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← Having a job to do is what makes perception tractable, which is why an…
17 connected korrents · 15 moments from 1 Jan 1998 to 4 Sept 2026.
Everything filed under robotics
robotics
Everything filed under computer vision
computer vision
Everything filed under reinforcement learning
reinforcement learning
Everything filed under mental models
mental models
Everything filed under world models
world models
Everything filed under semiconductors
semiconductors
Everything filed under AI alignment
AI alignment
Everything filed under optimism
optimism
Everything filed under reinforcement learning
reinforcement learning 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: Having a job to do is what makes perception tractable, which is why an embodied model can learn from vision where a video model cannot. Having a job to do is what makes perception tractable, which is why an embodied model can learn from vision where a video model cannot. Last stated a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work. — tap to centre the map on it A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: Letting a robot imagine the next image helps, but it is not essential — the model was surprisingly good without it. — tap to centre the map on it Letting a robot imagine the next image helps, but it is not essential — the model was surprisingly good without it. Last stated 2 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: The brain is more parallel than a GPU, and an embodied model should run perception, planning and memory at once rather than one token at a time. — tap to centre the map on it The brain is more parallel than a GPU, and an embodied model should run perception, planning and memory at once rather than one token at a time. Last stated a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: Intuitive physics can be learned from passive observation alone, so an embodied robot is not required to understand the physical world. — tap to centre the map on it Intuitive physics can be learned from passive observation alone, so an embodied robot is not required to understand the physical world. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — tap for who they are
Same subject: A model may have a worldview of its own, but it can never be placed above a person under any circumstance. — tap to centre the map on it A model may have a worldview of its own, but it can never be placed above a person under any circumstance. Last stated 5 months ago
3 Jun 2026
KH
Kelsey Hightower — holds since 2026-06-03 — tap for who they are
Same subject: Experimenting with mental models does not require high craft and is better done at speed, in the open, in iteration than by polishing something that is the wrong shape. — tap to centre the map on it Experimenting with mental models does not require high craft and is better done at speed, in the open, in iteration than by polishing something that is the wrong shape. Last stated 4 months ago
8 Jul 2026
MC
Meaghan Choi — holds since 2026-07-08 — tap for who they are
Same subject: The industry is too optimistic about AI for video, and the consistency problem will not simply be remedied. — tap to centre the map on it The industry is too optimistic about AI for video, and the consistency problem will not simply be remedied. Last stated a year ago
30 Apr 2025
TS
Tim Sweeney — holds since 2025-04-30 — tap for who they are
Same subject: Mental models can help explain and navigate the world without being good in themselves. — tap to centre the map on it Mental models can help explain and navigate the world without being good in themselves. Last stated 10 years ago
by 6 Jul 2016
GW
Gabriel Weinberg — holds since 2016-07-06 — 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: 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: Graph transformer networks allow multimodule document recognition systems to be trained globally with gradient methods to minimize overall performance. — tap to centre the map on it Graph transformer networks allow multimodule document recognition systems to be trained globally with gradient methods to minimize overall performance. Last stated 29 years ago
1 Jan 1998
YB
Yoshua Bengio — holds since 1998-01-01 — tap for who they are
Same subject: The depth of a representation, not any particular architecture trick, is what most visual recognition tasks turn on. — tap to centre the map on it The depth of a representation, not any particular architecture trick, is what most visual recognition tasks turn on. 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: Unsupervised learning trained on unfiltered, internet-scale image data now genuinely works. — tap to centre the map on it Unsupervised learning trained on unfiltered, internet-scale image data now genuinely works. Last stated 6 years ago
6 Apr 2021
GW
Gwern — holds since 2021-04-06 — 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
At the centre
Having a job to do is what makes perception tractable, which is why an embodied model can learn from vision where a video model cannot.
Holds Read this korrent →
A model that can predict the next frames of a video coherently understands the world, in the only sense of the word that is doing any work.
Holds
Letting a robot imagine the next image helps, but it is not essential — the model was surprisingly good without it.
Holds
The brain is more parallel than a GPU, and an embodied model should run perception, planning and memory at once rather than one token at a time.
Holds
Intuitive physics can be learned from passive observation alone, so an embodied robot is not required to understand the physical world.
Holds
A model may have a worldview of its own, but it can never be placed above a person under any circumstance.
Holds
Experimenting with mental models does not require high craft and is better done at speed, in the open, in iteration than by polishing something that is the wrong shape.
Holds
The industry is too optimistic about AI for video, and the consistency problem will not simply be remedied.
Holds
Mental models can help explain and navigate the world without being good in themselves.
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
Convolutional neural networks are specifically designed to handle variability in two-dimensional shapes.
Holds
Graph transformer networks allow multimodule document recognition systems to be trained globally with gradient methods to minimize overall performance.
Holds
The depth of a representation, not any particular architecture trick, is what most visual recognition tasks turn on.
Holds
Unsupervised learning trained on unfiltered, internet-scale image data now genuinely works.
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