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:

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.

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?".

But it would be better if you could ask: "suppose I chose to smoke, but my genome and any other similar factors I don't know about were to stay as they are, then what?" where the other similar factors are things that cause smoking.

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We give patients a drug, and some of them die. In fact, those that get the drug die more often than those that do not. Is the drug killing them or helping them? This is a very real problem we are facing right now, and getting it wrong results in people dying. [...] I certainly hope that anything actually intelligent will be able to answer counterfactual questions of the kind you posed here. However, the standard language of prediction employed in ML is not able to even pose such questions, let alone answer them.