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@noahdgoodman
humans& co-founder. professor of natural and artificial intelligence @Stanford. (@StanfordNLP @StanfordAILab) ex: Google DeepMind, UberAI
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Humans& says developers can access the model through its Agents API without joining a waitlist.
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A user posted 👀 with a photo link and a quote of another post on October 19.
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In a reply, a commenter suggests discussing deception alongside loss of monitorability and recommends a paper on monitoring.
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The author says they're preparing a lecture for their class and invites suggestions from friends in AI safety.
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Gary Marcus says he never attacked Noah Goodman personally, though he critiqued some of Goodman's work a decade ago.
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One post says Dominik's result looked more convincing than expected; another praises novel ideas rather than just scaling.
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Eric Zelikman argues regex-based and statistical signals can help, but should not become headline results.
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A researcher introducing “Self-Play Pretraining with Zero Data” says two models start from random initialization: a generator proposes programs, and a learner trains on their outputs.
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The player sharing the run called it a “startling achievement,” saying they have played a lot of NetHack but never won themselves.
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The paper’s authors say MAttr uses gradient descent to find which parts of a neural network are responsible for a behavior. They report a benchmark result of 2.9× the runner-up.
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Co-founder Andi Peng departs humans& after helping build the human-centric AI lab.
