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Recent AI failures are cracks in the magic
(www.theintrinsicperspective.com)
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this post was submitted on 24 Feb 2024
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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.
Lol yup, some people think they're real smart for realizing how limited LLMs are, but they don't recognize that the researchers that actually work on this are years ahead on experimentation and theory already and have already realized all this stuff and more. They're not just making the specific models better, they're also figuring out how to combine them to make something more generally intelligent instead of super specialized.