I think that bayes with resource constraints looks a lot like toolbox-ism.
Take for example the problem of reversing MD5 hashes. You could bayesianly update your probabilities of which original string goes to which hash, but computationally there is no uncertainty so there is no point storing probabilities, so you strip those out. Or you could just download the tool of a rainbow table and use that and not have to compute the hashes yourself at all.
Or having to compute the joint probability of two sets of data that comes in at different times. Say you always have the event A at time T, but what happened to B at T can come in immediately or days to weeks later. You might not be able to store all the information about A if it is happening quickly (e.g. network interface activity). So you have to chose which data to drop. Perhaps you age it out after a certain time or drop things that are usual, but they still stop you being able to bayesianly update properly when that B comes in. Bayes doesn’t say what you should drop. You should drop the unimportant stuff, but figuring out what is unimportant a priori seems implausible.
So I still use bayes upon occasion when it makes sense and still think AI could be highly risky. But I think I’m a toolbox-ist.
I agree. In particular I’ve noticed that a lots of frequentist methods can be described in terms of the Bayesian method by replacing a step where you take the expected value of some quantity by instead taking the worst case of this quantity. This improves the computational efficiency by avoiding a difficult integral. Of course we choose the worst case rather than the best case because humans are risk averse. This shows an interesting fact: Bayesian methods are the same for every agent, but when resource constraints force you away from Bayesian methods your inferences can end up depending on your utility function. (And because humans are risk averse the frequentist method often looks like it is being more responsible and conservative than the Bayesian method even though the Bayesian method will in fact always produce predictions with an optimal amount of risk-averseness if you use it with an accurate utility function.)
I think that bayes with resource constraints looks a lot like toolbox-ism.
Take for example the problem of reversing MD5 hashes. You could bayesianly update your probabilities of which original string goes to which hash, but computationally there is no uncertainty so there is no point storing probabilities, so you strip those out. Or you could just download the tool of a rainbow table and use that and not have to compute the hashes yourself at all.
Or having to compute the joint probability of two sets of data that comes in at different times. Say you always have the event A at time T, but what happened to B at T can come in immediately or days to weeks later. You might not be able to store all the information about A if it is happening quickly (e.g. network interface activity). So you have to chose which data to drop. Perhaps you age it out after a certain time or drop things that are usual, but they still stop you being able to bayesianly update properly when that B comes in. Bayes doesn’t say what you should drop. You should drop the unimportant stuff, but figuring out what is unimportant a priori seems implausible.
So I still use bayes upon occasion when it makes sense and still think AI could be highly risky. But I think I’m a toolbox-ist.
I agree. In particular I’ve noticed that a lots of frequentist methods can be described in terms of the Bayesian method by replacing a step where you take the expected value of some quantity by instead taking the worst case of this quantity. This improves the computational efficiency by avoiding a difficult integral. Of course we choose the worst case rather than the best case because humans are risk averse. This shows an interesting fact: Bayesian methods are the same for every agent, but when resource constraints force you away from Bayesian methods your inferences can end up depending on your utility function. (And because humans are risk averse the frequentist method often looks like it is being more responsible and conservative than the Bayesian method even though the Bayesian method will in fact always produce predictions with an optimal amount of risk-averseness if you use it with an accurate utility function.)