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.
One activity occurs in a hazily understood chemical system with near completely unobservable states in interplay with activities and systems which have effects on the main system that are barely recognized, much less fully understood. The other is a constructed, completely defined system with near total observability of states affected by probabilities based on inputs defined by human written, completely known tokenization systems.
Will they next speculate about the similarity of ‘preferences’ between a robot from Japan and the nearest non-terrestrial intelligent species?
Yes, this is more or less the counter argument. If you are familiar with Anil Seth this seems to be his basis as well.
IMO it’s basically arguing that we can’t know something because it’s really complex. Historically those problems do tend to get solved with tech and cleverness.
Yes and no of course. Defined and observable, but profoundly difficult to interpret or understand.
I find it interesting that what known about mechanistic interpretability is partially the result of work done in using LLMs to interpret human brain scans – scanning LLM layers to match active features.
You can look at a pair of meshed gears and understand it just as readily as you can understand a single transformation by running the math on paper with a pencil, but I don’t think anyone will ever be able to comprehend the fullness of any useful-scale model for the same reason they would never comprehend a clockwork with the same number of gears as an LLM has transformers or indices. It’s a non-intuitable space with too much complexity which cannot be simplified without oversimplifying.