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.
It seems to me that a sufficiently smart prediction machine could answer questions of this kind. E.g., suppose what it really is is a very fast universe simulator. Simulate a lot of patients, diddle with their environments, either give each one the drug or not, repeat with different sets of parameters. I'm not actually recommending this (it probably isn't possible, it produces interesting ethical issues if the simulation is really accurate, etc.) but the point is that merely being a predictor as such doesn't imply inability to answer causal questions.
Was Yann LeCun saying (1) "AI is all about prediction in the ordinary informal sense of the word" or (2) "AI is all about prediction in the sense in which it's discussed formally in the machine learning community"? I thought it was #1.
Simulations (and computer programs in general -- think about how debuggers for computer programs work) are causal models, not purely predictive models. Your answer does no work, because being able to simulate at that level of fidelity means we are already Done with the science of what we are simulating. In particular our simulator will contain in it a very detailed causal model that would contain answers to everything we might want to know. The question is what do we do when our information isn't very good, not when we can j... (read more)