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@andrewlampinen
Interested in cognition and artificial intelligence. MTS at @AnthropicAI. Previously @DeepMind, cognitive science @StanfordPsych. Tweets are mine.
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A user asks how models could learn to do this and points to a thread.
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A researcher says his work examines cases where model-written training data improves generalization, at least at later stages.
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The blogger argues that metaphors help people understand relationships and make predictions, starting with their role in language and cognition.
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A post invites current or graduated cognitive science or adjacent-field PhDs, or researchers with equivalent experience, to apply.
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A writer argues that agnosticism could reinforce existing prejudices and deepen epistemic closure amid uncertainty about AI consciousness.
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A user wonders what Gary Marcus thinks of the bet now and says they saved screenshots from a seemingly deleted thread.
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At a CiNeT/University of Osaka conference, a speaker suggested testing the idea by changing AI data or constraints.
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The authors of a COBS perspective piece suggest this framework offers a path to more precise modeling.
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A thread takes aim at arguments against language models learning meaning, saying their authors leave terms undefined and fail to engage with contrary evidence.
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A user called current AI “definitely not ASI”—artificial superintelligence—saying image generation had been “dumb as bricks” during an attempt.
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A sarcastic post challenges the dismissal of experienced researchers while pundits citing thousands of LessWrong posts are treated as experts.
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One post argues that calling LLMs incapable of thought is unscientific without a rigorous definition of thinking and evidence that their mechanisms fundamentally differ from human cognition.
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Gebru says her own work has been cited in AI policy, questioning why she would call for a pause.
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Chollet posted that million-line codebases calling neural models at inference qualify as neurosymbolic.
