We've updated the preprint of our Naturalistic Computational Cognitive Science paper (arxiv.org/abs/2502.20349) — we've tried to clarify and streamline the arguments, and added some new examples: 1/5
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Andrew Lampinen
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Researcher with public evidence across Models & releases, AI research, Culture, work & education.
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Interested in cognition and artificial intelligence. Researcher at Anthropic; previously DeepMind, cognitive science at Stanford. Posts are mine.
lampinen.github.io
What they're sharing
[2502.20349] Naturalistic Computational Cognitive Science: Towards generalizable models and theories that capture the full range of natural behavior arxiv.org
[2605.20529] Collocational bootstrapping: A hypothesis about the learning of subject-verb agreement in humans and neural networks arxiv.org
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Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible arxiv.org
What are the real problems of continual learning? infinitefaculty.substack.com
Symbols, neural networks, and mathematical intelligence infinitefaculty.substack.com
Episodes | Stanford Psychology Podcast stanfordpsychologypodcast.com
Articles & links
Andrew Lampinen reposted
🤖🧠NEW PAPER🧠🤖 Children & neural networks can learn syntax from linear strings of words. How do they do it? Our hypothesis: Word co-occurrence statistics provide cues to syntax! (I.e., a new type of bootstrapping to consider!) Paper: arxiv.org/abs/2605.20529 1/n
Andrew Lampinen reposted
To appear in Computational Brain & Behavior soon: the claimed 2024 proof (also in CBB) that AGI via learning is intractable also "proves" that ImageNet is intractable. My reading of the hole: equivocation on the variable D. Preprint here: arxiv.org/abs/2411.06498
AI Weekly's analysis
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- Guerzhoy argues van Rooij et al.'s 2024 proof that AGI via machine learning is intractable rests on an unjustified assumption about data distributions.
- The same proof structure, applied consistently, would show ImageNet classification is intractable, yet that task demonstrably works.
- Three barriers block any such proof: defining human-like behavior precisely, accounting for inductive bias, and specifying relevant data subsets.
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What are the real problems to be solved in continual learning? In my latest post, I tackle this question — reviewing where I think the field went astray in the past, how language models changed things, and where the real challenges remain. infinitefaculty.substack.com/p/what-a…
What should we make of recent language model advancements in mathematics? In my new post, I reflect on long-standing cognitive debates about symbols and neural networks in light of this progress. infinitefaculty.substack.com/p/symbols-ne...
You can find it here if you're interested! www.stanfordpsychologypodcast.com/episodes/epi...
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