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← Physical AI has to make far fewer mistakes than any AI deployed so…
17 connected korrents · 14 moments on record from 22 Jun 2023 to 3 Sept 2026.
Everything filed under chat interfaces
chat interfaces
Everything filed under robotics
robotics
Everything filed under OpenAI
OpenAI
Everything filed under reinforcement learning
reinforcement learning
Everything filed under AI alignment
AI alignment 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: Physical AI has to make far fewer mistakes than any AI deployed so far, because it is only useful when it acts autonomously on the world. Physical AI has to make far fewer mistakes than any AI deployed so far, because it is only useful when it acts autonomously on the world. Last stated 4 weeks ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. — tap to centre the map on it A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases. Last stated a month ago
3 Aug 2026
DD
Dmitri Dolgov — holds since 2026-08-03 — tap for who they are
Same subject: Very capable AI will be harder to align than current systems, because the loop of spotting a bad behaviour and patching the training that caused it breaks down. — tap to centre the map on it Very capable AI will be harder to align than current systems, because the loop of spotting a bad behaviour and patching the training that caused it breaks down. Last stated 4 weeks ago
11 Aug 2026
RG
Ryan Greenblatt — holds since 2026-08-11 — tap for who they are
Same subject: Robots will learn from deployment more easily than chatbots do, because a physical mistake is obvious the moment it happens. — tap to centre the map on it Robots will learn from deployment more easily than chatbots do, because a physical mistake is obvious the moment it happens. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — 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: 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 6 days ago
1 Sept 2026
AC
Ajeya Cotra — holds since 2026-09-01 — tap for who they are
Same subject: Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data. — tap to centre the map on it Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data. Last stated 4 weeks ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: Better AI makes robots cheaper, because cheap visual feedback removes the need for expensive mechanical precision. — tap to centre the map on it Better AI makes robots cheaper, because cheap visual feedback removes the need for expensive mechanical precision. Last stated 12 months ago
12 Sept 2025
SL
Sergey Levine — holds since 2025-09-12 — tap for who they are
Same subject: Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. — tap to centre the map on it Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful. Last stated 4 weeks ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: A blank chat box is lazy: it violates the first rule of a good user experience, that it is obvious what you can do. — tap to centre the map on it A blank chat box is lazy: it violates the first rule of a good user experience, that it is obvious what you can do. Last stated a year ago
12 May 2025
JZ
Julie Zhuo — holds since 2025-05-12 — tap for who they are
Same subject: AI is the first new user-interface paradigm in sixty years: intent-based outcome specification, where the user says what outcome they want rather than how to get it. — tap to centre the map on it AI is the first new user-interface paradigm in sixty years: intent-based outcome specification, where the user says what outcome they want rather than how to get it. Last stated 3 years ago
22 Jun 2023
JN
Jakob Nielsen — holds since 2023-06-22 — tap for who they are
Same subject: All user interfaces will converge on the same shape: chat and gestures in, chat and video out. — tap to centre the map on it All user interfaces will converge on the same shape: chat and gestures in, chat and video out. Last stated 3 weeks ago
19 Aug 2026
NR
Naval Ravikant — holds since 2026-08-19 — 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 technique that lets an AI model's reasoning shift outside its visible Chain of Thought is dangerous, both because it works and because a leading lab is willing to deploy it. — tap to centre the map on it A technique that lets an AI model's reasoning shift outside its visible Chain of Thought is dangerous, both because it works and because a leading lab is willing to deploy it. Last stated 4 days ago
3 Sept 2026
ZM
Zvi Mowshowitz — holds since 2026-09-03 — 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: 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
At the centre
Physical AI has to make far fewer mistakes than any AI deployed so far, because it is only useful when it acts autonomously on the world.
Holds CF Chelsea FinnRead this korrent →
A physical AI agent has to be safe on day one, because you cannot ship something merely good enough and let users find the edge cases.
Holds DD Dmitri Dolgov
Very capable AI will be harder to align than current systems, because the loop of spotting a bad behaviour and patching the training that caused it breaks down.
Holds RG Ryan Greenblatt
Robots will learn from deployment more easily than chatbots do, because a physical mistake is obvious the moment it happens.
Holds SL Sergey Levine
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
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
Hand-tuning a robot's data set cannot reach very high reliability, because people get tired; the system has to seek out its own missing data.
Holds CF Chelsea Finn
Better AI makes robots cheaper, because cheap visual feedback removes the need for expensive mechanical precision.
Holds SL Sergey Levine
Every machine-learning deployment that has paid off so far left a person making the decision, which is the only reason imperfect models were useful.
Holds CF Chelsea Finn
A blank chat box is lazy: it violates the first rule of a good user experience, that it is obvious what you can do.
Holds JZ Julie Zhuo
AI is the first new user-interface paradigm in sixty years: intent-based outcome specification, where the user says what outcome they want rather than how to get it.
Holds JN Jakob Nielsen
All user interfaces will converge on the same shape: chat and gestures in, chat and video out.
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 technique that lets an AI model's reasoning shift outside its visible Chain of Thought is dangerous, both because it works and because a leading lab is willing to deploy it.
Holds ZM Zvi Mowshowitz
A tiny language model inventing a plausible-sounding name is the same phenomenon as a large one confidently stating a false fact.
Holds
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