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:

Thanks Ilya, that was a lot of useful context and I wasn't aware that causality was more in stats than ML. For the record, I think that causality is super-interesting and cool, I hope that I didn't sound too negative by calling it "niche" (I would have described e.g. Bayesian nonparametrics, which I used to do research in, the same way, although perhaps it's unfair to lump in causality with nonparametric Bayes, since the former has a much more distinguished history).

I agree with pretty much everything you say above, although I'm still confused about "you will need causal assumptions somewhere". If I could somehow actually do inference under the Solomonoff prior, do you think that some notion of causality would not pop out? I'd understand if you didn't want to take the time to explain it to me; I've had this conversation with 2 other causality people already and am still not quite sure I understand what is meant by "you need causal assumptions to get causal inferences". (Note I already agree that this is true in the context of graphical models, i.e. you can't distincuish between X->Y and X<-Y without do(X) or some similar information.)

Graphical models are only a "thing" because our brain dedicates lots of processing to vision, so, for instance, we immediately understand complicated conditional independence statements if expressed in the visual form of d-separation. In some sense, graphs in the context of graphical models do not really add any extra information mathematically that wasn't already encoded even without graphs.

Given this, I am not sure there really is a context for graphical models separate from the context of "variables and their relationships". What y... (read more)