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MrMind comments on Open thread, Dec. 21 - Dec. 27, 2015 - Less Wrong Discussion
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I think a more charitable read would go like this: being smarter doesn't necessarily mean that you know everything there's to know nor that you are more rational than other people. Since being rational or knowing about Bayesian epistemology is important in every field of science, physicists should be motivated to learn this stuff. I don't think he was suggesting that French pastries are literally useful to them.
Well, LW was born as a forum about artificial intelligence. Solomonoff induction is like an ideal engine for generalized intelligence, which is very cool!
That's unfortunate, but we cannot ask of anyone, even geniuses, to transcend their time. Leonardo da Vinci held some ridiculous beliefs, for our standars, just like Ramanujan or Einstein. With this I'm not implying that Jaynes was a genius of that caliber, I would ascribe that status more to Laplace. On the 'bright' side, in our time nobody knows how to reconcile epistemic probability and quantum causality :)
That seems to be a pretty big claim. Can you articulate why you believe it to be true?
Because AIXI is the first complete mathematical model of a general AI and is based on Solomonoff induction.
Also, computable approximation to Solomonoff prior has been used to teach small AI to play videogames unsupervised.
So, yeah.
As far as I am aware, Solomonoff induction describes the singularly correct way to do statistical inference in the limits of infinite compute. (It computes generalized/full Bayesian inference)
All of AI can be reduced to universal inference, so understanding how to do that optimally with infinite compute perhaps helps one think more clearly about how practical efficient inference algorithms can exploit various structural regularities to approximate the ideal using vastly less compute.
If you don't consider Jaynes to be comtemporary, which author do you consider to be his successor that updated where Jaynes went wrong?
While Bretthorst is his immediate and obvious successor, unfortunately nobody that I know of has taken up the task to develop the field the way Jaynes did.
I am pretty sure jacob_connell specifically brought up Solomonoff induction. I am still waiting for him to explain why I (let alone Ed Witten) should care about this idea.
How do you know what is important in every field of science? Are you a scientist? Do you publish? Where is your confidence coming from, first principles?
Whether Solomonoff induction is cool or not is a matter of opinion (and "mathematical taste,") but more to the point the claim seems to be it's not only cool but vital for physicists to know about. I want to know why. It seems fully useless to me.
Jaynes died in 1997. Bayesian networks (the correct bit of math to explain what is going on with Bell inequalities) were written up in book form in 1988, and were known about in various special case forms long before that.
???
Well, yes of course. Cox' theorem. Journals are starting to refute papers based on the "p<0.05" principle. Many studies in medicine and psychology cannot be replicated. Scientists are using inferior analysis methods when better are available just because they were not taught to.
I do say there's a desperate need to divulge Bayesian thinking.
I wasn't referring to that. Jaynes knew that quantum mechanics was incompatible with the epistemic view of probability, and from his writing, while never explicit, it's clear that he was thinking about a hidden variables model.
Undisputable violation of the Bell inequalities were performed only this year. Causality was published in 2001. We still don't know how to stitch epistemic probabilities and quantum causality.
What I'm saying is that the field was in motion when Jaynes died, and we still don't know a large deal about it. As I said, we cannot ask anyone not to hold crazy ideas from time to time.
Datapoint: in [biological] systematics in its broadest sense, Bayesian methods are increasingly important (molecular evolution studies,...), but I've never heard about pure Bayesian epistemology being in demand. Maybe because we leave it all to our mathematicians.
Part of the issue I keep harping about is people keep confusing Bayes rule, Bayesian networks, Bayesian statistical inference, and Bayesian epistemology. I don't have any issue with a thoughtful use of Bayesian statistical inference when it is appropriate -- how could I?
My issue is people being confused, or people having delusions of grandeur.