Even more fundamental than science, there is missing philosophy, both in us regarding these systems, and in the systems themselves. An AGI implemented by an LLM needs to, at the minimum, be able to self-learn by updating its weights, self-finetune, otherwise it quickly hits a wall between its baked-in weights and finite context window. What is the optimal "attention" mechanism for choosing what to self-finetune with, and with what strength, to improve general intelligence? Surely it should focus on reliable academics, but which academics are reliable? How can we reliably ensure it studies topics that are "pure knowledge", and who does it choose to be, if we assume there is some theoretical point where it can autonomously outpace all of the world's best human-based research teams?
Well,
Original 80s AI was based on mathematical logic. And while that might not encompass all philosophy, it certainly was a product of philosophy broadly speaking - some analytical philosophers could endorse. But it definitely failed and failed because it could process uncertainty (imo). I think also if you closely, classical philosophy wasn't particularly amenable to uncertainty either.
If anything, I would say that AI has inherited its failure from philosophy's failure and we should look to alternative approaches (from Cybernetics to Bergson to whatever) for a basis for it.
Quote: The study focused on two activities: reading for pleasure (reading a book, newspaper, magazine, reading on electronic devices and listening to audiobooks) and reading with children.