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← Studying history for the cause-and-effect mechanics of things that…
17 connected korrents · 15 moments from 4 Jan 2017 to 15 Jul 2026.
Everything filed under AI and science
AI and science
Everything filed under mathematics
mathematics
Everything filed under robotics
robotics
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LLMs
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infrastructure 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: Studying history for the cause-and-effect mechanics of things that happen over and over again allows the rules to be converted into algorithms. Studying history for the cause-and-effect mechanics of things that happen over and over again allows the rules to be converted into algorithms. Last stated 4 years ago
27 Sept 2022
RD
Ray Dalio — holds since 2022-09-27 — tap for who they are
Same subject: History teaches complexity rather than prediction: it can explain the mechanisms of change without licensing any claim about what comes next. — tap to centre the map on it History teaches complexity rather than prediction: it can explain the mechanisms of change without licensing any claim about what comes next. Last stated 10 years ago
4 Jan 2017
AP
Ada Palmer — holds since 2017-01-04 — tap for who they are
Same subject: Reading further back in history shows that what feels new and unique to the present era is usually a very old pattern. — tap to centre the map on it Reading further back in history shows that what feels new and unique to the present era is usually a very old pattern. Last stated 5 years ago
25 Jan 2022
MA
Marc Andreessen — holds since 2022-01-25 — tap for who they are
Same subject: Studying what arbitrary computational rules do is a distinct science, separate from computer science and mathematics. — tap to centre the map on it Studying what arbitrary computational rules do is a distinct science, separate from computer science and mathematics. Last stated 9 months ago
12 Jan 2026
SW
Stephen Wolfram — holds since 2026-01-12 — tap for who they are
Same subject: The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds. — tap to centre the map on it The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds. Last stated 2 years ago
3 Feb 2025
DP
Dylan Patel — holds since 2025-02-03 — tap for who they are
Same subject: An agent repeats its own history: whatever it did on the last change — ran the tests or skipped them — is what it will do on the next one, because it is predicting the next message in the conversation. — tap to centre the map on it An agent repeats its own history: whatever it did on the last change — ran the tests or skipped them — is what it will do on the next one, because it is predicting the next message in the conversation. Last stated 3 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: Knowledge of cause and effect is never reached by reasoning a priori; it comes entirely from experience of things constantly conjoined — tap to centre the map on it Knowledge of cause and effect is never reached by reasoning a priori; it comes entirely from experience of things constantly conjoined
DH
David Hume — holds since c. 1748 — tap for who they are
Same subject: The hypotheses AI 'co-scientist' systems are producing are ones the literature already contains, and the publicity around them does not say so. — tap to centre the map on it The hypotheses AI 'co-scientist' systems are producing are ones the literature already contains, and the publicity around them does not say so. Last stated 4 months ago
27 May 2026
DL
Derek Lowe — holds since 2026-05-27 — tap for who they are
Same subject: Making a change within a complex system tends to produce unforeseen and unintended consequences. — tap to centre the map on it Making a change within a complex system tends to produce unforeseen and unintended consequences. Last stated 4 years ago
30 May 2022
SC
Steve Coast — holds since 2022-05-30 — 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: Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. — tap to centre the map on it Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics. Last stated 5 years ago
15 Jul 2021
DH
Demis Hassabis — holds since 2021-07-15 — tap for who they are
Same subject: AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. — tap to centre the map on it AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points. Last stated 11 months ago
23 Nov 2025
EH
Elliot Hershberg — holds since 2025-11-23 — tap for who they are
Same subject: AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work. — tap to centre the map on it AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work. Last stated 2 years ago
19 Sept 2024
DL
Derek Lowe — holds since 2024-09-19 — tap for who they are
Same subject: AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. — tap to centre the map on it AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape. — tap to centre the map on it AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape. Last stated 7 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: AI in chess and mathematics does not explain anything; it says which position is better, and humans build the theory from that. — tap to centre the map on it AI in chess and mathematics does not explain anything; it says which position is better, and humans build the theory from that. Last stated a year ago
14 Jun 2025
LF
Lex Fridman — holds since 2025-06-14 — tap for who they are
At the centre
Studying history for the cause-and-effect mechanics of things that happen over and over again allows the rules to be converted into algorithms.
Holds Read this korrent →
History teaches complexity rather than prediction: it can explain the mechanisms of change without licensing any claim about what comes next.
Holds
Reading further back in history shows that what feels new and unique to the present era is usually a very old pattern.
Holds
Studying what arbitrary computational rules do is a distinct science, separate from computer science and mathematics.
Holds
The aha moment for reasoning models will come from computer use and robotics rather than from scientific discovery, because those are the infinitely verifiable playgrounds.
Holds
An agent repeats its own history: whatever it did on the last change — ran the tests or skipped them — is what it will do on the next one, because it is predicting the next message in the conversation.
Holds
Knowledge of cause and effect is never reached by reasoning a priori; it comes entirely from experience of things constantly conjoined
Last stated c. 1748
Holds
The hypotheses AI 'co-scientist' systems are producing are ones the literature already contains, and the publicity around them does not say so.
Holds
Making a change within a complex system tends to produce unforeseen and unintended consequences.
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
Accurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics.
Holds
AI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points.
Holds
AI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work.
Holds
AI could compete with human mathematicians once it acquires a mathematical sense of smell: knowing which way of splitting a problem makes it easier rather than harder.
Holds
AI excels at breadth and human experts at depth, which makes them complementary and makes today's depth-focused way of doing science the wrong shape.
Holds
AI in chess and mathematics does not explain anything; it says which position is better, and humans build the theory from that.
Holds