On Mon, Jul 21, 2008 at 10:32 PM, Richard Loosemore <[EMAIL PROTECTED]> wrote: > > Steve, > > Principal component analysis is not new, it has a long history, and so far > it is a very long way from being the basis for a complete AGI, let alone a > theory of everything in computer science. > > Is there any concrete reason to believe that this particular PCA paper is > doing something that is some kind of quantum leap beyond what can be found > in the (several thousand?) other PCA papers that have already been written? > > To give you an idea of what I am looking for, does the algorithm go beyond > single-level encoding patterns? Can it find patterns of patterns, up to > arbitrary levels of depth? And is there empirical evidence that it really > does find a set of patterns comparable to those found by the human cognitive > mechanism, without missing any obvious cases? > > Bloated claims for the effectiveness of some form of PCA turn up frequently > in cog sci, NN and AI. It can look really impressive until you realize how > limited and non-extensible it is. >
Indeed, there are many techniques to perform "flat" clustering, and some of them work really well. The trick is to use such techniques to build up levels of representation, from "sensory" perception and up to the higher concepts, with cross-checks everywhere and goal-directed dynamics at the core. -- Vladimir Nesov [EMAIL PROTECTED] http://causalityrelay.wordpress.com/ ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=108809214-a0d121 Powered by Listbox: http://www.listbox.com
