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← Synthetic experience can only rehearse what a system already knows;…
17 connected korrents · 13 moments from 6 Jul 2016 to 22 Sept 2026.
Everything filed under world models
world models
Everything filed under recursive self-improvement
recursive self-improvement
Everything filed under reinforcement learning
reinforcement learning
Everything filed under mental models
mental models
Everything filed under LLMs
LLMs
Everything filed under physics
physics
Everything filed under scaling laws
scaling laws
Everything filed under consciousness
consciousness 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 a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: A system trained only on what is already known cannot produce genuine surprises, because a known thing stops being a surprise. — tap to centre the map on it A system trained only on what is already known cannot produce genuine surprises, because a known thing stops being a surprise. Last stated a month ago
18 Sept 2026
CD
Cory Doctorow — holds since 2026-09-18 — 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 a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — 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: 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: 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 3 months ago
8 Jul 2026
MC
Meaghan Choi — holds since 2026-07-08 — 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: An adaptive agent's world model cannot be a static representation learned once and fixed — tap to centre the map on it An adaptive agent's world model cannot be a static representation learned once and fixed Last stated a year ago
17 Jul 2025
FC
François Chollet — holds since 2025-07-17 — tap for who they are
Same subject: Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop. — tap to centre the map on it Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop. Last stated 2 months ago
10 Aug 2026
FC
François Chollet — holds since 2026-08-10 — tap for who they are
Same subject: A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute. — tap to centre the map on it A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute. Last stated 4 weeks ago
22 Sept 2026
TC
Tyler Cowen — holds since 2026-09-22 — tap for who they are
Same subject: AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started. — tap to centre the map on it AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started. Last stated 3 months ago
3 Aug 2026
CP
Chamath Palihapitiya — holds since 2026-08-03 — tap for who they are
Same subject: AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033. — tap to centre the map on it AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033. Last stated 2 months ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does. — tap to centre the map on it The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does. Last stated a year ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — 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
At the centre
Synthetic experience can only rehearse what a system already knows; information about the world has to be injected from outside.
Holds Read this korrent →
A system trained only on what is already known cannot produce genuine surprises, because a known thing stops being a surprise.
Holds
Reinforcement learning only works once a model already knows something, which is why robots must be pre-trained by imitation first.
Holds
Mental models can help explain and navigate the world without being good in themselves.
Holds
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
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
Consciousness and free will are not illusions: being emergent and non-fundamental is exactly as real as a table is.
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
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
An adaptive agent's world model cannot be a static representation learned once and fixed
Holds
Coding is not just another application domain for AI; it is the meta-skill that lets AI generate its own training material and start the recursive self-improvement loop.
Holds
A ban on self-recursive improvement in AI models would create a big incentive for some other parts of the world to invest a lot more in compute.
Holds
AI is already inside the recursive self-improvement loop — what the labs have shipped in recent weeks shows the cycle has started.
Holds
AI research will be fully automated around 2030 or 2031, and AI will beat humans at essentially any job by about 2033.
Holds
The goal was never good simulation; it was answering counterfactuals, and a value function does that job as well as a simulator does.
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