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← Growing evidence suggests the brain performs a form of backpropagation-like learning.
17 connected korrents · 19 moments from 1 Nov 1997 to 18 Sept 2026.
Everything filed under neural networks
neural networks
Everything filed under LLMs
LLMs
Everything filed under computer vision
computer vision
Everything filed under pruning
pruning
Everything filed under AI and science
AI and science 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: Growing evidence suggests the brain performs a form of backpropagation-like learning. Growing evidence suggests the brain performs a form of backpropagation-like learning. Last stated 6 years ago
13 Mar 2021
GW
Gwern — holds since 2021-03-13 — tap for who they are
Same subject: The most fertile way to study neuroscience today is to work on AI models rather than on brains. — tap to centre the map on it The most fertile way to study neuroscience today is to work on AI models rather than on brains. Last stated 2 months ago
20 Aug 2026
KK
Kevin Kelly — holds since 2026-08-17 — tap for who they are
MH
Max Hodak — holds since 2026-08-20 — 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: Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it. — tap to centre the map on it Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it. Last stated a year ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Recurrent backpropagation learns slowly over long intervals mainly because error backflow decays. — tap to centre the map on it Recurrent backpropagation learns slowly over long intervals mainly because error backflow decays. 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: The brain is doing classical computation, not quantum, so everything it does can in principle be mimicked by a classical computer. — tap to centre the map on it The brain is doing classical computation, not quantum, so everything it does can in principle be mimicked by a classical computer. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: The adult brain's stock of neurons is essentially fixed, and that is no limit on learning, because plasticity is the changing of connections between the cells you already have. — tap to centre the map on it The adult brain's stock of neurons is essentially fixed, and that is no limit on learning, because plasticity is the changing of connections between the cells you already have. Last stated 3 months ago
20 Jul 2026
TW
Tommy Wood — holds since 2026-07-20 — tap for who they are
Same subject: Multilayer neural networks trained with backpropagation are the best example of successful gradient-based learning. — tap to centre the map on it 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: Deep learning will be understood very differently in thirty years than it is today. — tap to centre the map on it Deep learning will be understood very differently in thirty years than it is today. Last stated 11 years ago
3 Sept 2015
CO
Christopher Olah — holds since 2015-09-03 — 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 10 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: Most agent harnesses need some pruning during context compaction anyway for cost reasons, which Jev might help with. — tap to centre the map on it Most agent harnesses need some pruning during context compaction anyway for cost reasons, which Jev might help with. Last stated a month ago
18 Sept 2026
AR
Armin Ronacher — holds since 2026-09-18 — tap for who they are
Same subject: Neuroplasticity is as much the removal of connections as the growth of new ones, so pruning is a form of learning rather than a form of decay. — tap to centre the map on it Neuroplasticity is as much the removal of connections as the growth of new ones, so pruning is a form of learning rather than a form of decay. Last stated 3 months ago
20 Jul 2026
TW
Tommy Wood — holds since 2026-07-20 — tap for who they are
Same subject: As reasoning models and agents retain more tokens for longer, KV-cache size and attention cost become the main constraint on scaling context. — tap to centre the map on it As reasoning models and agents retain more tokens for longer, KV-cache size and attention cost become the main constraint on scaling context. Last stated 5 months ago
16 May 2026
SR
Sebastian Raschka — holds since 2026-05-16 — tap for who they are
Same subject: Context engineering has stayed relevant for a year because it is grounded in how transformer attention works, and it will matter to anyone building on AI until post-transformer models arrive. — tap to centre the map on it Context engineering has stayed relevant for a year because it is grounded in how transformer attention works, and it will matter to anyone building on AI until post-transformer models arrive. Last stated 3 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: Directly optimizing prompt embeddings (prefix-tuning) yields better performance than even a single optimized text prompt. — tap to centre the map on it Directly optimizing prompt embeddings (prefix-tuning) yields better performance than even a single optimized text prompt. Last stated 6 years ago
4 Feb 2021
GW
Gwern — holds since 2021-02-04 — tap for who they are
Same subject: AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function. — tap to centre the map on it AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function. Last stated 3 months ago
26 Jul 2026
JH
Jensen Huang — holds since 2026-07-26 — tap for who they are
Same subject: Atlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century. — tap to centre the map on it Atlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: Growth of internet image data can enable more sophisticated models for indexing and interacting with multimedia. — tap to centre the map on it Growth of internet image data can enable more sophisticated models for indexing and interacting with multimedia. Last stated 17 years ago
1 Jun 2009
FL
Fei-Fei Li — holds since 2009-06-01 — tap for who they are
At the centre
Growing evidence suggests the brain performs a form of backpropagation-like learning.
Holds Read this korrent →
The most fertile way to study neuroscience today is to work on AI models rather than on brains.
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
Deep learning generalises badly, and catastrophic interference with what a network already knew is the proof of it.
Holds
Recurrent backpropagation learns slowly over long intervals mainly because error backflow decays.
Holds
The brain is doing classical computation, not quantum, so everything it does can in principle be mimicked by a classical computer.
Holds
The adult brain's stock of neurons is essentially fixed, and that is no limit on learning, because plasticity is the changing of connections between the cells you already have.
Holds
Multilayer neural networks trained with backpropagation are the best example of successful gradient-based learning.
Holds
Deep learning will be understood very differently in thirty years than it is today.
Holds
Batteries will replace transmission as the cheapest way to keep the lights on, and the grid will shrink rather than grow.
Holds
Most agent harnesses need some pruning during context compaction anyway for cost reasons, which Jev might help with.
Holds
Neuroplasticity is as much the removal of connections as the growth of new ones, so pruning is a form of learning rather than a form of decay.
Holds
As reasoning models and agents retain more tokens for longer, KV-cache size and attention cost become the main constraint on scaling context.
Holds
Context engineering has stayed relevant for a year because it is grounded in how transformer attention works, and it will matter to anyone building on AI until post-transformer models arrive.
Holds
Directly optimizing prompt embeddings (prefix-tuning) yields better performance than even a single optimized text prompt.
Holds
AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function.
Holds
Atlas is the first model to unify pixel generation and pixel reconstruction, two tracks computer vision has kept apart for over half a century.
Holds
Growth of internet image data can enable more sophisticated models for indexing and interacting with multimedia.
Holds