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.
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 it's more natural to make causality an ontologically fundamental aspect of the model, whereas it's far from clear to me that this is the most natural thing to do (i.e. you can imagine learning about causality as a feature of the environment). In essence, you are asserting that "do" is an ontologically fundamental notion, but I personally think of it as a notion that just happens to be important enough to many of the prediction tasks we care about that we hard-code it as a feature of the model, and supply the causal information by hand. I suspect the people you argue with below have similar intuitions but lack the terminology to express them to your satisfaction.
I'll freely admit that I'm not an expert on causality in particular, so perhaps some of what I say above is off-base. But if I'm also below the bar for respectful discourse then your target audience is small indeed.
[ Upvoted. ]
If anyone felt I was uncivil to them in any subthread, I hereby apologize here.
I am not sure causality is a subfield of ML in the sense that I don't think many ML people care about causality. I think causal inference is a subfield of stats (lots of talks with the word "causal" at this year's JSM). I think it's weird that stats and ML are different fields, but that's a separate discussion.
I think it is possible to formalize causality without talking about interventions as Pearl et al. thinks of them, for example people in reinfor... (read more)