Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!swrinde!zaphod.mps.ohio-state.edu!tut.cis.ohio-state.edu!neuron.cis.ohio-state.edu!pja From: pja@neuron.cis.ohio-state.edu (Peter J Angeline) Newsgroups: comp.ai.neural-nets Subject: Re: Gibson's escape from computational impossibility. Message-ID: Date: 22 Mar 91 15:53:20 GMT References: Sender: news@tut.cis.ohio-state.edu Reply-To: pja@cis.ohio-state.edu Distribution: comp.ai.neural-nets comp.ai.philosophy Organization: Ohio State Computer Science Lines: 43 In-reply-to: bradski@park.bu.edu's message of 21 Mar 91 23:41:56 GMT In article bradski@park.bu.edu (Gary Bradski) writes: If the [Ecological View of J. J. Gibson] 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. Wait, aren't the computations you're talking about really the same hard computations that everyone is trying to do? Why is the nervous system relieved of any computational burdens simply by recognizing that the information is apparent in the "ambient array"? How are you planning to identify the invarients in the environement if you're not going to concentrate on "computation" just "extracting and tuning"? You've still got to build an "eye" to get the invarients. Extraction of features is not that different than extraction of invarients if you assume that the features are the invarients of some environment. Choosing the right invarients IS NOT the hard problem in perception, it's consistently acquiring the invarients in real time. Gibson's view of perception is very important in that it specifys that the "design" constraints of a biological perceptual system is hopelessly tied to the invarients of the environment rather than being some idealized sensor. But the hard computational problems are still there and are essentailly the same. You've just narrowed the search a little, assuming you know the correct invarients. 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 -pete angeline -- ------------------------------------------------------------------------------- Peter J. Angeline ! Laboratory for AI Research (LAIR) ARPA: ! THE Ohio State University, Columbus, Ohio 43210 pja@cis.ohio-state.edu ! "Nature is more ingenious than we are."