From: jmc@SAIL.Stanford.EDU (John McCarthy)
Subject: Re: Dieback and culture: Was(Re: AIDS in Africa (was: environment and animals))
Reply-To: jmc@cs.Stanford.EDU
Organization: Computer Science Department, Stanford University
Date: 15 Apr 92 20:32:24

In article <40911839@hpindda.cup.hp.com> alanm@hpindda.cup.hp.com (Alan McGowen) writes:

   / hpindda:sci.environment / jmc@SAIL.Stanford.EDU (John McCarthy) /  3:35 pm  Apr  8, 1992 /
   >Alan McGowen writes:
   >
   >     Human intelligence is a specific set of biological
   >     adaptations peculiar to one species. There is noting
   >     "general" about it -- the belief in the existence of
   >     "general" intelligence abstractable from the biological
   >     substrate is a myth of AI. Mind you, I am not saying that
   >     human intelligence cannot be produced artificially: if you
   >     can emulate the neurobiology with sufficient detail, you
   >     will emulate human intelligence. But we are a *very* long
   >     way from being able to emulate the neurobiology.
   >
   >Why "myth" and not hypothesis?

   A hypothesis is generally an explanation of something (in this 
   case intelligence) for which there is evidence. The evidence for 
   a neurobiology-independent algorithmic explanation of natural 
   intelligence is lacking: everything neural science has learned about
   the brain and what it does shows it to be a complex of adaptations 
   like all others in biology, arising out of local optimizations of 
   innumerable historical contingencies, full of quirks of history. Human
   intelligence is a human adaptation.  The lack of success of AI based on 
   the "hypothesis" of generalizable algorithmic intelligence is further 
   evidence against it. Persistent belief in a "hypothesis" for which there 
   is no evidence and plenty of counterevidence smacks of mythology.

   If you want to emulate bits of intelligence in an artificial substrate,
   you will have to emulate the neural architectures natural selection has
   produced to generate intelligent behavior. When that is possible, some 
   form of AI will be possible. Artificial intelligence capable of passing a 
   "Turing test" is pretty unlikely though -- emulation of human neurology closely
   enough to accomplish this would require emulation of human social, emotional,
   and personality development: rather difficult without a human body growing 
   up among humans. More likely, practical applications of AI will be less 
   concerned with emulating human intelligence in detail than with confering 
   bits of animal visual systems and movement control on machines: if you want 
   to build a machine that can climb a tree and retrieve a cat, you are going 
   to have to put a great deal of monkey neural business into it.

   The road to AI is neural science, not programming trickery.

   NB: I am generally bashing "cognitive science" here, not asserting that 
   there is something nonmechanistic about intelligence or conciousness, nor 
   that some sort of quantum weirdness differentiates humans from machines. 
   Humans are machines, biochemical machines. But intelligence is not something 
   separate from the details of that machinery. Intelligence is still biology,
   and AI, when it finally exists, will be a kind of biotechnology, even if it
   is partly done with software.

   ----------
   Alan McGowen

Mr. McGowen is as arrogant about AI as he is about everything else.
It will be a real tragedy if people like him ever get political power.
He is as pigheaded as Lenin, and if he had power, he would most likely
be succeeded by someone as brutal as Stalin.

There are many approaches to AI, but they divide into two main groups
with a certain amount of peeking at what the others are doing.  One
group of approaches involves psychology and/or neurophysiology, i.e.
they study the human behavior or the physiology of the brain and
try to imitate it.

The other approaches study the world and the intellectual problems
involved in achieving goals in the world.  Its tools involve various
emphases on formalizations in mathematical logic or on programming.

All approaches have achieved some success.  To get specific, why does
Alan McGowen think that formalizing nonmonotonic reasoning in
mathematical logic, a problem first studied in the late 1970s doesn't
constitute progress?  Probable answer: he has never heard of it,
and prefers opinions based on a priori grounds.  If that's the
situation he is not alone.  For example, Penrose's book "The
Emperor's New Mind" has about 200 references, not one of which
is to the technical literature on AI.

--
John McCarthy, Computer Science Department, Stanford, CA 94305
*
He who refuses to do arithmetic is doomed to talk nonsense.

Newsgroups: sci.environment