Every AI-related invention is hyped as "intelligence" but turns out to be "Necessary but Not Sufficient" for true intelligence.

Neural networks are necessary but not sufficient. LLMs are necessary but not sufficient.

I have no doubt that there are multiple (perhaps thousands? more?) of LLM-like subsystems in our brains. They appear to be a necessary part of creating useful intelligence. My pet theory is that LLMs are used for associative memory purposes. They help generate new ideas and make predictions. They extract information buried in other memory. Clearly there is another system on top that tests, refines, and organizes the output. And probably does many more things we haven't even thought to name yet.

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Ferret7446 6 days ago | parent | next [-]

Most adult humans don't have "true intelligence" so I don't quite get the point

	▲	Jensson 6 days ago \| parent [-]
		What do you mean? Most adult humans can learn to drive a car, book a plain ticket, get a passport, fly abroad, navigate in a foreign country etc. There is a variation in human intelligence, but almost all humans are very intelligent compared to everything else we know about.

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JackFr 7 days ago | parent | prev [-]

> Every AI-related invention is hyped as "intelligence" but turns out to be "Necessary but Not Sufficient" for true intelligence.

Alternatively, the goalposts keep being moved.

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xenadu02 a day ago | parent | next [-]

I don't think we fully understand all the aspects of intelligence. What the potential feature set is. How to categorize or break it down into parts. We have some data and some categories but we are so far away from a full description that it only makes sense we must move the goalposts constantly.

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ezst 6 days ago | parent | prev [-]

Not really, only "merchants" are trying to package and sell LLMs as "artificial intelligence". To this day AI still very much is the name of a research field focused on computational methods: it's not a discovery, it's not a singular product or tool at or disposal (or it is in no greater capacity than Markov chains, support vector machines or other techniques that came before). If you ever expect the goalposts to settle, you are essentially wishing for research to stop.

	▲	ithkuil 6 days ago \| parent [-]
		Both things can be true: 1. People are trying to sell a product that is not ready and thus are overhyping it 2. The tech is in its early days and may evolve into something useful via refinement and not necessarily by some radical paradigm shift In order for (2) to happen it helps if the field is well motivated and funded (1)