Thinking of all the colleagues and friends I have who are connected to Brown University. What a devastating day for Brown, and for all of us.

🔍 What are the limits of interpretability in ML? Mech interp often stays at Marr’s algorithmic level but without the computational level (what the task is, what counts as the right solution) the mechanisms we find can look arbitrary. Why does a model learn one algorithm rather than another? 🧵 (1/2)

Anthropic has a great new piece on “Signs of introspection in large language models” 👉 www.anthropic.com/research/int... 🤔 Neat evidence that LLMs can report on manipulated activations, with big caveats! 🧠 But leaves open: what are the “internal states” an LLM can introspect in the first place?

This is a beautiful paper! The first third helpfully labels a stream of recent work in philosophy of AI as "propositional interpretability". The idea is to use propositional attitudes like belief, desire, and intention, to help explain AI in a way that we can understand. 1/n

"The AI risk repository, which includes over 700 AI risks grouped by causal factors (e.g. intentionality), and domains (e.g. discrimination), was born out of a desire to understand the overlaps and disconnects in AI safety research" #AIEthics techcrunch.com/2024/08/14/m...