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← Multilayer neural networks trained with backpropagation are the best example of successful gradient-based learning.
17 connected korrents · 15 moments on record from 1 Nov 1997 to 8 Sept 2026.
Everything filed under neural networks
neural networks
Everything filed under measuring intelligence
measuring intelligence
Everything filed under pruning
pruning
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: Multilayer neural networks trained with backpropagation are the best example of successful gradient-based learning. Multilayer neural networks trained with backpropagation are the best example of successful gradient-based learning. Last stated 29 years ago
1 Jan 1998
YB
Yoshua Bengio — holds since 1998-01-01 — 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 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source. — tap to centre the map on it Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source. Last stated 3 years ago
24 Jun 2023
YB
Yoshua Bengio — holds since 2023-06-24 — tap for who they are
Same subject: An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job. — tap to centre the map on it An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Anything nature shaped can be learned efficiently by a classical neural network, because evolutionary processes leave structure behind. — tap to centre the map on it Anything nature shaped can be learned efficiently by a classical neural network, because evolutionary processes leave structure behind. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: Convolutional neural networks are specifically designed to handle variability in two-dimensional shapes. — tap to centre the map on it Convolutional neural networks are specifically designed to handle variability in two-dimensional shapes. Last stated 29 years ago
1 Jan 1998
YB
Yoshua Bengio — holds since 1998-01-01 — tap for who they are
Same subject: Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. — tap to centre the map on it Depth is not free: past a point, deeper neural networks are harder to train rather than simply better. 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: Large language models are grown rather than built, and nobody really knows how the resulting working AI functions. — tap to centre the map on it Large language models are grown rather than built, and nobody really knows how the resulting working AI functions. Last stated 2 days ago
8 Sept 2026
SA
Scott Alexander — holds since 2026-09-08 — tap for who they are
Same subject: LSTM can solve complex artificial long-time-lag tasks that prior recurrent network algorithms never solved. — tap to centre the map on it LSTM can solve complex artificial long-time-lag tasks that prior recurrent network algorithms never solved. Last stated 29 years ago
1 Nov 1997
JS
Jürgen Schmidhuber — holds since 1997-11-01 — tap for who they are
SH
Sepp Hochreiter — holds since 1997-11-01 — tap for who they are
Same subject: If intelligence is the process of acquiring skills, no single task demonstrates intelligence unless it is a meta-task of skill-acquisition across many tasks. — tap to centre the map on it If intelligence is the process of acquiring skills, no single task demonstrates intelligence unless it is a meta-task of skill-acquisition across many tasks. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — tap for who they are
Same subject: Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence. — tap to centre the map on it Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence. Last stated 2 years ago
3 Feb 2025
NL
Nathan Lambert — holds since 2025-02-03 — tap for who they are
Same subject: Solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience. — tap to centre the map on it Solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — 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 winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work. — tap to centre the map on it A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work. 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: Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow. — tap to centre the map on it Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow. Last stated 9 months ago
8 Dec 2025
CH
Casey Handmer — holds since 2023-10-11 — tap for who they are
CH
Casey Handmer — holds since 2025-12-08 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget. — tap to centre the map on it A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
At the centre
Multilayer neural networks trained with backpropagation are the best example of successful gradient-based learning.
Holds Read this korrent →
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Access to large-scale generalist AI systems that could be weaponized should be limited, meaning their code and neural network parameters should not be released open-source.
Holds
An agent can understand a maximally simplified explanation and still be unable to come up with it — that gap is what is left of the expert's job.
Holds
Anything nature shaped can be learned efficiently by a classical neural network, because evolutionary processes leave structure behind.
Holds DH Demis Hassabis
Convolutional neural networks are specifically designed to handle variability in two-dimensional shapes.
Holds
Depth is not free: past a point, deeper neural networks are harder to train rather than simply better.
Holds JS Jian SunKH Kaiming He
Large language models are grown rather than built, and nobody really knows how the resulting working AI functions.
Holds SA Scott Alexander
LSTM can solve complex artificial long-time-lag tasks that prior recurrent network algorithms never solved.
Holds JS Jürgen SchmidhuberSH Sepp Hochreiter
If intelligence is the process of acquiring skills, no single task demonstrates intelligence unless it is a meta-task of skill-acquisition across many tasks.
Holds
Language models are already a form of AGI; what the labs are chasing is a further step, not the arrival of general intelligence.
Holds NL Nathan Lambert
Solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience.
Holds
A small enough winning ticket learns faster than the network it was cut out of, and ends up more accurate than it.
Holds MC Michael CarbinJF Jonathan Frankle
A winning ticket wins on its initial weights: the connections it keeps started at values that happen to make training work.
Holds MC Michael CarbinJF Jonathan Frankle
Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow.
Holds CH Casey Handmer
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds DP Dylan Patel
A model's capability is now a function of how much money you spend on it, so asking what a model can do means nothing until you name a budget.
Holds NB Noam Brown