On Sat, Mar 29, 2008 at 3:58 PM, Mike Tintner <[EMAIL PROTECTED]> wrote: > > IMO there is one key & in fact crucial distinction between AI & AGI - which > hinges on "adaptivity". > > An AI program has "special(ised) adaptivity" -can adapt its actions but only > within a known domain > > An AGI has "general adaptivity"- can also adapt its actions to deal with > unknown, unfamiliar domains. >
There is no "general adaptivity" - all learning algorithms are constrained to efficient learning only in narrow domains. The problem with narrow AI systems is that their performance either relies on people manually designing learning algorithms which work fine on given narrow domains, or requires insanely much data to learn things with less biased algorithms. In first case each new problem requires a human in the loop and months of thinking about the problem, in effect human acquires information about the target domain using his intelligence and then encodes this information in parameterized form, so that it can then be tweaked a little to solve a "last mile problem" of adapting to particular features of target domain that are hard to encode manually or are different in each case. In second case algorithm is terrible at learning in target domain, but when you have a whole Internet of data it doesn't necessarily look like that, considering that you don't have to tweak the algorithm to the problem. Making a general AI learning requires understanding of a general AI domain, and this general AI domain is not "more general" than some of these narrow AIs. Probability is conserved, general AI is necessarily restricted in learning ability. So, while an ability to represent an arbitrary algorithm would be nice (which is not present in many popular machine learning algorithms), it doesn't mean that system will guess any algorithm given only partial data about it. It might need to actually look at it. But it needs to be able to guess the kind of information that people are good at guessing, and what is it exactly that we learn, infer from incomplete description, as opposed to memorizing when presented in whole, is the core of the problem. Here's a paper that may give some intuition about this issue: http://yann.lecun.com/exdb/publis/pdf/bengio-lecun-07.pdf Yoshua Bengio, Yann LeCun. 2007. Scaling learning algorithms towards AI. -- Vladimir Nesov [EMAIL PROTECTED] ------------------------------------------- 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=98558129-0bdb63 Powered by Listbox: http://www.listbox.com
