tag:blogger.com,1999:blog-8429570072441023296.post2742093117603168814..comments2023-09-07T06:36:59.520-04:00Comments on The Virtual Philosophy Club: Bayesian AI Advisor - Drill Here? Drill Now?Ira Glicksteinhttp://www.blogger.com/profile/10800080810596424897noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-8429570072441023296.post-34861169229018676492009-04-10T23:42:00.000-04:002009-04-10T23:42:00.000-04:00Thanks for your comments and I agree that once an ...Thanks for your comments and I agree that once an "AI" problem is solved it is an algorithm. However one of my Profs at Binghamton U. was a "computationalist" and claimed that our brains when functioning as thinking machines with input-process-output were also just (very complex) algorithms (based on the Turing idea of the Universal Computer).<BR/><BR/>Your example of using a Bayesian algorithm for sorting products according to the species of wood is a job that, until a couple decades ago, absolutely had to be done by a human. Now it could be done by a computer using an algorithm, and faster and more accurately and also cheaper.<BR/><BR/>Of course, in the case of the computer decision making, someone had to program the computer with the Bayes algorithm and much more. On the other hand, in the olden days when a human did the sorting, someone had to explain to him how to recognize different species of tree (and before he reported to work he had to learn language and may other things as he was growing up). <BR/><BR/>Of course, the computer can only do the specific job programmed (sorting wood), and could not, for example write poetry or make love to a woman, etc. But neither can some humans!<BR/><BR/>Ira GlicksteinIra Glicksteinhttps://www.blogger.com/profile/10800080810596424897noreply@blogger.comtag:blogger.com,1999:blog-8429570072441023296.post-29398194247883567092009-04-09T18:21:00.000-04:002009-04-09T18:21:00.000-04:00Thanks Ira. That was an interesting explanation o...Thanks Ira. That was an interesting explanation of the Bayesian approach. The fact that you call it the "AI" tutor, reminds me of the fact that one of my AI friends used to say that a problem is artificial intelligence until you solve it. Then it's just an algorithm.<BR/><BR/>I used to teach some of this decision making stuff in a robotics course. An interesting application is in mixed products on an assembly line. Suppose a single assembly line factory makes plywood from two different species of wood. Only when the sheets have passed through the entire process are they segregated into packages labelled according to species. The automated equipment measures the brightness or reflectance of the wood in order to "guess" the species. If we make an additional automatic measurement of the coarseness of the grain, the machine can make a much more accurate composite guess using Bayes' approach to conditional probability. What is important in this method is that we have data on the probability of any given brightness for each of the wood species and the probability of any given coarseness for each of the species. What is absolutely crucial is that we have the "a priori" probability of each of the species occurring. (It many real world cases this is not available.) In the tee-shirt case one has this data from the bookstore sales. In the plywood factory, one has the number or trees of each type that enter the factory. I like to say that we know the "parent distribution." <BR/><BR/>None of this actually addresses the intelligent part of the decision making problem. Someone has to set goals. One might minimize error, because customers get upset when we deliver the wrong species. One can maximize short run profit. One can maximize long term profit. In short, one needs a business model and/or a corporate ethos. The same is true in your oilwell model, only it's hidden behind the "return on investment" factor. That has to come from humans based upon such psychological factors such as level of greed, tolerance to risk and whether or not they have a happy marriage and their feelings about the stability of the Middle-East.<BR/><BR/>My point is that algorithms are essential, but results most often depend upon blind faith and intuition about things beyond our ken.<BR/><BR/>With respect -Joeljoelhttps://www.blogger.com/profile/08770806025343971171noreply@blogger.com