seth@gandalf.cognet.ucla.edu (Seth R. Goldman) (06/09/88)
In article <3c84f2a9.224b@apollo.uucp> nelson_p@apollo.UUCP (Peter Nelson) writes: > > I don't see why everyone gets hung up on mimicking natural > intelligence. The point is to solve real-world problems. Make > machines understand continous speech, translate technical articles, > put together mechanical devices from parts that can be visually > recognized, pick out high priority targets in self-guiding missiles, > etc. To the extent that we understand natural systems and can use > that knowledge, great! Otherwise, improvise! It depends what your goals are. Since AI is a part of computer science there is naturally a large group of people concerned with finding solutions to real problems using a more engineering type of approach. This is the practical end of the field. The rest of us are interested in learning something about human behavior/intelligence and use the computer as a tool to build models and explore various theories. Some are interested in modelling the hardware of the brain (good luck) and some are interested in modelling the behavior (more luck). It is these research efforts which eventually produce technology that can be applied to practical problems. You need both sides to have a productive field.
sewilco@datapg.DataPg.MN.ORG (Scot E. Wilcoxon) (06/09/88)
In article <3c84f2a9.224b@apollo.uucp> nelson_p@apollo.UUCP (Peter Nelson) writes: ... > I don't see why everyone gets hung up on mimicking natural > intelligence. The point is to solve real-world problems. Make ... > etc. To the extent that we understand natural systems and can use > that knowledge, great! Otherwise, improvise! The discussion has been zigzagging between this viewpoint and another. This is the "thought engineering" side, while others have been trying to define the "thought science" side. The "thought science" definition is concerned with how carbon-based creatures actually think. The "thought engineering" definition is concerned with techniques which produce desired results. There are many cases where engineering has produced solutions which are different than the definition provided by an existing technique. Duplicating the motions of a flying bird or dish-washing human does not directly lead to our present standards of fixed-wing airplanes and mechanical dishwashers. -- Scot E. Wilcoxon sewilco@DataPg.MN.ORG {amdahl|hpda}!bungia!datapg!sewilco Data Progress UNIX masts & rigging +1 612-825-2607 uunet!datapg!sewilco
sierch@well.UUCP (Michael Sierchio) (06/10/88)
One of the criticisms of AI is that it is too engineering oriented -- iut
is a field that had its origins in deep questions about intelligence and
automata. Like many fields, the seminal questions remain unanswered, while
Ph.D.s are based on producing Yet Another X-based Theorem Prover/Xprt System/
whatever.
The problem has enormous practival consequences, since practice follows
theory. For instance, despite all the talk about it, why is it that
cognition is mimicked as the basis fro intelligence? What about cellular
intelligence, memory, etc of the immune system? Einstein's "positiona;
and muscular" thinking?
I think that there is, ideally , an interplay between praxis and theory.
But Computer SCIENCE is just that -- or should be -- It has, lamentably,
become an engineering discipline. Just so you knowm, I pay the rent through
the practical application of engineering kbnowledge. But I love to ponder
those unanswered questions. And never mind my typing -- just wait till I
get my backspace key fixed!
--
Michael Sierchio @ SMALL SYSTEMS SOLUTIONS
2733 Fulton St / Berkeley / CA / 94705 (415) 845-1755
sierch@well.UUCP {..ucbvax, etc...}!lll-crg!well!sierch