Do LLMs “think” in a similar way to humans? Or is it totally different?
Maybe it’s a good idea to listen to someone who publishes papers on this very subject, and is a professor of both philosophy and psychiatry and directs an Institute for Cognitive Science. That person is Dr. Chandra Sripada and his insights are fascinating.
Sean Carroll (interviewer, scientist and science communicator) says this interview made him lean towards the answer being “yes, they think like humans” whereas previously he favored the opposite view.
That’s a very simplistic interpretation. A popular book that counters it is https://bookwyrm.social/book/29025/s/thinking-fast-and-slow
I can tell you didn’t listen to the podcast. A lot of it is about how System I / production systems are very similar to simple one-pass LLM outputs, and how Reasoning models closely match System II
Which BTW has very little directly to do with the notion that minimizing surprise – predicting the next thing – is the basis of much neurology and psychology.
I wouldn’t think that your advertising of the podcast is so successful as to make people listen to it.
But somehow you seem to be leaking the idea that reasoning models are something different than autoregressive LLMs. Yes, they do have different fine-tuning and system prompts, but little beyond that. Subbarao Kambhampati has worked a lot on this, e.g. https://doi.org/10.1111/nyas.15339 or https://doi.org/10.1111/nyas.15125