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← No AI system today reduces either of the two risks that sink most…
17 connected korrents · 15 moments from 15 Jul 2021 to 22 Sept 2026.
Everything filed under AI and science
AI and science
Everything filed under mathematics
mathematics
Everything filed under protein structure prediction
protein structure prediction
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under AI alignment
AI alignment
Everything filed under design
design 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: No AI system today reduces either of the two risks that sink most drugs in the clinic: picking the wrong target, and human toxicity. No AI system today reduces either of the two risks that sink most drugs in the clinic: picking the wrong target, and human toxicity. Last stated 3 years ago
25 May 2023
DL
Derek Lowe — holds since 2023-05-25 — 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: There are few clear-cut cases in biopharma today where buying an AI system is obviously worth it. — tap to centre the map on it There are few clear-cut cases in biopharma today where buying an AI system is obviously worth it. Last stated 2 years ago
14 Oct 2024
DL
Derek Lowe — holds since 2024-10-14 — 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: There is no route to AI alignment or safety that goes around reliability: a system that cannot reliably follow a known algorithm cannot be made safe. — tap to centre the map on it There is no route to AI alignment or safety that goes around reliability: a system that cannot reliably follow a known algorithm cannot be made safe. Last stated a year ago
7 Jun 2025
GM
Gary Marcus — holds since 2025-06-07 — 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: 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: 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. — tap to centre the map on it 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: We do not control neural networks well enough to guarantee an AI will not harm humans, so we should be careful about what capabilities we give them. — tap to centre the map on it We do not control neural networks well enough to guarantee an AI will not harm humans, so we should be careful about what capabilities we give them. Last stated 4 years ago
17 Feb 2023
AR
Armin Ronacher — holds since 2023-02-17 — 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
No AI system today reduces either of the two risks that sink most drugs in the clinic: picking the wrong target, and human toxicity.
Holds Read this korrent →
We have neither the data nor the insight to use machine learning to pick drug targets that succeed in the clinic more often.
Holds
There are few clear-cut cases in biopharma today where buying an AI system is obviously worth it.
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
There is no route to AI alignment or safety that goes around reliability: a system that cannot reliably follow a known algorithm cannot be made safe.
Holds
A compound's AI origin story is not by itself a reason to expect it to do better in the clinic.
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
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
We do not control neural networks well enough to guarantee an AI will not harm humans, so we should be careful about what capabilities we give them.
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