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← The human data an AI model would need to predict trial outcomes can…
17 connected korrents · 12 moments from 15 Jul 2021 to 22 Sept 2026.
Everything filed under AI and science
AI and science
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under mathematics
mathematics
Everything filed under protein structure prediction
protein structure prediction
Everything filed under design
design
Everything filed under robotics
robotics
Everything filed under scaling laws
scaling laws 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: The human data an AI model would need to predict trial outcomes can only come from trials in people, so the loop runs no faster than the trials themselves. The human data an AI model would need to predict trial outcomes can only come from trials in people, so the loop runs no faster than the trials themselves. Last stated 8 months ago
28 Feb 2026
RT
Ruxandra Teslo — holds since 2026-02-28 — tap for who they are
Same subject: There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate. — tap to centre the map on it There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate. Last stated 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: The AI research cycle from idea to experiment will likely soon be 10x faster, but that may not make researchers 10x more productive in the field's net understanding. — tap to centre the map on it The AI research cycle from idea to experiment will likely soon be 10x faster, but that may not make researchers 10x more productive in the field's net understanding. Last stated a month ago
22 Sept 2026
NL
Nathan Lambert — holds since 2026-09-22 — tap for who they are
Same subject: Better drug candidates do not shorten clinical trials, because a trial's duration is set by operational and regulatory friction rather than by how good the drug is. — tap to centre the map on it Better drug candidates do not shorten clinical trials, because a trial's duration is set by operational and regulatory friction rather than by how good the drug is. Last stated 8 months ago
28 Feb 2026
RT
Ruxandra Teslo — holds since 2026-02-28 — tap for who they are
Same subject: Deep learning is not yet ready for the task that would actually add value in drug development: predicting safety and efficacy in humans. — tap to centre the map on it Deep learning is not yet ready for the task that would actually add value in drug development: predicting safety and efficacy in humans. Last stated 3 years ago
23 Dec 2023
AT
Alex Telford — holds since 2023-12-23 — 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: 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 3 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — 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 2 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: Scaling up spending on expert human data has not been a major driver of progress in AI research. — tap to centre the map on it Scaling up spending on expert human data has not been a major driver of progress in AI research. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — 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: 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
Same subject: AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033. — tap to centre the map on it AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
At the centre
The human data an AI model would need to predict trial outcomes can only come from trials in people, so the loop runs no faster than the trials themselves.
Holds Read this korrent →
There is no real impediment to models running their own research loop and improving themselves at a far more rapid rate.
Holds
The AI research cycle from idea to experiment will likely soon be 10x faster, but that may not make researchers 10x more productive in the field's net understanding.
Holds
Better drug candidates do not shorten clinical trials, because a trial's duration is set by operational and regulatory friction rather than by how good the drug is.
Holds
Deep learning is not yet ready for the task that would actually add value in drug development: predicting safety and efficacy in humans.
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
Learned approximations of slow simulators change what science is possible, by turning a six-month screening run into something you do over lunch.
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
Scaling up spending on expert human data has not been a major driver of progress in AI research.
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
AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started.
Holds
AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033.
Holds