently rising dividend yield eventually forces capital appreciation as the market rerates the stock.
Holds
A proof and an explanation are different things, and a theorem can stay an unsolved expository problem long after it is proved.
Holds
A stream of AI-written papers with any error rate at all becomes insufferable, because finding the error costs more than the paper is worth even at ninety-nine percent.
Holds
A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute.
Holds
A sealed machine that redesigns itself will eventually redesign everything that touches its seal.
Holds
AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started.
Holds