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by djokkataja 793 days ago
I agree with Moravec. As he points out a bit later on:

> Only on the outside, where they can be appreciated as a whole, will the impression of intelligence emerge. A human brain, too, does not exhibit the intelligence under a neurobiologist's microscope that it does participating in a lively conversation.

We only have fuzzy definitions of "intelligence", not any essential, unambiguous things we can point to at a minute level, like a specific arrangement of certain atoms.

Put another way, we've used the term "intelligent" to refer to people (or not) because we found it useful to describe a complex bundle of traits in a simple way. But now that we're training LLMs to do things that used to be assumed to be exclusively the capacity of humans, the term is getting stretched and twisted and losing some of its usefulness.

Maybe it would be more useful to subdivide the term a bit by referring to "human intelligence" versus "LLM intelligence". And when some new developments in AI seem like they're different from "LLM intelligence", we can call them by whatever distinguishes them, like "Q* intelligence", for example.

2 comments

Agreed, kind of similar to the trope of 'virtual intelligences' vs 'artificial intelligences' in sci-fi, where VIs are a lot more similar to LLM intelligence (imitative, often unable to learn, able to make simple inferences and hold basic conversation but lacking that instantly recognizable 'spark' of an intelligent being that we can see in humans - especially kids - and some other animals) rather than AIs, which are 'true' intelligences comparing to or exceeding humans in every way.
> The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, concerning priors, experience, and generalization difficulty.

(Chollet, 2019, https://arxiv.org/pdf/1911.01547.pdf)

Priors here means how targeted is the model design to the task. Experience means how large is the necessary training set. Generalization difficulty is how hard is the task.

So intelligence is defined as ability to learn a large number of tasks with as little experience and model selection as possible. If it's a skill only possible because your model already follows the structure of the problem, then it won't generalize. If it requires too much training data, it's not very intelligent. If it's just a set number of skills and can't learn new ones quickly, it's not intelligent.

Your final paragraph is a poor definition of human level intelligence.

Yes, learning is an important aspect of human cognition. However, the key factor that humans possess that LLMs will never possess, is the ability to reason logically. That facility is necessary in order to make new discoveries based on prior logical frameworks like math, physics, and computer science.

I believe LLMs are more akin to our subconscious processes like image recognition, or forming a sentence. What’s missing is an executive layer that has one or more streams of consciousness, and which can reason logically with full access to its corpus of knowledge. That would also add the ability for the AI to explain how it reached a particular conclusion.

There are likely other nuances required (motivation etc.) for (super) human AI, but some form of conscious executive is a hard requirement.