LINK: AI Researcher Yann LeCun on AI function

Shmi

Yann LeCun, now of Facebook, was interviewed by The Register. It is interesting that his view of AI is apparently that of a prediction tool:

"In some ways you could say intelligence is all about prediction," he explained. "What you can identify in intelligence is it can predict what is going to happen in the world with more accuracy and more time horizon than others."

rather than of a world optimizer. This is not very surprising, given his background in handwriting and image recognition. This "AI as intelligence augmentation" view appears to be prevalent among the AI researchers in general.

Yann LeCun, now of Facebook, was interviewed by The Register. It is interesting that his view of AI is apparently that of a prediction tool:

Prediction cannot solve causal problems.

"ML person thinks AI is about what ML people care about. News at 11."

Ilya, I don't think it is very fair for you to bludgeon people with terminology / appeals to authority (as you do later in a couple of the sub-threads to this comment) especially given that causality is a somewhat niche subfield of machine learning. I.e. I think many people in machine learning would disagree with the implicit assumptions in the claim "probabilistic models cannot capture causal information". I realize that this is true by definition under the definitions preferred by causality researchers, but the assumption here seems to be that ... (read more)

5Eliezer Yudkowsky13y

Agreed. And search is not the same problem as prediction, you can have a big search problem even when evaluating/predicting any single point is straightforward.

1gjm13y

What counts as a causal problem? A sufficiently good predictor might be able to answer questions of the form "if I do X, what will happen thereafter?" and "if I do Y, what will happen thereafter?" even though what-will-happen-thereafter may be partly caused by doing X or Y. Is your point that (to take a famous example with which I'm sure you're already very familiar) in a world where the correlation between smoking and lung cancer goes via a genetic feature that makes both happen, if you ask the machine that question it may in effect say "he chose to smoke, therefore he has that genetic quirk, therefore he will get lung cancer"? Surely any prediction device that would be called "intelligent" by anyone less gung-ho than, say, Ray Kurzweil would enable you to ask it questions like "suppose I -- with my current genome -- chose to smoke; then what?" and "suppose I -- with my current genome -- chose not to smoke; then what?". I do agree that there are important questions a pure predictor can't help much with. For instance, the machine may be as good as you please at predicting the outcome of particle physics experiments, but it may not have (or we may not be able to extract from it in comprehensible form) any theory of what's going on to produce those outcomes.

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LINK: AI Researcher Yann LeCun on AI function

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LINK: AI Researcher Yann LeCun on AI function

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