At one point in the recent past, I had relegated the concept of "clustering" to the narrow AI domain. But at around the same time, I was attempting to wrap my head around the problem of hidden variables. Hidden variables allow an AI to reason about entities beyond its sensory data, but they introduce a huge search space. Furthermore, patterns due to hidden variables can always be explained instead as (possibly more complicated) patterns just in terms of visible data. My question was: when should a rational entity hypothesize additional hidden variables?
Around that time someone on this list mentioned the Alchemy markov-logic system. One of the papers from the Alchemy website (http://alchemy.cs.washington.edu/papers/kok07/) talks about a method for learning hidden variables using clustering. At first I was surprised, but after a little thought this made sense: clusters can be seen as different states of a hidden variable that is probabilistically determining the data. In fact, adding hidden predicates and entities in the case of Markov logic makes the space of models Turing-complete (and even bigger than that if higher-order logic is used). But if I am not mistaken the clustering used in the paper I refer to is not that powerful. So the question is: is clustering in general powerful enough for AGI? Is it fundamental to how minds can and should work? PS- I know the LIDA framework makes extensive use of clustering, in the form of associative memory, for another example. ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=106510220-47b225 Powered by Listbox: http://www.listbox.com
