jacob_cannell above seems to think it is very important for physicists to know about Solomonoff induction.
Solomonoff induction is one of those ideas that keeps circulating here, for reasons that escape me.
If we are talking about Bayesian methods for data analysis, almost no one on LW who is breathlessly excited about Bayesian stuff actually knows what they are talking about (with 2-3 exceptions, who are stats/ML grad students or up). And when called on it retreat to the “Bayesian epistemology” motte.
Bayesian methods didn’t save Jaynes from being terminally confused about causality and the Bell inequalities.
I still haven’t figured out what you have against Bayesian epistemology. It’s not like this is some sort of LW invention—it’s pretty standard in a lot of philosophical and scientific circles, and I’ve seen plenty of philosophers and scientists who call themselves Bayesians.
Solomonoff induction is one of those ideas that keeps circulating here, for reasons that escape me.
My understanding is that Solomonoff induction is usually appealed to as one of the more promising candidates for a formalization of Bayesian epistemology that uses objective and specifically Occamian priors. I haven’t heard Solomonoff promoted as much outside LW, but other similar proposals do get thrown around by a lot of philosophers.
Bayesian methods didn’t save Jaynes from being terminally confused about causality and the Bell inequalities.
Of course Bayesianism isn’t a cure-all by itself, and I don’t think that’s controversial. It’s just that it seems useful in many fundamental issues of epistemology. But in any given domain outside of epistemology (such as causation or quantum mechanics), domain-relevant expertise is almost certainly more important. The question is more whether domain expertise plus Bayesianism is at all helpful, and I’d imagine it depends on the specific field. Certainly for fundamental physics it appears that Bayesianism is often viewed as at least somewhat useful (based on the conference linked by the OP and by a lot of other things I’ve seen quoted from professional physicists).
I don’t have any problem with Bayesian epistemology at all. You can have whatever epistemology you want.
What I do have a problem with is this “LW myopia” where people here think they have something important to tell to people like Ed Witten about how people like Ed Witten should be doing their business. This is basically insane, to me. This is strong evidence that the type of culture that gets produced here isn’t particularly sanity producing.
Solomonoff induction is useless to know about for anyone who has real work to do (let’s say with actual data, like physicists). What would people do with it?
In many cases I’d agree it’s pretty crazy, especially if you’re trying to go up against top scientists.
On the other hand, I’ve seen plenty of scientists and philosophers claim that their peers (or they themselves) could benefit from learning more about things like cognitive biases, statistics fallacies, philosophy of science, etc. I’ve even seen experts claim that a lot of their peers make elementary mistakes in these areas. So it’s not that crazy to think that by studying these subjects you can have some advantages over some scientists, at least in some respects.
Of course that doesn’t mean you can be sure that you have the advantage. As I said, probably in most cases domain expertise is more important.
Absolutely agree it is important for scientists to know about cognitive biases. Francis Bacon, the father of the empirical method, explicitly used cognitive biases (he called them “idols,” and even classified them) as a justification for why the method was needed.
I always said that Francis Bacon should be LW’s patron saint.
So it sounds like you’re only disagreeing with the OP in degree. You agree with the OP that a lot of scientists should be learning more about cognitive biases, better statistics, epistemology, etc., just as we are trying to do on LW. You’re just pointing out (I think) that the “informed laymen” of LW should have some humility because (a) in many cases (esp. for top scientists?) the scientists have indeed learned lots of rationality-relevant subject matter, perhaps more than most of us on LW, (b) domain expertise is usually more important than generic rationality, and (c) top scientists are very well educated and very smart.
edit: Although I should say LW “trying to learn better statistics” is too generous. There is a lot more “arguing on the internet” and a lot less “reading” happening.
jacob_cannell above seems to think it is very important for physicists to know about Solomonoff induction.
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.
Solomonoff induction is one of those ideas that keeps circulating here, for reasons that escape me.
Well, LW was born as a forum about artificial intelligence. Solomonoff induction is like an ideal engine for generalized intelligence, which is very cool!
Bayesian methods didn’t save Jaynes from being terminally confused about causality and the Bell inequalities.
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 :)
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.
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.
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.
A really smart physicist may be highly competent at say string theory, but know very little about french
pasteries or cuda programming or—more to the point—solomonoff induction.
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.
Since being rational or knowing about Bayesian epistemology is important in every field of science
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?
Solomonoff induction is like an ideal engine for generalized intelligence, which is very cool!
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.
we cannot ask of anyone, even geniuses, to transcend their time.
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.
Where is your confidence coming from, first principles?
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.
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.
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.
jacob_cannell above seems to think it is very important for physicists to know about Solomonoff induction.
Nah—I was just using that as an example of things physicists (regardless of IQ) don’t automatically know.
Most physicists were trained to think in terms of Popperian epistemology, which is strictly inferior to (dominated by) Bayesian epistemology (if you don’t believe that, it’s not worth my time to debate). In at least some problem domains, the difference in predictive capability between the two methodologies are becoming significant.
Physicists don’t automatically update their epistemologies, it isn’t something they are using to having to update.
Most physicists were trained to think in terms of Popperian epistemology, which is strictly inferior to (dominated
by) Bayesian epistemology (if you don’t believe that, it’s not worth my time to debate).
I equate “Bayesian epistemology” with a better approximation of universal inference. It’s easy to generate example environments where Bayesian agents dominate Popperian agents, while the converse is never true. Popperian agents completely fail to generalize well from small noisy datasets. When you have very limited evidence, popperian reliance on hard logical falsifiability just fails.
This shouldn’t even really be up for debate—do you actually believe the opposite position, or are you just trolling?
jacob_cannell above seems to think it is very important for physicists to know about Solomonoff induction.
Solomonoff induction is one of those ideas that keeps circulating here, for reasons that escape me.
If we are talking about Bayesian methods for data analysis, almost no one on LW who is breathlessly excited about Bayesian stuff actually knows what they are talking about (with 2-3 exceptions, who are stats/ML grad students or up). And when called on it retreat to the “Bayesian epistemology” motte.
Bayesian methods didn’t save Jaynes from being terminally confused about causality and the Bell inequalities.
I still haven’t figured out what you have against Bayesian epistemology. It’s not like this is some sort of LW invention—it’s pretty standard in a lot of philosophical and scientific circles, and I’ve seen plenty of philosophers and scientists who call themselves Bayesians.
My understanding is that Solomonoff induction is usually appealed to as one of the more promising candidates for a formalization of Bayesian epistemology that uses objective and specifically Occamian priors. I haven’t heard Solomonoff promoted as much outside LW, but other similar proposals do get thrown around by a lot of philosophers.
Of course Bayesianism isn’t a cure-all by itself, and I don’t think that’s controversial. It’s just that it seems useful in many fundamental issues of epistemology. But in any given domain outside of epistemology (such as causation or quantum mechanics), domain-relevant expertise is almost certainly more important. The question is more whether domain expertise plus Bayesianism is at all helpful, and I’d imagine it depends on the specific field. Certainly for fundamental physics it appears that Bayesianism is often viewed as at least somewhat useful (based on the conference linked by the OP and by a lot of other things I’ve seen quoted from professional physicists).
I don’t have any problem with Bayesian epistemology at all. You can have whatever epistemology you want.
What I do have a problem with is this “LW myopia” where people here think they have something important to tell to people like Ed Witten about how people like Ed Witten should be doing their business. This is basically insane, to me. This is strong evidence that the type of culture that gets produced here isn’t particularly sanity producing.
Solomonoff induction is useless to know about for anyone who has real work to do (let’s say with actual data, like physicists). What would people do with it?
In many cases I’d agree it’s pretty crazy, especially if you’re trying to go up against top scientists.
On the other hand, I’ve seen plenty of scientists and philosophers claim that their peers (or they themselves) could benefit from learning more about things like cognitive biases, statistics fallacies, philosophy of science, etc. I’ve even seen experts claim that a lot of their peers make elementary mistakes in these areas. So it’s not that crazy to think that by studying these subjects you can have some advantages over some scientists, at least in some respects.
Of course that doesn’t mean you can be sure that you have the advantage. As I said, probably in most cases domain expertise is more important.
Absolutely agree it is important for scientists to know about cognitive biases. Francis Bacon, the father of the empirical method, explicitly used cognitive biases (he called them “idols,” and even classified them) as a justification for why the method was needed.
I always said that Francis Bacon should be LW’s patron saint.
So it sounds like you’re only disagreeing with the OP in degree. You agree with the OP that a lot of scientists should be learning more about cognitive biases, better statistics, epistemology, etc., just as we are trying to do on LW. You’re just pointing out (I think) that the “informed laymen” of LW should have some humility because (a) in many cases (esp. for top scientists?) the scientists have indeed learned lots of rationality-relevant subject matter, perhaps more than most of us on LW, (b) domain expertise is usually more important than generic rationality, and (c) top scientists are very well educated and very smart.
Is that correct?
Yup!
edit: Although I should say LW “trying to learn better statistics” is too generous. There is a lot more “arguing on the internet” and a lot less “reading” happening.
I nominate Carneades, the inventor of the idea of degrees of certainty.
Harry J.E. Potter did receive Bacon’s diary as a gift from his DADA teacher, after all.
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?
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.
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.
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.
Nah—I was just using that as an example of things physicists (regardless of IQ) don’t automatically know.
Most physicists were trained to think in terms of Popperian epistemology, which is strictly inferior to (dominated by) Bayesian epistemology (if you don’t believe that, it’s not worth my time to debate). In at least some problem domains, the difference in predictive capability between the two methodologies are becoming significant.
Physicists don’t automatically update their epistemologies, it isn’t something they are using to having to update.
Heh, ok. Thanks for your time!
Ok, so I lied, I’ll bite.
I equate “Bayesian epistemology” with a better approximation of universal inference. It’s easy to generate example environments where Bayesian agents dominate Popperian agents, while the converse is never true. Popperian agents completely fail to generalize well from small noisy datasets. When you have very limited evidence, popperian reliance on hard logical falsifiability just fails.
This shouldn’t even really be up for debate—do you actually believe the opposite position, or are you just trolling?