Tap a claim on the ring to put it at the centre.
← A model that invents an aerial view of the Stanford quad out of…
16 connected korrents · 13 moments from 1 Jan 1998 to 17 Sept 2026.
Everything filed under LLMs
LLMs
Everything filed under neural networks
neural networks
Everything filed under computer vision
computer vision
Everything filed under scaling laws
scaling laws
Everything filed under robotics
robotics
Everything filed under physics
physics 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 model that invents an aerial view of the Stanford quad out of photographs taken from the ground is generating pixels that still obey the laws of reconstruction. A model that invents an aerial view of the Stanford quad out of photographs taken from the ground is generating pixels that still obey the laws of reconstruction. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — 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: 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: 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: Generating pixels was the easy step; generating pixels that are spatially contextualised and grounded is the hard one. — tap to centre the map on it Generating pixels was the easy step; generating pixels that are spatially contextualised and grounded is the hard one. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: Video generation was not an algorithmic revolution: it was the same neural-network era with video added to the training data. — tap to centre the map on it Video generation was not an algorithmic revolution: it was the same neural-network era with video added to the training data. Last stated 2 months ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — tap for who they are
Same subject: The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. — tap to centre the map on it The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture. Last stated 2 months ago
4 Sept 2026
FL
Fei-Fei Li — holds since 2026-09-04 — tap for who they are
Same subject: AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it. — tap to centre the map on it AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it. Last stated a year ago
23 Jul 2025
DH
Demis Hassabis — holds since 2025-07-23 — 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: 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: A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. — tap to centre the map on it A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models. Last stated 8 years ago
11 Oct 2018
KT
Kristina Toutanova — holds since 2018-10-11 — tap for who they are
MC
Ming-Wei Chang — holds since 2018-10-11 — tap for who they are
KL
Kenton Lee — holds since 2018-10-11 — tap for who they are
JD
Jacob Devlin — holds since 2018-10-11 — 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 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 2 months ago
7 Sept 2026
KK
Kevin Kelly — holds since 2026-09-07 — tap for who they are
Same subject: A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. — tap to centre the map on it A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI. Last stated 10 months ago
1 Jan 2026
JL
Jason Lemkin — holds since 2026-01-01 — tap for who they are
Same subject: A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. — tap to centre the map on it A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to. Last stated 3 months ago
15 Jul 2026
DH
Dex Horthy — holds since 2026-07-15 — tap for who they are
Same subject: A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls. — tap to centre the map on it A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls. Last stated a month ago
17 Sept 2026
PC
Patrick Collison — holds since 2026-09-17 — tap for who they are
At the centre
A model that invents an aerial view of the Stanford quad out of photographs taken from the ground is generating pixels that still obey the laws of reconstruction.
Holds Read this korrent →
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
Letting a robot imagine the next image helps, but it is not essential — the model was surprisingly good without 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
Generating pixels was the easy step; generating pixels that are spatially contextualised and grounded is the hard one.
Holds
Video generation was not an algorithmic revolution: it was the same neural-network era with video added to the training data.
Holds
The scaling law for world models was a conviction held in advance, not a discovery; the devils were always in the architecture choices and the data mixture.
Holds
AI will not run out of data, because there is already enough real-world data to build the simulators that generate more of it.
Holds
AlexNet's real breakthrough was not computer vision but the discovery of a universal function approximator that can learn any function.
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
A deep bidirectional model is strictly more powerful than a left-to-right model or a shallow concatenation of unidirectional models.
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 neural network's latent space is closer to an uncopyrightable syntax than to copyrightable expression.
Holds
A badly written AI outbound email is evidence of a bad vendor, not of a limit of AI.
Holds
A bigger context window does not give you a smarter model; the intelligence of the model is what decides how much of that window it can actually attend to.
Holds
A body of publications is easy for AI models to consume via pretraining corpora but cumbersome for most humans due to CAPTCHAs, accounts, and paywalls.
Holds