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← Widespread tolerance for AI being wrong means adding AI features to a…
17 connected korrents · 15 moments from 10 Oct 2023 to 22 Sept 2026.
Everything filed under LLMs
LLMs
Everything filed under AI alignment
AI alignment
Everything filed under reinforcement learning
reinforcement learning
Everything filed under AI agents
AI agents
Everything filed under privacy
privacy
Everything filed under design
design
Everything filed under AGI
AGI 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: Widespread tolerance for AI being wrong means adding AI features to a product carries little brand risk as long as it behaves as expected. Widespread tolerance for AI being wrong means adding AI features to a product carries little brand risk as long as it behaves as expected. Last stated 2 weeks ago
21 Sept 2026
HD
Horace Dediu — holds since 2026-09-21 — tap for who they are
Same subject: People have grown accustomed to AI being non-deterministic and occasionally wrong, so it carries little brand risk anymore. — tap to centre the map on it People have grown accustomed to AI being non-deterministic and occasionally wrong, so it carries little brand risk anymore. Last stated 2 weeks ago
18 Sept 2026
HD
Horace Dediu — holds since 2026-09-18 — tap for who they are
Same subject: For a company embedding AI in its products, the bigger risk to manage is a privacy or security breach, not the AI being wrong. — tap to centre the map on it For a company embedding AI in its products, the bigger risk to manage is a privacy or security breach, not the AI being wrong. Last stated 2 weeks ago
21 Sept 2026
HD
Horace Dediu — holds since 2026-09-21 — tap for who they are
Same subject: Consumer AI that is right 90% of the time delights people; industrial AI that is right 90% of the time is 100% dissatisfaction. — tap to centre the map on it Consumer AI that is right 90% of the time delights people; industrial AI that is right 90% of the time is 100% dissatisfaction. Last stated 9 months ago
8 Jan 2026
JH
Jensen Huang — holds since 2026-01-08 — 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 2 months ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: Building AI that can actually be trusted is the goal; containing an AI known to be misaligned is not a substitute for it. — tap to centre the map on it Building AI that can actually be trusted is the goal; containing an AI known to be misaligned is not a substitute for it. Last stated 2 years ago
24 Jan 2025
JL
Jan Leike — holds since 2025-01-24 — tap for who they are
Same subject: There is no route to AI alignment or safety that goes around reliability: a system that cannot reliably follow a known algorithm cannot be made safe. — tap to centre the map on it There is no route to AI alignment or safety that goes around reliability: a system that cannot reliably follow a known algorithm cannot be made safe. Last stated a year ago
7 Jun 2025
GM
Gary Marcus — holds since 2025-06-07 — tap for who they are
Same subject: AI agents are less reliable than humans when a failure depends on a subjective definition of a good product experience. — tap to centre the map on it AI agents are less reliable than humans when a failure depends on a subjective definition of a good product experience. Last stated a week ago
22 Sept 2026
LR
Lenny Rachitsky — holds since 2026-09-22 — tap for who they are
Same subject: AI companies are conflicted messengers when warning about AI safety risks, given their own marketing and regulatory incentives. — tap to centre the map on it AI companies are conflicted messengers when warning about AI safety risks, given their own marketing and regulatory incentives. Last stated 3 weeks ago
11 Sept 2026
CN
Casey Newton — holds since 2026-09-11 — tap for who they are
Same subject: A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated. — tap to centre the map on it A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated. Last stated 4 months ago
4 Jun 2026
AI
Alex Imas — holds since 2026-06-04 — tap for who they are
Same subject: A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. — tap to centre the map on it A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted. Last stated a year ago
7 Jun 2025
GM
Gary Marcus — holds since 2025-06-07 — tap for who they are
Same subject: A model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality — tap to centre the map on it A model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality Last stated 3 years ago
10 Oct 2023
CH
Chip Huyen — holds since 2023-10-10 — 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 9 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 child who has seen ten cats learns what a machine needs the whole internet of cat photos for, by a learning pathway nobody has solved. — tap to centre the map on it A child who has seen ten cats learns what a machine needs the whole internet of cat photos for, by a learning pathway nobody has solved. Last stated 2 months ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — 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 2 months 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: Better execution environments are needed for LLMs to properly utilize their ability to evolve systems. — tap to centre the map on it Better execution environments are needed for LLMs to properly utilize their ability to evolve systems. Last stated 2 weeks ago
21 Sept 2026
TL
Tobias Lütke — holds since 2026-09-21 — tap for who they are
At the centre
Widespread tolerance for AI being wrong means adding AI features to a product carries little brand risk as long as it behaves as expected.
Holds Read this korrent →
People have grown accustomed to AI being non-deterministic and occasionally wrong, so it carries little brand risk anymore.
Holds
For a company embedding AI in its products, the bigger risk to manage is a privacy or security breach, not the AI being wrong.
Holds
Consumer AI that is right 90% of the time delights people; industrial AI that is right 90% of the time is 100% dissatisfaction.
Holds
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
Building AI that can actually be trusted is the goal; containing an AI known to be misaligned is not a substitute for it.
Holds
There is no route to AI alignment or safety that goes around reliability: a system that cannot reliably follow a known algorithm cannot be made safe.
Holds
AI agents are less reliable than humans when a failure depends on a subjective definition of a good product experience.
Holds
AI companies are conflicted messengers when warning about AI safety risks, given their own marketing and regulatory incentives.
Holds
A country outside the AI supply chain should just buy the index — which works only in the world where AI ends up commoditised rather than concentrated.
Holds
A language model is no substitute for a well-specified conventional algorithm, so it cannot simply be dropped into a complex problem and trusted.
Holds
A model that could learn effectively from raw bitstrings or bytestrings would be extremely powerful, able to learn from any data modality
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 child who has seen ten cats learns what a machine needs the whole internet of cat photos for, by a learning pathway nobody has solved.
Holds
A physical AI company needs three AIs, not one — the agent, the simulator and the critic — turning deployment into a flywheel.
Holds
A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well.
Holds
Better execution environments are needed for LLMs to properly utilize their ability to evolve systems.
Holds