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← The history of machine learning is of proclaimed barriers dissolving inside the big blob of compute.
17 connected korrents · 17 moments from 10 Dec 2015 to 24 Sept 2026.
Everything filed under scaling laws
scaling laws
Everything filed under neural networks
neural networks
Everything filed under LLMs
LLMs
Everything filed under benchmarks
benchmarks
Everything filed under reinforcement learning
reinforcement learning
Everything filed under mathematics
mathematics
Everything filed under AI and jobs
AI and jobs
Everything filed under physics
physics
Everything filed under innovation
innovation 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 history of machine learning is of proclaimed barriers dissolving inside the big blob of compute. The history of machine learning is of proclaimed barriers dissolving inside the big blob of compute. Last stated 8 months ago
13 Feb 2026
DA
Dario Amodei — holds since 2026-02-13 — 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 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: If companies replace large numbers of workers with machine learning, money and power will become more concentrated in the hands of capital. — tap to centre the map on it If companies replace large numbers of workers with machine learning, money and power will become more concentrated in the hands of capital. Last stated 6 months ago
14 Apr 2026
KK
Kyle Kingsbury — holds since 2026-04-14 — tap for who they are
Same subject: Models are enormous only because the internet is bad: most of that capacity does memory work, and a better dataset would need far less of it. — tap to centre the map on it Models are enormous only because the internet is bad: most of that capacity does memory work, and a better dataset would need far less of it. Last stated a year ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: Current AI struggles even to rediscover old laws of physics from data, and when it does, contamination from training is the first suspect. — tap to centre the map on it Current AI struggles even to rediscover old laws of physics from data, and when it does, contamination from training is the first suspect. Last stated a year ago
14 Jun 2025
TT
Terence Tao — holds since 2025-06-14 — tap for who they are
Same subject: Refusing to use or build AI tools will not stop AI development altogether, but it will slow it down, which is a good thing. — tap to centre the map on it Refusing to use or build AI tools will not stop AI development altogether, but it will slow it down, which is a good thing. Last stated 6 months ago
16 Apr 2026
KK
Kyle Kingsbury — holds since 2026-04-16 — tap for who they are
Same subject: There is probably an informal taboo on AI 'true neuralese' with no chain of thought, but no agreement on which component it covers. — tap to centre the map on it There is probably an informal taboo on AI 'true neuralese' with no chain of thought, but no agreement on which component it covers. Last stated 4 weeks ago
24 Sept 2026
SA
Scott Alexander — holds since 2026-09-24 — tap for who they are
Same subject: The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. — tap to centre the map on it The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. Last stated a year ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: The dangerous concentration of power in AI comes from who can afford the compute, not from who holds the model weights. — tap to centre the map on it The dangerous concentration of power in AI comes from who can afford the compute, not from who holds the model weights. Last stated 2 years ago
19 Jun 2024
AS
Aravind Srinivas — holds since 2024-06-19 — tap for who they are
Same subject: A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. — tap to centre the map on it A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score. Last stated 2 months ago
26 Aug 2026
HK
Henrik Karlsson — holds since 2026-08-26 — tap for who they are
Same subject: A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. — tap to centre the map on it A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel. Last stated 3 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. — tap to centre the map on it A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. Last stated 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. — tap to centre the map on it A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable. Last stated 11 years ago
10 Dec 2015
JS
Jian Sun — holds since 2015-12-10 — tap for who they are
KH
Kaiming He — holds since 2015-12-10 — tap for who they are
Same subject: A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. — tap to centre the map on it A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it. Last stated 9 years ago
9 Mar 2018
MC
Michael Carbin — holds since 2018-03-09 — tap for who they are
JF
Jonathan Frankle — holds since 2018-03-09 — tap for who they are
Same subject: A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. — tap to centre the map on it A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution. Last stated 9 years ago
12 Jun 2017
IP
Illia Polosukhin — holds since 2017-06-12 — tap for who they are
JU
Jakob Uszkoreit — holds since 2017-06-12 — tap for who they are
NP
Niki Parmar — holds since 2017-06-12 — tap for who they are
AV
Ashish Vaswani — holds since 2017-06-12 — tap for who they are
NS
Noam Shazeer — holds since 2017-06-12 — tap for who they are
AG
Aidan N. Gomez — holds since 2017-06-12 — tap for who they are
+2
2 more
Same subject: A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year. — tap to centre the map on it A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year. Last stated 2 months ago
3 Sept 2026
DR
Dax Raad — holds since 2026-09-03 — tap for who they are
Same subject: A carmaker's claim to be the safest is mostly an artefact of comparing a new car against a fleet average twelve years old. — tap to centre the map on it A carmaker's claim to be the safest is mostly an artefact of comparing a new car against a fleet average twelve years old. Last stated 3 years ago
19 Dec 2023
PK
Philip Koopman — holds since 2023-12-19 — tap for who they are
Same subject: A company should define what great looks like for its own role rather than borrow another company's definition. — tap to centre the map on it A company should define what great looks like for its own role rather than borrow another company's definition. Last stated 2 months ago
9 Aug 2026
AW
Adam Ward — holds since 2026-08-09 — tap for who they are
At the centre
The history of machine learning is of proclaimed barriers dissolving inside the big blob of compute.
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
If companies replace large numbers of workers with machine learning, money and power will become more concentrated in the hands of capital.
Holds
Models are enormous only because the internet is bad: most of that capacity does memory work, and a better dataset would need far less of it.
Holds
Current AI struggles even to rediscover old laws of physics from data, and when it does, contamination from training is the first suspect.
Holds
Refusing to use or build AI tools will not stop AI development altogether, but it will slow it down, which is a good thing.
Holds
There is probably an informal taboo on AI 'true neuralese' with no chain of thought, but no agreement on which component it covers.
Holds
The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out.
Holds
The dangerous concentration of power in AI comes from who can afford the compute, not from who holds the model weights.
Holds
A feedback loop that reinforces a behavior pulls it toward whatever improves the loop's own score.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
A layer should learn a residual with reference to its own input rather than an unreferenced function, which is what makes great depth trainable.
Holds
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Holds
A transduction model can rely entirely on self-attention for input and output representations without sequence-aligned RNNs or convolution.
Holds
A benchmark that ranks Claude Code last while it stays first in use is measuring the wrong thing, and has been for a year.
Holds
A carmaker's claim to be the safest is mostly an artefact of comparing a new car against a fleet average twelve years old.
Holds
A company should define what great looks like for its own role rather than borrow another company's definition.
Holds