"I plan to go back to posting about plain old rationality on Monday."
You praise Bayes highly and frequently. Yet you haven't posted a commensurate amount of material on Bayesian theory. I've read the Intuitive and Technical Explanation essays, and they made me think that you could write a really superb series on Bayesian theory.
Philosophers have written lots on a priori arguments for Bayesianism (e.g. Cox's Theorem, Dutch Book Arguments, etc.). I'm more curious about the fruitfulness of Bayesianism: e.g. what issues it clarifies and what interesting questions it brings to light. Here are some more specific questions:
What are some of the insights you've gained from Pearl's work on causal graphs and counterfactuals? How did reading Pearl change your views about certain topics? What are the insights from Pearl that have been most productive for you in your own thinking? What do you disagree with Pearl about?
What are some more practical examples of powerful applications of Bayesianism in AI? That Bayesianism is the correct normative theory of rationality doesn't imply that adopting a Bayesian framework will immediately yield big practical advantages in AI design. It might take people time to develop practical methods. How good are those methods? (I'm thinking, for example, about tractability, as well as the fact that many AI people over 40 won't have had so much early training on Bayes).
What areas of the Bayesian picture need development? What problems do you think cannot currently be given a very satisfying treatment in the Bayesian framework?
Given your ability, demonstrated in "Intuitive" and elsewhere, to not just tell people how to think about a topic but to get them thinking in the right way, a series on Bayesian that started elementary and built up could be very worthwhile.
"I plan to go back to posting about plain old rationality on Monday."
You praise Bayes highly and frequently. Yet you haven't posted a commensurate amount of material on Bayesian theory. I've read the Intuitive and Technical Explanation essays, and they made me think that you could write a really superb series on Bayesian theory.
Philosophers have written lots on a priori arguments for Bayesianism (e.g. Cox's Theorem, Dutch Book Arguments, etc.). I'm more curious about the fruitfulness of Bayesianism: e.g. what issues it clarifies and what interesting questions it brings to light. Here are some more specific questions:
What are some of the insights you've gained from Pearl's work on causal graphs and counterfactuals? How did reading Pearl change your views about certain topics? What are the insights from Pearl that have been most productive for you in your own thinking? What do you disagree with Pearl about?
What are some more practical examples of powerful applications of Bayesianism in AI? That Bayesianism is the correct normative theory of rationality doesn't imply that adopting a Bayesian framework will immediately yield big practical advantages in AI design. It might take people time to develop practical methods. How good are those methods? (I'm thinking, for example, about tractability, as well as the fact that many AI people over 40 won't have had so much early training on Bayes).
What areas of the Bayesian picture need development? What problems do you think cannot currently be given a very satisfying treatment in the Bayesian framework?
Given your ability, demonstrated in "Intuitive" and elsewhere, to not just tell people how to think about a topic but to get them thinking in the right way, a series on Bayesian that started elementary and built up could be very worthwhile.