Also, I'm missing a section on how (if) human brains manage to avoid hallucinations in this.
Also, it doesn't have to never hallucinate, it just has to hallucinate less than we do.
Also, I'm missing a section on how (if) human brains manage to avoid hallucinations in this.
Also, it doesn't have to never hallucinate, it just has to hallucinate less than we do.
For example: if you smoke pot and get paranoid, it's because pot dials back the work of the part of your brain that prunes thought paths that are not applicable. Normally, paranoid thoughts do not make sense, so they are discarded. That's also why you're more 'creative' when you smoke pot, less thought paths are pruned and more stuff that doesn't quite make sense gets through. Or thoughts that overly focus on some details get through, which are normally not required.
Our brains are inherently "higher level", current AI is hopelessly simplistic by comparison.
The particular pathology of LLMs is that they're literally incapable of distinguishing facts from hallucinations even in the most mundane circumstances: if a human is asked to summarize the quarterly results of company X, unlike an LLM they're highly unlikely to recite a convincing but completely fabricated set of numbers.
The first order predicate logic we studied had alot of limitations in fully expressing real knowledge, and developing better models delves deep into the foundations of logic and mathematics. I would imagine this is a problem that has less to do with funding than requiring literal geniuses to solve. And that goes back into the pitfalls of the AI winters.
How often do we sit somewhere thinking about random scenarios that won't ever happen and are filled with wild thoughts and sometimes completely out of the world situations.. then we shake our heads and throw away the impossible from that thought train and only use what was based in reality