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:

I agree with pragmatist's explanation. But let me add a bit more detail to illustrate that a temporal ordering will not save you here. Imagine instead of two variables we have three variables : rain (R), my grass being wet (G1), and my neighbor's grass being wet (G2). Clearly R preceeds both G1, and G2, and G1 and G2 are contemporaneous. In fact, we can even consider G2 to be my neighbor's grass 1 hour in the future (so clearly G1 preceeds G2!).

Also clearly, p(R = yes | G1 = wet) is high, and p(R = yes | G2 = wet) is high, also p(G1 = wet | R = yes) is high, and p(G2 = wet | R = yes) is high.

So by hosing my grass I am making it more likely than my neighbor's grass one hour from now will be wet?

Or, to be more succinct : http://www.smbc-comics.com/index.php?db=comics&id=1994#comic

Yeah, well, I've heard somewhere that correlation does not equal causation :-)

I agree that causal models are useful -- if only because they make explicit certain relationships which are implicit in plain-vanilla regular models and so trip up people on a regular basis.What I'm not convinced of is that you can't re-express that joint density on the outcomes in a conventional way even if it turns out to look a bit awkward.