Andrew Lampinen (@AndrewLampinen) / X

Andrew Lampinen

2,964 posts

Andrew Lampinen

@AndrewLampinen

Interested in cognition and artificial intelligence. MTS at @AnthropicAI. Previously @DeepMind, cognitive science @StanfordPsych. Tweets are mine.

Joined November 2019

Pinned
Andrew Lampinen
@AndrewLampinen
Dec 16, 2025
Why isn’t modern AI built around principles from cognitive science or neuroscience? Starting a new substack (link below) by writing down my thoughts on that question: as part of a first series of posts giving my current thoughts on the relation between these fields. 1/3
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Andrew Lampinen
@AndrewLampinen
Feb 11, 2023
Ted Chiang is a great writer, but this is not a great take and I'm disappointed to see it getting heavily praised. It's not in keeping with our scientific understanding of LMs or deep learning more generally. Thread: 1/n
John Burn-Murdoch
@jburnmurdoch
Feb 11, 2023
Ted Chiang’s piece on ChatGPT and large language models is as good as everyone says. The fact that the outputs are rephrasings rather than direct quotes makes them seem game-changingly smart — even sentient — but they’re just very straightforwardly not. newyorker.com/tech/annals-of…
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Andrew Lampinen
@AndrewLampinen
May 2, 2025
How do language models generalize from information they learn in-context vs. via finetuning? We show that in-context learning can generalize more flexibly, illustrating key differences in the inductive biases of these modes of learning — and ways to improve finetuning. Thread: 1/
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Andrew Lampinen
@AndrewLampinen
Jul 2, 2020
Officially done with my PhD! My dissertation is now online (purl.stanford.edu/xj689nb3522), if you fancy reading way too many pages about flexibility and transfer in humans and deep learning models.
Andrew Lampinen
@AndrewLampinen
Jul 21, 2025
Quick thread on the recent IMO results and the relationship between symbol manipulation, reasoning, and intelligence in machines and humans:
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Andrew Lampinen
@AndrewLampinen
Sep 22, 2025
Why does AI sometimes fail to generalize, and what might help? In a new paper, we highlight the latent learning gap — which unifies findings from language model weaknesses to agent navigation — and suggest that episodic memory complements parametric learning to bridge it. Thread:
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Andrew Lampinen
@AndrewLampinen
Feb 25, 2023
What is emergence, and why is it of recent interest in AI, and long-standing interest in cognitive science? And why is this an exciting time for considering emergence across these fields? A thread: 1/
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Andrew Lampinen
@AndrewLampinen
Jun 3, 2023
Computational physicists doing most their operations (e.g. derivatives) via an FFT because it's more efficient
Rob Miles
@robertskmiles
May 30, 2023
Replying to @robertskmiles
Their results are bizarre and inhuman. @NeelNanda5 trained a tiny transformer to do addition, then spent weeks figuring out what it was doing - one of the only times in history someone has understood how a transformer works. This is the algorithm it created. To *add two numbers*!
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Andrew Lampinen
@AndrewLampinen
Dec 22, 2023
Research in mechanistic interpretability and neuroscience often relies on interpreting internal representations to understand systems, or manipulating representations to improve models. I gave a talk at @unireps at NeurIPS on a few challenges for this area, summary thread: 1/
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Andrew Lampinen
@AndrewLampinen
May 26, 2023
What can be learned about causality and experimentation from passive data? What could language models learn from simply passively imitating text? We explore these questions in our new paper: “Passive learning of active causal strategies in agents and language models” Thread: 1/
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Andrew Lampinen
@AndrewLampinen
Sep 13, 2024
What aspects of human knowledge are vision models missing, and can we align them with human knowledge to improve their performance and robustness on cognitive and ML tasks? Excited to share this new work led by @lukas_mut! 1/10
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Andrew Lampinen
@AndrewLampinen
Sep 29, 2022
I'm not a scaling maximalist, but it's surprising to me how many people are 1) interested in differences between human and artificial intelligence and 2) think scaling to improve performance means deep learning is doing something fundamentally wrong. 1/n
Andrew Lampinen
@AndrewLampinen
Dec 10, 2024
What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve written a perspective that suggests interpreting a much broader spectrum of contextual behaviors as ICL! Summary thread: 1/8
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Andrew Lampinen
@AndrewLampinen
Jul 15, 2022
Abstract reasoning is ideally independent of content. Language models do not achieve this standard, but neither do humans. In a new paper (arxiv.org/abs/2207.07051 co-led by Ishita Dasgupta) we show that LMs in fact mirror classic human patterns of content effects on reasoning. 1/
arxiv.org
Language models show human-like content effects on reasoning tasks
Reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks, but exhibit many imperfections. However, human...