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← Traditional recommender systems predict user behavior well but cannot…
17 connected korrents · 17 moments on record from 5 Nov 2019 to 15 Sept 2026.
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AI agents 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: Traditional recommender systems predict user behavior well but cannot be steered via natural language or explain their own reasoning. Traditional recommender systems predict user behavior well but cannot be steered via natural language or explain their own reasoning. Last stated a year ago
14 Sept 2025
EY
Eugene Yan — holds since 2025-09-14 — 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 a month ago
12 Aug 2026
CF
Chelsea Finn — holds since 2026-08-12 — tap for who they are
Same subject: The recommendation algorithm has no idea that you like surfing; it has a number that happens to correlate with surfing. — tap to centre the map on it The recommendation algorithm has no idea that you like surfing; it has a number that happens to correlate with surfing. Last stated 2 months ago
9 Jul 2026
AM
Adam Mosseri — holds since 2026-07-09 — tap for who they are
Same subject: Recommendation algorithms are not sinister; they simply predict engagement, and what humans engage with most is outrage. — tap to centre the map on it Recommendation algorithms are not sinister; they simply predict engagement, and what humans engage with most is outrage. Last stated 3 years ago
29 Jun 2023
GH
George Hotz — holds since 2023-06-29 — tap for who they are
Same subject: Social media recommendation algorithms are only partially designed to serve the people using them. — tap to centre the map on it Social media recommendation algorithms are only partially designed to serve the people using them. Last stated 8 months ago
13 Jan 2026
AW
Adam Wiggins — holds since 2026-01-13 — tap for who they are
Same subject: A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world. — tap to centre the map on it A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world. Last stated 12 months ago
26 Sept 2025
RS
Richard Sutton — holds since 2025-09-26 — tap for who they are
Same subject: Increasing language model size does not inherently improve following of user intent. — tap to centre the map on it Increasing language model size does not inherently improve following of user intent. Last stated 5 years ago
4 Mar 2022
JL
Jan Leike — holds since 2022-03-04 — tap for who they are
Same subject: Fine-tuning with human feedback is a promising direction for aligning language models with human intent. — tap to centre the map on it Fine-tuning with human feedback is a promising direction for aligning language models with human intent. Last stated 5 years ago
4 Mar 2022
JL
Jan Leike — holds since 2022-03-04 — tap for who they are
Same subject: A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. — tap to centre the map on it A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human. Last stated 2 years ago
22 Apr 2024
SC
Sean Carroll — holds since 2024-04-22 — tap for who they are
Same subject: A true artificial general intelligence cannot exist without being recognized as a moral subject. — tap to centre the map on it A true artificial general intelligence cannot exist without being recognized as a moral subject. Last stated a year ago
10 Jun 2025
SH
Samuel Hammond — holds since 2025-06-10 — tap for who they are
Same subject: A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. — tap to centre the map on it A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model. Last stated 6 days ago
15 Sept 2026
PG
Paul Graham — holds since 2026-09-15 — tap for who they are
Same subject: Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. — tap to centre the map on it Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect. Last stated 7 years ago
5 Nov 2019
FC
François Chollet — holds since 2019-11-05 — tap for who they are
Same subject: A low-effort AI-designed landing page costs a product its audience before the product itself is ever judged. — tap to centre the map on it A low-effort AI-designed landing page costs a product its audience before the product itself is ever judged. Last stated a month ago
13 Aug 2026
MH
Mitchell Hashimoto — holds since 2026-08-13 — tap for who they are
Same subject: A stream of AI-written papers with any error rate at all becomes insufferable, because finding the error costs more than the paper is worth even at ninety-nine percent. — tap to centre the map on it A stream of AI-written papers with any error rate at all becomes insufferable, because finding the error costs more than the paper is worth even at ninety-nine percent. Last stated 3 months ago
30 Jun 2026
GS
Grant Sanderson — holds since 2026-06-30 — tap for who they are
Same subject: AI-generated writing tends to repeat the same themes, names, and underlying ideas across different outputs. — tap to centre the map on it AI-generated writing tends to repeat the same themes, names, and underlying ideas across different outputs. Last stated 3 weeks ago
31 Aug 2026
EM
Ethan Mollick — holds since 2026-08-31 — tap for who they are
Same subject: "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. — tap to centre the map on it "Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next. Last stated 10 months ago
25 Nov 2025
IS
Ilya Sutskever — holds since 2025-11-25 — 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 3 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
At the centre
Traditional recommender systems predict user behavior well but cannot be steered via natural language or explain their own reasoning.
Holds Read this korrent →
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
The recommendation algorithm has no idea that you like surfing; it has a number that happens to correlate with surfing.
Holds
Recommendation algorithms are not sinister; they simply predict engagement, and what humans engage with most is outrage.
Holds
Social media recommendation algorithms are only partially designed to serve the people using them.
Holds
A language model can predict what a person would say but not what will happen, and only the second of those is a model of the world.
Holds
Increasing language model size does not inherently improve following of user intent.
Holds
Fine-tuning with human feedback is a promising direction for aligning language models with human intent.
Holds
A language model's apparent mind is mostly our own bias: it predicts text, and leverages our evolved habit of attributing intentionality to anything that acts human.
Holds
A true artificial general intelligence cannot exist without being recognized as a moral subject.
Holds
A unit of AI inference needs to be defined, for example via a chain of increasingly hard problems where each consecutive pair is solvable by one model.
Holds
Both the special-purpose-programs view and the blank-slate view of human intelligence are likely incorrect.
Holds
A low-effort AI-designed landing page costs a product its audience before the product itself is ever judged.
Holds
A stream of AI-written papers with any error rate at all becomes insufferable, because finding the error costs more than the paper is worth even at ninety-nine percent.
Holds
AI-generated writing tends to repeat the same themes, names, and underlying ideas across different outputs.
Holds
"Scaling" was powerful because it was one word: naming a research direction is what tells a whole field what to do next.
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
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