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Excited to announce a new book telling the story of mathematical approaches to studying the mind, from the origins of cognitive science to modern AI! The Laws of Thought will be published in February, and is available for pre-order now.
Griffiths Computational Cognitive Science Lab
211 posts
Griffiths Computational Cognitive Science Lab
@cocosci_lab
Tom Griffiths' Computational Cognitive Science Lab. Studying the computational problems human minds have to solve.
- (1/5) Very excited to announce the publication of Bayesian Models of Cognition: Reverse Engineering the Mind. More than a decade in the making, it's a big (600+ pages) beautiful book covering both the basics and recent work: mitpress.mit.edu/9780262049412/…
- A computational theory of Aha! moments! New preprint from our lab on why Aha! moments occur and why they feel so good. TLDR: They are a form of meta-cognitive prediction errors i.e. they occur when we surprise ourselves about our own abilities! psyarxiv.com/c5v42
- Does the success of deep neural networks in creating AI systems mean Bayesian models are no longer relevant? Our new paper argues the opposite: these approaches are complementary, creating new opportunities to use Bayes to understand intelligent machines
- It only took a global pandemic, but the Princeton CoCoSci Lab is now on Twitter. Hello World!
- Habituation and comparisons can result in depression, materialism, and overconsumption. Why are these disruptive features even part of human cognition? New paper with Rachit Dubey and Peter Dayan in @PLOSCompBiol provides a RL perspective on this question
- Princeton is hiring postdocs interested in projects focused on comparing AI systems to human cognition or using ideas from cognitive science to better understand those systems as part of a new initiative studying Natural and Artificial Minds. Apply at puwebp.princeton.edu/AcadHire/posit…
- This paper uses metalearning to distill a Bayesian prior into a set of initial weights for a neural network, providing a way to create networks with interpretable soft inductive biases. The resulting networks can learn just as quickly as a Bayesian model when applied to new data.🤖🧠Paper out in Nature Communications! 🧠🤖 Bayesian models can learn rapidly. Neural networks can handle messy, naturalistic data. How can we combine these strengths? Our answer: Use meta-learning to distill Bayesian priors into a neural network! nature.com/articles/s4146… 1/n
- Come join us! New postdoctoral position in computational cognitive science, with specific interest in applications of large language models in cognitive science and use of Bayesian methods and metalearning to understand human cognition and AI systems. princeton.edu/acad-positions…
- The new Princeton AI Lab has positions for AI Postdoctoral Research Fellows for three research initiatives: AI for Accelerating Invention, Natural and Artificial Minds, and Princeton Language and Intelligence. More information here:
- 1/7 Happy to share our new paper! "Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo" arxiv.org/pdf/2401.16657…
- New preprint! In-context and in-weights learning are two interacting forms of plasticity, like genetic evolution and phenotypic plasticity. We use ideas from evolutionary biology to predict when neural networks will use each kind of learning.(1/11) Evolutionary biology offers powerful lens into Transformers learning dynamics! Two learning modes in Transformers (in-weights & in-context) mirror adaptive strategies in evolution. Crucially, environmental predictability shapes both systems similarly.
- In this new paper we apply a cognitive science approach to large language models like GPT-4. Focusing on the problem LLMs were trained to solve - predicting the next word - reveals a lot about their behavior. Simple tasks can result in errors when they push against this training.🤖🧠NEW PAPER🧠🤖 Language models are so broadly useful that it's easy to forget what they are: next-word prediction systems Remembering this fact reveals surprising behavioral patterns: 🔥Embers of Autoregression🔥 (counterpart to "Sparks of AGI") arxiv.org/abs/2309.13638 1/8
- Replying to @cocosci_lab(4/5) Here's the table of contents. An Open Access version of the book is available through the MIT Press website.
