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← A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
17 connected korrents · 17 moments from 1 Jan 1998 to 21 Sept 2026. Nearly all of them are about neural networks .
Everything filed under reinforcement learning
reinforcement learning
Everything filed under AI and jobs
AI and jobs
Everything filed under LLMs
LLMs
Everything filed under pruning
pruning
Everything filed under open source
open source
Everything filed under data centers
data centers
Everything filed under computer vision
computer vision Rests on 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: A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. 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
Rests on: How verifiable a task is now predicts how automatable it is, the way specifiability predicted it in the 1980s. — tap to centre the map on it How verifiable a task is now predicts how automatable it is, the way specifiability predicted it in the 1980s. Last stated 11 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — tap for who they are
Same subject: A neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression. — tap to centre the map on it A neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression. Last stated a month ago
7 Sept 2026
KK
Kevin Kelly — holds since 2026-09-07 — 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: AI is unlikely to be a hyperprofits engine for research labs whose attached companies make money serving neural-network calculations from datacenters. — tap to centre the map on it AI is unlikely to be a hyperprofits engine for research labs whose attached companies make money serving neural-network calculations from datacenters. Last stated 4 weeks ago
21 Sept 2026
BD
Brad DeLong — holds since 2026-09-21 — 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 7 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: Complete reverse-engineering of neural networks is a less promising research direction than studying model biology and applying interpretability usefully — tap to centre the map on it Complete reverse-engineering of neural networks is a less promising research direction than studying model biology and applying interpretability usefully Last stated a year ago
19 Aug 2025
NN
Neel Nanda — holds since 2025-08-19 — 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: 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 2 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. — tap to centre the map on it Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model. Last stated 2 months ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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 4 weeks 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
At the centre
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds Read this korrent →
How verifiable a task is now predicts how automatable it is, the way specifiability predicted it in the 1980s.
Holds
A neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression.
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
AI is unlikely to be a hyperprofits engine for research labs whose attached companies make money serving neural-network calculations from datacenters.
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
Complete reverse-engineering of neural networks is a less promising research direction than studying model biology and applying interpretability usefully
Holds
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
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
Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.
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