[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Is DeepBlue intelligent? Some extra-curricular philosophy

 This is a mail I had sent to the class in Fall 2003. As they say
about re-runs,  if you haven't seen it, it is *new* for you ;-)


Here is an article that discusses the question whether Deep Blue--the
Kasparov-beating chess program--that we are discussing--is

I send this to you because this is pretty much my bias/position too on
this issue (plus I like Drew McDermott's style--if you ever get a
chance, you should read his paper "Artificial Intelligence meets
Natural Stupidity"--which can be found at
http://rakaposhi.eas.asu.edu/mcdermott.pdf -- and was written in the
early days of AI (~1978) to criticize researchers' tendency to
self-delude... (which is also related to the AI/Thermos Flask
joke--ask me about it sometime). 

Bottom line: Introspection is a lousy way to theorize about thinking. 

See the end for a pointer to a different perspective


How Intelligent is Deep Blue?
Drew McDermott

[This is the original, long version of an article that appeared in the
May 14, 1997 New York Times with more flamboyant title.]

IBM's chess computer, Deep Blue, has shocked the world of chess by
defeating Garry Kasparov in a six-game match.  It surprised many in
computer science as well.  Last year, after Kasparov's victory against
the previous version, I told the students in my class, ``Introduction
to Artificial Intelligence,'' that it would be many years before
computers could challenge the best humans.  Now that I and many others
have been proved wrong, there are a lot of people rushing to assure us
that Deep Blue is not actually intelligent, and that its victory this
year has no bearing on the future of artificial intelligence as such.
I agree that Deep Blue is not actually intelligent, but I think the
usual argument for this conclusion is quite faulty, and shows a basic
misunderstanding of the goals and methods of artificial intelligence.

Deep Blue is unintelligent because it is so narrow.  It can win a
chess game, but it can't recognize, much less pick up, a chess piece.
It can't even carry on a conversation about the game it just won.
Since the essence of intelligence would seem to be breadth, or the
ability to react creatively to a wide variety of situations, it's hard
to credit Deep Blue with much intelligence.

However, many commentators are insisting that Deep Blue shows no
intelligence whatsoever, because it doesn't actually ``understand'' a
chess position, but only searches through millions of possible move
sequences ``blindly.''  The fallacy in this argument is the assumption
that intelligent behavior can only be the result of intelligent
cogitation.  What the commentators are failing to acknowledge is that
if there ever is a truly intelligent computer, then the computations
it performs will seem as blind as Deep Blue's.  If there is ever a
nonvacuous explanation of intelligence, it will explain intelligence
by reference to smaller bits of behavior that are not themselves
intelligent.  Presumably *your brain* works because each of its
billions of neurons carry out hundreds of tiny operations per second,
none of which in isolation demonstrates any intelligence at all.

When people express the opinion that human grandmasters do not examine
200,000,000 move sequences per second, I ask them, ``How do you
know?''  The answer is usually that human grandmasters are not *aware*
of searching this number of positions, or *are* aware of searching
many fewer.  But almost everything that goes on in our minds we are
unaware of.  I tend to agree that grandmasters are not searching the
way Deep Blue does, but whatever they are doing would, if implemented
on a computer, seem equally ``blind.''  Suppose most of their skill
comes from an ability to compare the current position against 10,000
positions they've studied.  (There is some evidence that this is at
least partly true.)  We call their behavior insightful because they
are unaware of the details; the right position among the 10,000 ``just
occurs to them.''  If a computer does it, the trick will be revealed;
we will see how laboriously it checks the 10,000 positions.  Still, if
the unconscious version yields intelligent results, and the explicit
algorithmic version yields essentially the same results, then they
will be intelligent, too.

Another example: Most voice-recognition systems are based on a
mathematical theory called Hidden Markov Models.  Consider the
following argument: ``If a computer recognizes words using Hidden
Markov Models, then it doesn't recognize words the way I do.  I don't
even know what a Hidden Markov Model is.  I simply hear the word and
it sounds familiar to me.''  I hope this argument sounds silly to you.
The truth is that we have no introspective idea how we recognize
spoken words.  It is perfectly possible that the synaptic connections
in our brains are describable, at least approximately, by Hidden
Markov Models; if they aren't, then some other equally
counterintuitive model is probably valid.  Introspection is a lousy
way to theorize about thinking.  There are fascinating questions about
why we are unaware of so much that goes on in our brains, and why our
awareness is the way it is.  But we can answer a lot of questions
about thinking before we need to answer questions about awareness.

I hope I am not taken as saying that all the problems of artificial
intelligence have been solved.  I am only pointing out one aspect of
what a solution would look like.  There are no big breakthroughs on
the horizon, no Grand Unified Theory of Thought.  Doing better and
better at chess has been the result of many small improvements (as was
the proof of a novel theorem last year by a computer at Argonne Lab.)
There have been other such developments, such as the
speech-recognition work I referred to earlier, and many results in
computer vision, but few ``breakthroughs.''  As the field has matured,
it has focused more and more on incremental progress, while worrying
less and less about some magic solution to all the problems of
intelligence.  A good example is the reaction by AI researchers to
neural nets, which are a kind of parallel computer based on ideas from
neuroscience.  Although the press and some philosophers hailed these
as a radical paradigm shift that would solve everything, what has
actually happened is that they have been assimilated into the AI
toolkit as a technique that appears to work some of the time --- just
like Hidden Markov Models, game-tree search, and several other
techniques.  Of course, there may be some breakthroughs ahead for the
field, but it is much more satisfying to get by on a diet of solid but
unglamorous results.  If we never arrive at a nonvacuous theory of
intelligence, we will no doubt uncover a lot of useful theories of
more limited mental faculties.  And we might as well aim for such a

So, what shall we say about Deep Blue?  How about: It's a ``little
bit'' intelligent.  It knows a tremendous amount about an incredibly
narrow area.  I have no doubt that Deep Blue's computations differ in
detail from a human grandmaster's; but then, human grandmasters differ
from each other in many ways.  On the other hand, a log of Deep Blue's
computations is perfectly intelligible to chess masters; they speak
the same language, as it were.  That's why the IBM team refused to
give game logs to Kasparov during the match; it would be equivalent to
bugging the hotel room where he discussed strategy with his seconds.
Saying Deep Blue doesn't really think about chess is like saying an
airplane doesn't really fly because it doesn't flap its wings.

It's entirely possible that computers will come to seem alive before
they come to seem intelligent.  The kind of computing power that fuels
Deep Blue will also fuel sensors, wheels, and grippers that will allow
computers to react physically to things in their environment,
including us.  They won't seem intelligent, but we may think of them
as a weird kind of animal --- one that can play a very good game of

For a radically different view point, see


This one is by David Gelernter, who, get this, is a colleague of Drew
McDermott at Yale. On an unrelated note, Gelernter is also one of the
scientists who was targeted by the Unabomber--Kazinscky(?), and was
seriously injured by a letter bomb--thus the title of his book at the
end of the article.]]