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← Synthetic experience can only rehearse what a system already knows;…
17 connected korrents · 15 moments on record from 9 Nov 2023 to 12 Aug 2026.
Everything filed under reinforcement learning
reinforcement learning
Everything filed under robotics
robotics
Everything filed under scaling laws
scaling laws
Everything filed under self-driving cars
self-driving cars 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: Synthetic experience can only rehearse what a system already knows; information about the world has to be injected from outside. Synthetic experience can only rehearse what a system already knows; information about the world has to be injected from outside. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first. — tap to centre the map on it Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: If we live in a simulation, whoever is running it does not know how it ends, because nobody runs a simulation whose outcome they already know. — tap to centre the map on it If we live in a simulation, whoever is running it does not know how it ends, because nobody runs a simulation whose outcome they already know. Last stated 3 years ago
9 Nov 2023
EM
Elon Musk — holds since 2023-11-09 — 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: 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: Consciousness and free will are not illusions: being emergent and non-fundamental is exactly as real as a table is. — tap to centre the map on it Consciousness and free will are not illusions: being emergent and non-fundamental is exactly as real as a table is. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: Once a robot is good enough, you can teach it with words instead of with demonstrations, and language becomes a training signal for motor skill. — tap to centre the map on it Once a robot is good enough, you can teach it with words instead of with demonstrations, and language becomes a training signal for motor skill. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: The advantage of digital minds may be the opposite of pooling knowledge: deliberately giving agents different contexts, which is a control humans do not have over themselves. — tap to centre the map on it The advantage of digital minds may be the opposite of pooling knowledge: deliberately giving agents different contexts, which is a control humans do not have over themselves. Last stated 2 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: Watching humans is not enough for a robot: there is no substitute for experience gathered on its own body. — tap to centre the map on it Watching humans is not enough for a robot: there is no substitute for experience gathered on its own body. Last stated 4 weeks ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — 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: Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. — tap to centre the map on it Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. Last stated 6 months ago
20 Mar 2026
AK
Andrej Karpathy — holds since 2026-03-20 — tap for who they are
Same subject: Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. — tap to centre the map on it Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. — tap to centre the map on it A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number. Last stated 2 months ago
26 Jun 2026
NB
Noam Brown — holds since 2026-06-26 — tap for who they are
Same subject: A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. — tap to centre the map on it A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from. Last stated 6 months ago
13 Mar 2026
DP
Dylan Patel — holds since 2026-03-13 — tap for who they are
Same subject: After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. — tap to centre the map on it After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute. Last stated 6 months ago
23 Mar 2026
JH
Jensen Huang — holds since 2026-03-23 — tap for who they are
Same subject: A five-year-old's vision is already good enough to drive a car, on a tiny and strikingly undiverse amount of data. — tap to centre the map on it A five-year-old's vision is already good enough to drive a car, on a tiny and strikingly undiverse amount of data. Last stated 9 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — tap for who they are
Same subject: A good robotaxi demonstration in one city does not mean the company is ready to scale the service. — tap to centre the map on it A good robotaxi demonstration in one city does not mean the company is ready to scale the service. Last stated a year ago
8 Jul 2025
TL
Timothy B. Lee — holds since 2025-07-08 — tap for who they are
Same subject: A huge fleet collecting driving data is not a silver bullet for self-driving, because the data arrives unlabelled. — tap to centre the map on it A huge fleet collecting driving data is not a silver bullet for self-driving, because the data arrives unlabelled. Last stated 2 years ago
21 May 2024
TL
Timothy B. Lee — holds since 2024-05-21 — tap for who they are
At the centre
Synthetic experience can only rehearse what a system already knows; information about the world has to be injected from outside.
Holds SL Sergey LevineRead this korrent →
Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first.
Holds SL Sergey Levine
If we live in a simulation, whoever is running it does not know how it ends, because nobody runs a simulation whose outcome they already know.
Holds
Intuitive physics can be learned from passive observation alone, so an embodied robot is not required to understand the physical world.
Holds DH Demis Hassabis
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 DH Demis Hassabis
Consciousness and free will are not illusions: being emergent and non-fundamental is exactly as real as a table is.
Holds SC Sean Carroll
Once a robot is good enough, you can teach it with words instead of with demonstrations, and language becomes a training signal for motor skill.
Holds SL Sergey Levine
The advantage of digital minds may be the opposite of pooling knowledge: deliberately giving agents different contexts, which is a control humans do not have over themselves.
Holds GS Grant Sanderson
Watching humans is not enough for a robot: there is no substitute for experience gathered on its own body.
Holds CF Chelsea Finn
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago.
Holds
Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving.
Holds
A benchmark result should be reported under a stated budget, or as a curve against test-time compute — never as a single number.
Holds NB Noam Brown
A lab should spend most of its compute on research rather than on building the next model, because research is where the tenfold yearly efficiency gains come from.
Holds DP Dylan Patel
After pre-training, post-training and test-time scaling, the fourth scaling law is agentic: multiplying AI by spawning agents, and the whole loop scales on one thing, compute.
Holds JH Jensen Huang
A five-year-old's vision is already good enough to drive a car, on a tiny and strikingly undiverse amount of data.
Holds IS Ilya Sutskever
A good robotaxi demonstration in one city does not mean the company is ready to scale the service.
Holds TL Timothy B. Lee
A huge fleet collecting driving data is not a silver bullet for self-driving, because the data arrives unlabelled.
Holds TL Timothy B. Lee