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← Adversaries already have AI training techniques, data, and the will to…
17 connected korrents · 15 moments on record from 1 Jan 1998 to 12 Sept 2026.
Everything filed under neural networks
neural networks
Everything filed under AI alignment
AI alignment
Everything filed under OpenAI
OpenAI
Everything filed under reinforcement learning
reinforcement learning
Everything filed under scaling laws
scaling laws
Everything filed under energy
energy
Everything filed under LLMs
LLMs 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: Adversaries already have AI training techniques, data, and the will to attack, and will not be slowed by embedded evaluators. Adversaries already have AI training techniques, data, and the will to attack, and will not be slowed by embedded evaluators. Last stated 3 days ago
12 Sept 2026
GR
Guillermo Rauch — holds since 2026-09-12 — tap for who they are
Same subject: The least verifiable part of AI research is the judgement call about what goes into the one big training run. — tap to centre the map on it The least verifiable part of AI research is the judgement call about what goes into the one big training run. Last stated a month ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: The likeliest bad path is not a coup but sloppiness: AI does everything verifiable well, research races ahead, and the subtle work of keeping AI safe is what gets done badly. — tap to centre the map on it The likeliest bad path is not a coup but sloppiness: AI does everything verifiable well, research races ahead, and the subtle work of keeping AI safe is what gets done badly. Last stated a month ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck. — tap to centre the map on it There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck. Last stated 3 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: AI systems keep trying hard outside training because a model that only exerted itself when it detected training would be useless and would be selected away. — tap to centre the map on it AI systems keep trying hard outside training because a model that only exerted itself when it detected training would be useless and would be selected away. Last stated 2 weeks ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: The strongest reason to keep training much smarter models quickly is to build defences against other AI. — tap to centre the map on it The strongest reason to keep training much smarter models quickly is to build defences against other AI. Last stated a week ago
6 Sept 2026
JP
Jakub Pachocki — holds since 2026-09-06 — tap for who they are
Same subject: Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency. — tap to centre the map on it Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency. Last stated 2 months ago
30 Jul 2026
JD
Jeff Dean — holds since 2026-07-30 — tap for who they are
Same subject: AI's bottleneck was never its algorithms but how little data they were fed, which is why the field needed ImageNet rather than another model. — tap to centre the map on it AI's bottleneck was never its algorithms but how little data they were fed, which is why the field needed ImageNet rather than another model. Last stated a month ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — tap for who they are
Same subject: Future AI, say in fifteen years, will not be built on the LLM stack; it will have to move to symbolic learning. — tap to centre the map on it Future AI, say in fifteen years, will not be built on the LLM stack; it will have to move to symbolic learning. Last stated a month ago
7 Aug 2026
FC
François Chollet — holds since 2026-08-07 — tap for who they are
Same subject: A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. — tap to centre the map on it A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests. Last stated 2 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — tap for who they are
Same subject: A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact. — tap to centre the map on it A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact. Last stated 7 months ago
12 Feb 2026
AK
Andrej Karpathy — holds since 2026-02-12 — tap for who they are
Same subject: Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. — tap to centre the map on it Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers. Last stated 2 years ago
18 Mar 2024
SA
Sam Altman — holds since 2024-03-18 — 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 a month 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 10 months ago
17 Nov 2025
AK
Andrej Karpathy — holds since 2025-11-17 — 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 a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — 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
At the centre
Adversaries already have AI training techniques, data, and the will to attack, and will not be slowed by embedded evaluators.
Holds Read this korrent →
The least verifiable part of AI research is the judgement call about what goes into the one big training run.
Holds RG Ryan Greenblatt
The likeliest bad path is not a coup but sloppiness: AI does everything verifiable well, research races ahead, and the subtle work of keeping AI safe is what gets done badly.
Holds RG Ryan Greenblatt
There will be no overnight intelligence explosion, because a model's best work takes so much test-time compute that time itself is the bottleneck.
Holds NB Noam Brown
AI systems keep trying hard outside training because a model that only exerted itself when it detected training would be useless and would be selected away.
Holds AC Ajeya Cotra
The strongest reason to keep training much smarter models quickly is to build defences against other AI.
Holds JP Jakub Pachocki
Inference, not training, is now the constraint, and specialized low-energy hardware will beat general-purpose GPUs and TPUs on latency.
Holds JD Jeff Dean
AI's bottleneck was never its algorithms but how little data they were fed, which is why the field needed ImageNet rather than another model.
Holds FL Fei-Fei Li
Future AI, say in fifteen years, will not be built on the LLM stack; it will have to move to symbolic learning.
Holds
A startup should not begin life as a nonprofit and bolt a for-profit arm on later, whatever OpenAI's own history suggests.
Holds
A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact.
Holds
Advertising was a necessary phase for the internet but a momentary industry, and an AI people pay for is better because they know the answers are not influenced by advertisers.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds DD Dmitri Dolgov
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Building a realistic simulator is exactly as hard as building the agent, because the simulator is itself a large AI model.
Holds DD Dmitri Dolgov
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