Please don't say "Bayesian model" when you mean "Bayesian network."
I do not mean "Bayesian networks". I mean Bayesian models of the kind e.g. described in Gelman's Bayesian Data Analysis.
p(A, B(a=1), B(a=0)) where B(a=1) means 'random variable B under intervention do(a=1)'. Assume binary A for simplicity here.
You still can express this as plain-vanilla conditional densities, can't you? "under intervention do(a=1)" is just a different way of saying "conditional on A=1", no?
A causal model over A,B is a set of densities { p(A, B(a=1), B(a=0) | [ some property ] }
and
with no mention of counterfactuals or interventions anywhere.
I don't see counterfactuals in your set of densities and how "interventions" are different from conditionality?
You still can express this as plain-vanilla conditional densities, can't you?
No. If conditioning was the same as interventions I could make it rain by watering my lawn and become a world class athlete by putting on a gold medal.
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.