Good. It’s dangerous to view AI as magic. I’ve had to debate way too many people who think they LLMs are actually intelligent. It’s dangerous to overestimate their capabilities lest we use them for tasks they can’t perform safely. It’s very powerful but the fact that it’s totally non deterministic and unpredictable means we need to very carefully design systems that rely on LLMs with heavy guards rails.
I think it’s a big mistake to think that because the most basic LLMs are just autocompletes, or that because LLMs can hallucinate, that what big LLMs do doesn’t constitute “thinking”. No, GPT4 isn’t conscious, but it very clearly “thinks”.
It’s started to feel to me like current AIs are reasonable recreations of parts of our minds. It’s like they’re our ability to visualize, to verbalize, and to an extent, to reason (at least the way we intuitively reason, not formally), but separared from the “rest” of our thought processes.
Conversely, there are way too many people who think that humans are magic and that it’s impossible for AI to ever do <insert whatever is currently being debated here>.
I’ve long believed that there’s a smooth spectrum between not-intelligent and human-intelligent. It’s not a binary yes/no sort of thing. There’s basic inert rocks at one end, and humans at the other, and everything else gets scattered at various points in between. So I think it’s fine to discuss where exactly on that scale LLMs fall, and accept the possibility that they’re moving in our direction.
It’s not linear either. Brains are crazy complex and have sub cortexes that are more specialized to specific tasks. I really don’t think that LLMs alone can possibly demonstrate advanced intelligence, but I do think it could be a very important cortex for one. There’s also different types of intelligence. LLMs are very knowledgeable and have great recall but lack reasoning or worldview.
Indeed, and many of the more advanced AI systems currently out there are already using LLMs as just one component. Retrieval-augmented generation, for example, adds a separate “memory” that gets searched and bits inserted into the context of the LLM when it’s answering questions. LLMs have been trained to be able to call external APIs to do the things they’re bad at, like math. The LLM is typically still the central “core” of the system, though; the other stuff is routine sorts of computer activities that we’ve already had a handle on for decades.
IMO it still boils down to a continuum. If there’s an AI system that’s got an LLM in it but also a Wolfram Alpha API and a websearch API and other such “helpers”, then that system should be considered as a whole when asking how “intelligent” it is.
Not being combative or even disagreeing with you - purely out of curiosity, what do you think are the necessary and sufficient conditions of intelligence?
A worldview simulation it can use as a scratch pad for reasoning. I view reasoning as a set of simulated actions to convert a worldview from state a to state b.
It depends on how you define intelligence though. Normally people define it as human like, and I think there are 3 primary sub types of intelligence needed for cognizance, being reasoning, awareness, and knowledge. I think the current Gen is figuring out the knowledge type, but it needs to be combined with the other two to be complete.
Thanks! I’m not clear on what you mean by a worldview simulation as a scratch pad for reasoning. What would be an example of that process at work?
For sure, defining intelligence is non trivial. What clear the bar of intelligence, and what doesn’t, is not obvious to me. So that’s why I’m engaging here, it sounds like you’ve put a lot of thought into an answer. But I’m not sure I understand your terms.
