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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- 7d ago
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
Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazine quantamagazine.org
On the generalization of language models from in-context learning and finetuning: a controlled study arxiv.org
Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences arxiv.org
Passive learning of active causal strategies in agents and language models proceedings.neurips.cc
Conceptual Reasoning Fellow job-boards.greenhouse.io
Math, research, and meaning in the age of AI infinitefaculty.substack.com
What are the real problems of continual learning? infinitefaculty.substack.com
Symbols, neural networks, and mathematical intelligence infinitefaculty.substack.com
In defense of metaphor infinitefaculty.substack.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
→
- 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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(Article I'm commenting on, btw: www.quantamagazine.org/is-ai-reason...)
and/or by reducing those elements down to simpler small settings where everything is controlled. E.g., some of our work taking those two paths to studying relational generalization (arxiv.org/abs/2505.00661 and arxiv.org/abs/2509.16189) or causal reasoning (proceedings.neurips…
and/or by reducing those elements down to simpler small settings where everything is controlled. E.g., some of our work taking those two paths to studying relational generalization (arxiv.org/abs/2505.00661 and arxiv.org/abs/2509.16189) or causal reasoning (proceedings.neurips…
and/or by reducing those elements down to simpler small settings where everything is controlled. E.g., some of our work taking those two paths to studying relational generalization (arxiv.org/abs/2505.00661 and arxiv.org/abs/2509.16189) or causal reasoning (proceedings.neurips…
job-boards.greenhouse.io/anthropic/jo...
New post reflecting on recent AI progress in math, how AI is changing the way we work, and some worries about where people will find meaning as they offload more of their work to AI: infinitefaculty.substack.com/p/math-resea...
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...
I argue that describing LMs as having goals, beliefs etc. clearly meets that bar. Even if the metaphors are not always perfect, that doesn't mean we have to discard them entirely. Instead, we can use them in ways that acknowledge their imperfections. infinitefaculty.substack.c…
Their own posts
Recent commentary
New blog post! I wrote about why I think it's useful to use metaphorical (and anthropomorphic) terms to describe AI. I start by describing the role of metaphor in language and cognition, and how metaphors can help us to efficiently understand relational structure and make predictions 1/2
I gave a talk yesterday at a CiNeT / University of Osaka conference about AI & neuroscience. I started with this question — given that AI (especially language models) and brains are so different in structure and environment, how could they have anything to do with one another? 1/
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