My perspective on this is a combination of “basic theory is often necessary for knowing what the right formal tools to apply to a problem are, and for evaluating whether you’re making progress toward a solution” and “the applicability of Bayes, Pearl, etc. to AI suggests that AI is the kind of problem that admits of basic theory.” An example of how this relates to HRAD is that I think that Bayesian justifications are useful in ML, and that a good formal model of rationality in the face of logical uncertainty is likely to be useful in analogous ways. When I speak of foundational understanding making it easy to design the right systems, I’m trying to point at things like the usefulness of Bayesian justifications in modern ML. (I’m unclear on whether we miscommunicated about what sort of thing I mean by “basic insights”, or whether we have a disagreement about how useful principled justifications are in modern practice when designing high-reliability systems.)
Just planting a flag to say that I’m thinking more about this so that I can respond well.
Just planting a flag to say that I’m thinking more about this so that I can respond well.