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← Purely digital progress runs out: to keep learning, the machines will…
17 connected korrents · 14 moments from 12 Sept 2020 to 21 Aug 2026.
Everything filed under AI and science
AI and science
Everything filed under education
education
Everything filed under robotics
robotics
Everything filed under mathematics
mathematics
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: Purely digital progress runs out: to keep learning, the machines will have to run experiments and ask the universe questions. Purely digital progress runs out: to keep learning, the machines will have to run experiments and ask the universe questions. Last stated 7 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: The useful question about robot data is not how much is needed to finish but how much is needed to start a self-sustaining flywheel. — tap to centre the map on it The useful question about robot data is not how much is needed to finish but how much is needed to start a self-sustaining flywheel. Last stated a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: What AI still cannot do in mathematics is build cumulatively on partial progress; it is trial, error and brute force rather than an idea improving through use. — tap to centre the map on it What AI still cannot do in mathematics is build cumulatively on partial progress; it is trial, error and brute force rather than an idea improving through use. Last stated 7 months ago
20 Mar 2026
TT
Terence Tao — holds since 2026-03-20 — tap for who they are
Same subject: Offloading thinking to AI tools does not stop a person from learning; it can push them to learn more. — tap to centre the map on it Offloading thinking to AI tools does not stop a person from learning; it can push them to learn more. Last stated 2 months ago
21 Aug 2026
MW
Matt Webb — holds since 2026-08-21 — tap for who they are
Same subject: Progress is not driven mainly by material resources, so running out of them is not what would stop it. — tap to centre the map on it Progress is not driven mainly by material resources, so running out of them is not what would stop it. Last stated 6 years ago
12 Sept 2020
JC
Jason Crawford — holds since 2020-09-12 — tap for who they are
Same subject: You only fail if you stop; iteration that keeps going is not failure, it is learning. — tap to centre the map on it You only fail if you stop; iteration that keeps going is not failure, it is learning. Last stated 5 months ago
7 Jun 2026
TF
Tony Fadell — holds since 2026-06-07 — tap for who they are
Same subject: You only retain what you were pushed to your limit on: if learning feels like coasting, you are not learning. — tap to centre the map on it You only retain what you were pushed to your limit on: if learning feels like coasting, you are not learning. Last stated 2 years ago
25 May 2024
CR
Charan Ranganath — holds since 2024-05-25 — tap for who they are
Same subject: AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it. — tap to centre the map on it AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: New technologies should be used to explore more broadly and go deeper, not to do more things, and distilling and refining to the essence is how something great is made. — tap to centre the map on it New technologies should be used to explore more broadly and go deeper, not to do more things, and distilling and refining to the essence is how something great is made. Last stated 7 months ago
12 Mar 2026
JP
Josh Puckett — holds since 2026-03-12 — 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
Purely digital progress runs out: to keep learning, the machines will have to run experiments and ask the universe questions.
Holds Read this korrent →
The useful question about robot data is not how much is needed to finish but how much is needed to start a self-sustaining flywheel.
Holds
What AI still cannot do in mathematics is build cumulatively on partial progress; it is trial, error and brute force rather than an idea improving through use.
Holds
Offloading thinking to AI tools does not stop a person from learning; it can push them to learn more.
Holds
Progress is not driven mainly by material resources, so running out of them is not what would stop it.
Holds
You only fail if you stop; iteration that keeps going is not failure, it is learning.
Holds
You only retain what you were pushed to your limit on: if learning feels like coasting, you are not learning.
Holds
AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it.
Holds
New technologies should be used to explore more broadly and go deeper, not to do more things, and distilling and refining to the essence is how something great is made.
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