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← Expertise is not passively absorbing patterns — it is learning what to…
17 connected korrents · 16 moments on record from 11 Aug 2023 to 10 Aug 2026.
Everything filed under LLMs
LLMs
Everything filed under scaling laws
scaling laws
Everything filed under reinforcement learning
reinforcement learning
Everything filed under Psychotherapy
Psychotherapy 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: Expertise is not passively absorbing patterns — it is learning what to look for, which makes becoming an expert a training of attention. Expertise is not passively absorbing patterns — it is learning what to look for, which makes becoming an expert a training of attention. Last stated 2 years ago
25 May 2024
CR
Charan Ranganath — holds since 2024-05-25 — tap for who they are
Same subject: Expertise is an enormous accumulated library of small facts and skills, which is why no method can shortcut the sheer volume of learning it takes. — tap to centre the map on it Expertise is an enormous accumulated library of small facts and skills, which is why no method can shortcut the sheer volume of learning it takes. Last stated 3 months ago
27 May 2026
SY
Scott H. Young — holds since 2026-05-27 — tap for who they are
Same subject: Large language models are bad at chess: pattern recognition is not the same capability as searching one position very deeply. — tap to centre the map on it Large language models are bad at chess: pattern recognition is not the same capability as searching one position very deeply. Last stated 11 months ago
5 Oct 2025
AC
Albert Cheng — holds since 2025-10-05 — tap for who they are
Same subject: The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. — tap to centre the map on it The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out. Last stated 11 months ago
17 Oct 2025
AK
Andrej Karpathy — holds since 2025-10-17 — tap for who they are
Same subject: Opening a fixed relational pattern takes two things: the implicit made explicit, and the person's own recoil from what they see. — tap to centre the map on it Opening a fixed relational pattern takes two things: the implicit made explicit, and the person's own recoil from what they see. Last stated 3 years ago
11 Aug 2023
TR
Terry Real — holds since 2023-08-11 — tap for who they are
Same subject: AI learns from patterns, so where the patterns are scarce, as in liver surgery, a human working with the machine beats the machine working alone. — tap to centre the map on it AI learns from patterns, so where the patterns are scarce, as in liver surgery, a human working with the machine beats the machine working alone. Last stated 4 weeks ago
10 Aug 2026
FL
Fei-Fei Li — holds since 2026-08-10 — tap for who they are
Same subject: Becoming a deep expert makes you useless, because what an expert is steeped in is the past. — tap to centre the map on it Becoming a deep expert makes you useless, because what an expert is steeped in is the past. Last stated a month ago
28 Jul 2026
BS
Blake Scholl — holds since 2026-07-28 — tap for who they are
Same subject: “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed. — tap to centre the map on it “Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed. Last stated a month ago
28 Jul 2026
BS
Blake Scholl — holds since 2026-07-28 — tap for who they are
Same subject: Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent. — tap to centre the map on it Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent. Last stated a month ago
31 Jul 2026
PC
Patrick Collison — holds since 2026-07-31 — 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 language model is not using language at all, because language requires an intention to communicate. — tap to centre the map on it A language model is not using language at all, because language requires an intention to communicate. Last stated 2 years ago
31 Aug 2024
TC
Ted Chiang — holds since 2024-08-31 — 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 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 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
Expertise is not passively absorbing patterns — it is learning what to look for, which makes becoming an expert a training of attention.
Holds CR Charan RanganathRead this korrent →
Expertise is an enormous accumulated library of small facts and skills, which is why no method can shortcut the sheer volume of learning it takes.
Holds SY Scott H. Young
Large language models are bad at chess: pattern recognition is not the same capability as searching one position very deeply.
Holds AC Albert Cheng
The knowledge a model soaks up in pre-training is holding it back; what we actually want is the intelligence with the knowledge stripped out.
Holds
Opening a fixed relational pattern takes two things: the implicit made explicit, and the person's own recoil from what they see.
Holds TR Terry Real
AI learns from patterns, so where the patterns are scarce, as in liver surgery, a human working with the machine beats the machine working alone.
Holds FL Fei-Fei Li
Becoming a deep expert makes you useless, because what an expert is steeped in is the past.
Holds BS Blake Scholl
“Work on what you know” is the worst advice there is: knowledge can be acquired, but what you love cannot be changed.
Holds BS Blake Scholl
Knowing something yourself will stay faster than asking a model for it, because a lookup in your own head beats a round trip to an agent.
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 GM Gary Marcus
A language model is not using language at all, because language requires an intention to communicate.
Holds TC Ted Chiang
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 SC Sean Carroll
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 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