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Comment by criddell | Hacker News Reader

Do AI researchers have a commonly accepted definition for intelligence?

Not really, AI is more of a large collection of activities. I would distill it down to "problem-solving".

The irony is that once you solve a problem, it's not a problem, so people's natural reaction is to only call things AI when they're unsolved. Once it's solved, "that's not AI".

Ability to solve novel problems with little experience. Skill acquisition efficiency.

On the Measure of Intelligence, François Chollet https://arxiv.org/abs/1911.01547

That's not really compatible with the history of AI, where designing systems to solve a problem has traditionally been considered in scope. Seems that, like many people, Chollet wants to think of AI in terms of AGI (artificial general intelligence).

Yes, that's what he focuses on. General intelligence, not narrow task-related intelligence. If a system uses too much experience (training samples) to learn a task then it's not intelligent. It just brute forces the problem. Intelligence requires quick learning from very little experience.

There is also no specification for "AI".

There isn't even an accepted definition of intelligence in humans, let alone animals, never mind machines! We're pretty much at the "I know it when I see it" stage of definition.

How can that be true? If the author is doing AI research presumably they and others in the same field have have some foundational shared ideas that they build upon. If they didn't how would a person earning a PhD in this field ever be able to say if they are contributing something new to the study of AI? There has to be something that defines the work as work in AI rather than just computer science.

You have correctly deduced why AI research is just a paper factory.

A large foundation for this field is statistical inference. To me, AI almost always means ML, and ML = algorithms that optimize themselves to make predictions.

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