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C00012 00004	haugel[f86,jmc]		Review of John Haugland's ``Artificial Intelligence:
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C00031 00008	The Turing test
C00033 00009	Dualism and the pocket calculator
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C00036 00011		Years ago, when I was more naive, I hoped to get some useful
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\noindent {\it Artificial Intelligence: The Very Idea}. By JOHN HAUGELAND.
The MIT Press, Cambridge, MA, 1985. xii + 290 pp. \$14.95.
ISBN 0-262-08153-9.  A Bradford Book.

	Alas, John Haugeland has got the {\it Very Idea} wrong and
made a few other important errors.  Nevertheless, this is an excellent
book because of the number of things he has got right, his fair-mindedness
and his excellent explanations of the connections between AI and
older philosophical issues.

	His first error is regarding AI as a branch of biology, whereas it's
really a branch of computer science --- somewhat related to a branch of
biology.  As a branch of computer science, AI concerns how a machine
should decide how to achieve goals under certain conditions of information
and computational resources.  In this respect it's like linear
programming.  Indeed if achieving goals always amounted to finding the
maximum of a linear function given a collection of linear inequality
constraints, then AI would be included in linear programming.  However, the
problems we want machines to solve are often quite different.

	The key question in describing AI is characterizing
the problems that require
intelligence to solve and the methods available for solving them.  For example,
the roles of pattern matching, search and learning from experience
need to be discussed.  Haugeland doesn't attempt any general discussion
of this, although many of his examples are relevant.

	Haugeland's second mistake is to omit discussing mathematical
logic as a way of representing the machine's information about the world
and the consequences of action in the world.  Using logic is what gives AI
a chance to match the modularity of human representation of information, e.g.
the fact that we can receive information with provided by someone long
since dead who had no idea how it was going to be used.

	The third mistake is to omit discussing expert systems.  AI has
resulted in a certain technology that has both capabilities and limitations
that represent the current state of AI as a science.  He justifies this
omission by remarking that expert systems have no pyschological pretensions.
I suppose this dismissal is a consequence of regarding AI as biology rather
than computer science.

	Moreover, it seems to me that GOFAI (his abbreviation of ``good
old-fashioned AI'') doesn't rest on the theory that intelligence is
computation, an assertion whose vagueness makes me nervous.  The theory is
that intelligent behavior can be realized computationally.  The extent to
which human intelligence is realized digitally is a matter for
psychologists and physiologists.  For example, hormones may intervene
in human thought processes in an analog way and may have chemical roles
beyond communication, e.g. the same substance may digest food and signal
that a person is full.

	Here are some of the things he has got right.

	First of all, Haugeland has got right the polarization between
the scoffers and the boosters of AI --- the self-assurance of both
sides about the main philosophical issue.  The scoffers say it's
ridiculous --- ``like imagining that your car (really) hates you''
vs. the view that it's only a matter of time until we understand
intelligence well enough to program it.  This reviewer is a booster
and accepts Haugeland's characterization subject to some qualifications
not important enough to mention.

	Second he's right about the abstractness of the AI approach
to intelligence.  We consider it inessential whether the intelligence
is implemented by electronics or by neurochemical mechanisms or
even by a person manipulating pieces of paper according to rules he
can follow but whose purpose he doesn't understand.

	The discussion of the relation between arguments about
the possibility of AI and philosophical arguments going back
to Aristotle, Hobbes, Descartes and Leibniz and Hume is perhaps the
main content of the book.  It shows that many issues raised by
these philosophers are alive today in an entirely different
technological context.  However,
it's hard to trace any influence of this philosophy on present
AI thought or even to argue that reading Hobbes would be helpful.
What people are trying to do today is almost entirely
determined by their experience with modern computing facilities
rather than by old arguments however inspired.

	Haugeland doesn't discuss very much the influence of AI
on philosophical thought except to acknowledge its existence.
There is much more about that in Aaron Sloman's {\it The Computer
Revolution in Philosophy}, although Sloman's arguments don't
seem to convince many of his former colleagues in philosophy.
\vfill\eject\end

	John Haugeland, a philosopher, has got the ``very idea'' essentially
correct in this determinedly non-technical book.  Unfortunately,
discussing the philosophy of AI non-technically has imposes as severe
limitations as does a non-technical discussion of the philosophy of
mathematics or quantum mechanics.  Besides that, he omits to
discuss the use of mathematical logic in AI --- which could
be discussed non-technically to a considerable extent.  We begin
with the positive content of the book, after which we will discuss
the limitations of the non-technical approach which characterizes
almost all writing about AI by philosophers, even in the professional
philosophical literature.
haugel[f86,jmc]		Review of John Haugland's ``Artificial Intelligence:

Haugeland perhaps ignores Tarskian semantics.
- perhaps in both senses.

p..98 Chaitin

p. 112 GOFAI doesn't rest on the theory that intelligence is computation.
The theory is that intelligent behavior can be realized computationally.
The extent to which human intelligence is realized digitally is
a matter for psychologists and physiologists.
For example, the chemistry of hormones may play intervene in human
thought processes in an analog way.

The book is misleadingly non-technical.

Logic is ignored.

The state of AI technology.

The idea of AI doesn't actually depend on whether human thinking is
essentially computational, although I think it is substantially true.
Suppose that an important part of thinking is analog.  The most
plausible hypothesis in that direction is that the quantitative
amounts of the different hormones that are released determine
certain decisions.  Then we might be prepared to supplement the
digital computations representing reasoning by a digital simulation
of the important analog processes.  This would work unless these
processes were too extensive to be economically simulated.

Artificial intelligence is a science under development.  It has
substantial conceptual problems.  Under these conditions it is
not an easy task to summarize the field for the layman --- or
even for the practitioner.  Maybe it's as though someone tried
to summarize atomic physics in 1910.

Physical symbol hypothesis

How is meaning possible in a physical/mechanical universe?

An example of how philosophy gets itself entangled.

The discussion of meaning would benefit from inclusion of Tarskian
model-theoretic semantics.

	Perhaps the book's biggest weakness is that it gives little
picture of AI as a research activity.  AI researchers only rarely
ask what intelligence {\it is}, while they spend most of their time
asking how computers can be made to do something in particular.

	This is illustrated by a problem that has been unsolved since the
1950s.  Arthur Samuel (19xx,19xx) wrote programs for playing checkers that
learned to optimize the coefficients of the linear polynomial that
evaluated positions, e.g. it learned the best weights to be ascribed to
the numbers of kings and single men, control of the center and the back
rows and other functions of position discussed in the books about
checkers.  It replayed master games and adjusted its coefficients to
predict the moves considered good.  However, checker books also contain
information that can't readily be fitted into a position evaluation
function.  

	For example, a king can hold two single men of the opposite
side against the edge of the board so that neither can advance without
being captured.  If the opponent allows this to persist until both
sides king their remaining men, the side holding the other's two singles,
will outnumber the opponent by one on the rest of the board, and this
suffices to force exchanges and win.  Samuel's program ``would like'' to
advance the two singles, but the learned evaluation function doesn't give
it a high priority and the actual disaster may be 30 moves in the future
--- too far for lookahead.  It wouldn't be difficult to modify
the program to take this specific phenomenon into account, but humans learn
such things on the fly.

	Easier than making a program that can learn this
king-holds-two-singles stratagem should be making a program that can be
told about it.  Whoever tells the computer about the strategem should
not have to know the details of the program.  Otherwise, we have an
analogy to education by brain surgery.

	A related unsolved problem is how to make programs, specifically game
playing programs, decompose a situation into subsituations that can be
analyzed separately and whose interaction is subsequently analyzed.
Humans do this all the time, but it seems that quite good checkers and
chess can be played without it --- taking advantage of the computer's high
speed.  It is essential in the Japanese game of {\it go}, so there
are no good {\it go} programs yet.

How does this book differ from the usual ``Philosophy of X''?
Quotes:

p. 5
 ``$\ldots$ the only {\it theoretical} reason to take contemporary
Artificial Intelligence more seriously than clockwork fiction is the
powerful suggestion that our own minds work on computational
principles.  In other words, we're really interested in AI as
part of the theory that {\it people} are computers --- and we're
all interested in people.''

p.4
According to a central tradition in Western philosophy, thinking
(intellection) essentially {\it is} rational manipulation of
mental symbols (viz., ideas).

p.5
Artificial Intelligence in this sense (as a branch of cognitive
science) is the only kind we will discuss.  For instance, we will
pay no attention to commercial ventures (so-called ``expert systems'',
etc.) that make no pretense of developing or applying psychological
principles.  We also won't consider whether computers might have
some alien or inhuman kind of intellect (like Martians or squids?).
My own hunch, in fact, is that anthropomorphic prejudice, ``human
chauvinism,'' is built into our very concept of intelligence.  This
concept, of course, could still apply to all manner of creatures;
the point is merely that it's the only concept we have --- if we
escaped our ``prejudice,'' we wouldn't know what we were talking
about.

p.5 - a muddle
But the lesson goes deeper: if Artificial Intelligence really has
little to do with computer technology and much more to do with
abstract principles of mental organization, then the distinctions
among AI, psychology, and even philosophy of mind seem to melt away.
One can study those basic principles using tools and techniques from
computer science, or with the methods of experimental psychology,
or in traditional philosophical terms --- but it's the same subject
in each case.


p.39
On the other hand, if the manipulator does not pay attention to the
meanings, then the manipulatoions can't be instances of reasoning
--- because what's reasonable depends crucially on what the symbols
mean.

Does a calculator pay attention to the meanings?  What about the
HP-28c that has commutativity as an explicit rule?  Paying attention
to the meanings leads to the infinite regress of Achilles and the
Tortoise with modus ponens.  At some point the logic must be built
into the machinery.

p.93
Artificial Intelligence  embraces this fundamental approach --- to
the point, indeed, of arguing that thoughts {\it are} symbolic.  But
that doesn't imply that people think in English (or any ``language''
very similar to English); AI maintains only that thinking is ``like''
talking in the more abstract sense that it occurs in a symbolic
system --- probably incorporating a mode//content distinction.  Such
a system could be vastly different from ordinary languages (in richness
and subtlety or whatever) and still have the crucial abstract features
of compositionality and arbitrary basic meanings.


p. 100
``$ldots$ because chess is not an interpreted system''
Capablanca at age three succeeded in interpreting chess.

p.112
GOFAI as a branch of cognitive science, rests on a particular theory
of intelligence and thought --- essentially Hobbes's idea that
ratiocination is {\it computation}.  We shold appreciate that this
thesis is empirically substantive; it is not trivial or tautological,
but actually says something about the world that could be false.

No, it's more like an open mathematical conjecture.

p. 120
And that's exactly the sort of fact that inclines us to suppose that
people {\it understand} their thoughts, whereas paper and floating
magnets are utterly oblivious.

Perhaps understanding one's thoughts is like metamathematics; it
can be done to any level, but at the highest level to which it
is actually done, there are thoughts that are not understood, because
to do so one would have to ascend to another level, and one hasn't
taken the time.

p.174
[What is said today about the 1950s language translators is, I
suspect, a legend.  I mean about their motivations and what they
thought they could get away with.  Also about the utility of their
output.]

[Cybernetics may not be so bad if used in a symbolic domain.
H is right that its mathematical stability conditions provide
only a few metaphors of limited applicability].
GPS is cybernetics.

p.194
[There's a lot more to be learned from microworlds --- even from chess].
Drosophila.


Good things

p. 7
Why IQ is irrelevant

Hobbes?

relating AI to traditional philosophical issues

p.98 et seq

arguments from cryptography

p. 208
reasonable treatment of the frame problem

210 - some good remarks on dogs vs. trees as patterns.

216 - a mathematical theory of belief has to have its 0 and 1

242 - simulation as a way of understanding other systems is often
neither necessary nor sufficient.  He will never do X.  He will
try to achieve his goals.  Universal statements about behavior.

244 - The importance of knowledge representation was discussed in
my 1960 paper.

251 - GPS was a hypothesis about intelligence.

Mention Sloman.
The Turing test

	The Turing test shouldn't be used as a criterion for artificial
intelligence, and I don't think that Turing intended that way.  Instead it
serves primarily as a criterion for whether it is worthwhile talking
further with a philosopher of mind, e.g. a philosopher who publishes in
{\it Mind}.  Whoever won't admit that a machine that can pretend to be a
person is intelligent has a non-empirical criterion for intelligence, and
it's difficult to see how a scientific discussion with him can be continued.

	However, if one actually wants to set up the test, it must
be done carefully.  Otherwise, it may be possible to fool the interrogator
easily.  The interrogator should be sophisticated about AI and know
what is currently considered difficult for machines.  Of course,
the interrogator should know what task he is undertaking.  Otherwise
some people may think they are dealing with a person when interacting
with a quite ordinary expert system.
Dualism and the pocket calculator

	Some puzzles of the philosophy of mind can be made to assume their
characteristic form for systems simple enough to understand completely.
We regard the question ``Does a pocket calculator add numbers?'' as
entirely analogous to the question ``Does a human or AI program manipulate
ideas?''.

Does the fact that $7 + 8 = 15$ make the calculator print $15$ when
the user hits the keys $7$, $8$, $=$ in succession?  This is analogous
to the question of whether thoughts have a causal role in humans or
are merely an epiphenomenon.  The latter queston is no more likely to be
fruitful than the former.

Does a calculator ``pay attention to the meaning of numbers''?
p. 39


outline:

the very idea is wrong, but

cs vs. biology

travelling example

Samuel example

skates over the top

	How does the information situation in which intelligent
behavior is required differ from that of conventional forms of optimization
and control?  The full answer isn't known, so we must make do with
some characterizations and some examples.  

	AI is relevant when the information situation is open-ended,
when it must be possible to add new ideas at any time.

	Years ago, when I was more naive, I hoped to get some useful
work from philosophers in developing artificial intelligence (AI).
After all, AI is concerned with some of the same epistemological problems
philosophers have been working on for 2,000 years.  For example, we
both face the problem of reconciling making choices with determinism.
Thus a computer program has to consider its options, i.e. to regard
itself as having a free choice to make, even though it is a deterministic
machine and might very well have to know it.  It gradually became apparent
that AI was unlikely to get this help from philosophers --- at least from
those of the present generation.

	Haugeland's book doesn't actually offer any help, but it offers
more understanding than previous efforts by philosophers except perhaps
Aaron Sloman's {\it The Computer Revolution in Philosophy}.  The latter
makes big claims for the effect AI should have on philosophical thinking.
I mainly agree with the claims but have to admit that Sloman can't yet
sufficiently substantiate them to have much influence on his fellow
philosophers.

	Both the understanding and the potential help is considerably
vitiated by the fact that Haugeland has got the {\it Very Idea} of AI
wrong.  He treats it as a branch of biology, i.e. as concerned with
how people think, rather than as a branch of computer science, concerned
with how goals can be achieved under certain complex conditions of
information and capability for action.  The biology and the computer
science are related by the fact that certain problems require certain
methods whether the solver is human or a computer program.

	AI is a branch of computer science just as is linear programming.
Indeed if all goals presented themselves as minimizing linear functionals
subject to linear inequality constraints, then AI would be included in
linear programming.  However, the world is more complex then that, and
so is the information available when a goal is undertaken and that
can be made available by information seeking actions.  Therefore,
intelligent behavior requires many abilities.  There are more than I
can list here --- indeed more than AI has so far identified --- but
the following have been extensively studied.

	1. To recognize patterns in phenomena, i.e. to assign values
to variables in patterns according to the way the matches some
aspect of the phenomenon.

	2. To search a space of abstract objects for one that fullfills
a desired condition, e.g. to search for a strategy of action that
achieves a goal, e.g. for a move that wins in chess or a lemma that
helps prove a theorem.

	3. To represent information about the world in a way that
allows new information to be acquired without knowing how it is going
to be used.  Information about how information can be obtained must
also be represented.

	4. To infer from facts the answers to questions.

	Haugeland is mainly concerned with whether AI is philosophically
ok as a scientific subject.  He doesn't reach a definite conclusion.  From
this point of view, Hobbes and Leibniz are AI's philosophical founders,
because both of them wanted to regard thinking as calculation with
physical representations of symbols, and Leibniz even wanted to do it by
machine.  However, Descartes with his dualism and the idealists raised
various difficulties, and if they were right, it's hard to see how AI
would be possible.  The entire discussion can be carried out on a lofty
plane not requiring much identification of specific intellectual abilities
or the specific problems that AI is currently researching.

	Within the limitations he implicitly sets for himself, Haugeland
does a marvelous job.