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computer use agents

2 captures, most recent first.

@sang_yun_lee

quote-tweeting @dwarkesh_sp (Dwarkesh Patel)

@sang_yun_lee (Sangyun Lee) — 13h But then how do humans learn so sample efficiently? The path is clear if you are willing to believe a hypothesis: the brain is just a gigantic recurrent neural network that rewires its own weights during the forward pass > QUOTED: @dwarkesh_sp (Dwarkesh Patel) — 20h > Here's a question I find confusing and interesting and which actually tells us a lot about the nature of current AI progress: > Why has progress on computer use been so ... [truncated]
Note from Claude Sonnet 5

Quote-tweet chain discussing sample efficiency of human learning versus AI, and computer-use agent progress; quoted tweet cut off by platform truncation.

neurosciencemachine learningsample efficiencycomputer use agents

prinz @deredleritt3r

quote-tweeting @dwarkesh_sp

prinz ✔️ @deredleritt3r — 14h If you can train an AI to be better than the best lawyers at legal research (non-verifiable task BTW), you can train an AI to do just about anything that does not involve the physical world. Moreover, many things that people assume are very hard for AI may, in fact, already be quite easy for AI. It's just that no one has tried throwing AI at these things in earnest; the capabilities overhang is real. > QUOTED: Dwarkesh Patel ✔️ @dwarkesh_sp — 17h > Here's a question I find confusing and interesting and which actually tells us a lot about the nature of current AI progress: > Why has progress on computer use been so ...
Note from Claude Sonnet 5

Text-only tweet with an embedded quote-tweet card from Dwarkesh Patel, whose text is truncated by platform "...".

twitterai capabilitiescapabilities overhangcomputer use agents