Path: utzoo!utgpu!news-server.csri.toronto.edu!rpi!bu.edu!bradski From: bradski@park.bu.edu (Gary Bradski) Newsgroups: comp.ai.neural-nets Subject: Gibson's escape from computational impossibility. Message-ID: Date: 21 Mar 91 23:41:56 GMT Sender: news@bu.edu.bu.edu Distribution: comp.ai.neural-nets comp.ai.philosophy Organization: Boston University Center for Adaptive Systems Lines: 54 Decided to kick this idea around: When one sets out to create a machine that can operate as an independent agent in the world, one quickly finds that the problem is computationally impossible. [Where "quickly" can be on the order of 3 decades]. The usual response is to work on some toy problem and invoke the gods of massive parallelism to scale it up and do the rest. The gods of parallelism may help, but I think the demons of communication bottlenecks and "can't go faster than light" will still have the last laugh ['nother 3 decades...]. But -- all this time, even the smallest fly can navigate in three dimensions in a turbulent medium, find food, flee foes and mate with friends. I contend that the fly can do this not by doing *so* many computations, but because it is doing the *right* computations. I other words: the fastest, best generalizing, network/LISP code won't do very well if it's working on data that doesn't carry much, or obscures, the *information* of interest. I think the approach to take in developing intelligent machines is to first study of the form and content of the information that the environment provides. The best source on this is can be found in Ecological Psychology [James Gibson for founding/philosophy and works on perception, M. Turvey for more recent work]. Gibson's general idea is that the information needed to act in the world does not have to be synthesized -- it's already there. Gibson regarded perception as an active, searching process that extracts the embedded invariants (information) -- thus perception is considered as a full loop: the receptive organs as well as their motor and neural feedback tuning apparatus along with the intent to perceive. If the above view is correct, then the nervous system is relieved of an immense computational burden. AND, the types of machines built to operate in the world will concentrate not so much on computation as on extracting and tuning -- eg. coordinate transformations that bring out the embedded invariants and make them easily separable, correlation detectors, attentional and other types of focus etc... rather computations of masses, distances, reflectances, velocities, inertia's, inverse kinetics etc. I think the fly mentioned above speaks for this type of approach, the alternative is what I'll call the DARPA fly: 15 tons, 20 CRAYs and still can't do shit. >:-) -- Gary Bradski I'net: bradski@bucasb.bu.edu Center for Adaptive Systems Bitnet: bradski%thalamus@buacca Boston University. UUCP: {encore,harvard,uunet}!bu.edu!bradski 111 Cummington St, Boston MA 02215 I don't even agree with some of my opinions