That was pretty much the gist of it --- his remarks on this were very
brief.

He took one question on the topic and didn't answer it.

There's a physics analogy here, too actually. QM is the classic
example of a theory which simply tore down the roots of physics:
indeed physics has been divided ever since into `classical' and
`quantum' physics (and relativity is classical).  There are concepts
in QM which just never cropped up until it was discovered.  And yet,
if you want to really understand it, the first thing you need to do is
get *really good* at advanced formalisms for classical mechanics,
especially the Hamiltonian formalism and the calculus of variations.
If you don't understand that stuff, you'll never be able to hack QM.

What makes you so sure there's a continuum here?

The main fallacy in this kind of thinking is that the reductionist
hypothesis does not by any means imply a Œconstructionist¹ one: the
ability to reduce everything to simple fundamental laws does not
imply the ability to start from those laws and reconstruct the
universe. In fact, the more the elementary particle physicists tell
us about the nature of the fundamental laws, the less relevance they
seem to have to the very real problems of the rest of science, much
less to those of society. The constructionist hypothesis breaks down
when confronted with the twon difficulties of scale and complexity.
The behaviors or large and complex aggregates of elementary particles,
it turns out, is not to be understood in terms of a simple
xtrapolation of the the properties of a few particles. Instead, at
each level of complexity enturely new properties appear, and the
understanding of the new behaviors requires research which I think
is as fundamental in its natura as any other. That is, it seems to
me that one may array the sciences roughly linearly in a hierarchy,
according to the idea: The elementary entities of science X obey the
laws of science Y. [Š] But this hierarchy does not imply that science
X is Œjust applied Y.¹ At each stage entirely new laws, concepts, and
generalizations are necessary, requiring inspiration and creativity
to just as great a degree as in the previous one. Psychology is not
applied biology, nor is biology applied chemistry."

(p. 393 of Anderson, P. W. (1972). More is different: Broken symmetry
and the nature of hierarchical structure of science. Science, 177,
393-396.)

If understanding human cognition is the capstone of physical science,
then *maybe* one can take the reductionist route, try to model lower
organisms, and hope that these pieces will sum up to an adequate
account.  But I doubt it.

But these are just my hunches, just as those who claim that the
bottom-up approach is sufficient are offering just their opinions,
and not the only approach licensed by science.

But you cannot let yourself forget that ultimately all the observations
you make will have some sort of underlying pattern.  Otherwise you end
up doing what physicians might term "treating the symptoms and not the
disease;" that being a superficial way of dealing with a sick patient.

Understanding "lower organisms" may give us some of the tools needed to
understand ourselves; but in no way am I asserting that it will give us
an understanding of ourselves.  I just don't think you should be so
quick to dismiss a potential opportunity.  Lifeforms on Earth are not
that radically different from each other.  Human beings and other
animals may share quite a lot in common, and ignoring that would only be
to your own detriment.

Of course, keeping the resulting watershed from becoming a mass labor
catastrophe would be an Interesting[tm] political problem.

Michael

When all you have is a hammer, everything looks like a nail.  Even
probably non-sentient animals have access to sensory and interactivity
hardware that it many orders of magnitude more flexible than anything
under a current "AI"'s control.  

IOW, a chimp's, or even a dog's brain has access to a fairly complete
set of extremely well designed tools.  Your current sample of "AI"
attempts have access to a hammer.  No wonder that when you talk to
A.L.I.C.E., the conversation keeps coming back to nails.  I have much
more interesting conversations with my dog.

Michael

But...

The sentiment that I sense is out there, and perhaps this is only
my paranoia, is that there are two ways to understand a domain:
the reductionist strategy, which is real science, and everything
else, which is psuedoscientific by definition.

My main goal has been to argue that reduction is a scientific
strategy, but not *the* scientific strategy.  This is why I cited
Anderson, and his advocation of the constructionist strategy.

A secondary goal of mine was to argue that, reduction proceeds
bottom-up, there is a top-down strategy as well:  One can
look at the products of human cognition (i.e., literature,
art, mathematics, science) and explain them scientifically.
A good example of this is Langley, Simon, Bradshaw, and
Zytkow's (1987) "Scientific discovery: Computational
explorations of the creative processes", but there are others.
Piaget attempted something similar in his program of "genetic
epistemology".  The point here is that an adequate account of
human cognition must not just scale down to fit the facts of
biology "below"; it must also illuminate the discplines "above".
However, looking back over my other posts, I didn't really make
this point explicitly.

There is another point of relevance.  My reading of the history
of science is that reduction is after-the-fact.  There is a
theory of a low-level domain (say mechanics) and a theory of a
high-level domain (say thermodynamics).  These theories were
hatched through the hard work of scientists working at each
level (say Newton and Carnot/Clausius, respectively).  The
phenomena of each domain are understood adequately through each
of the theories.

Reduction in this case is a kind of scientifc tidying up.
Statistical mechanics unifies the two theories, and gives us
comfort that we will one day be able to write down a single
physical theory which accounts for everything above it.  Of
course, reduction also produces new insights for both the
low and high level domains, but these are second-order in
magnitude.  However, reduction is not possible until adequate
theories of both domains come into exsitence.  Which is to say,
independent efforts to understand high-level human cognition
must proceed by whatever means helps us understand phenomena
at this level.  In particular, there is no reason to focus on
just the biological level below and to ignore intrinsic
levels of psychological science and other levels above.
Reduction, in this reading, will find its primary role later,
to unify adequate psychological accounts with adequate
biological accounts formulates by scientists working relatively
independently at the respective levels.

What seems to be interesting is the quality of the execution engine and
the classifier, so that you know the shape of the result you want and
can figure out quickly whether a particular set of transformations on
current reality will lead to something like that shape.

Having a rigorous explanation of how you go from a stick and some boxes
and a bunch of bananas up on a hook to an ape eating bananas might well
do most of the work toward figuring out how you go from a bunch of
squares and right triangles to a proof.

In article <pw-FE6BF0.15562205112...@reader1.panix.com>, Paul Wallich

Michael

Minsky's "Society of Mind" and especially Hofstadter's "Fluid Concepts
and Creative Analogies" are computational attempts to realize such
processing, and neither draws much at all on animal intelligence.

[Tim B.: Hofstadter's ideas might be particularly comforting to your
inner physicist.]

I have articulated my position on the bottom-up approach to conquering
human cognition (in its entirety) in other messages in this thread.
Like Anderson (1972), I believe that reductive knowledge of how the
parts work does not guarantee knowledge of how the whole works.  Or
to put it more accurately, theories of level N in the hierarchy of
science (where N>=1, and N=1 for physics) cannot be mechanically
aggregated to yield theories of level N.  Rather, I believe that
theories must be formulated relatively independently at each level
-- only in this way will they do justice to the relevant phenomena
-- and then related after the fact via reduction.

This is the reason I keep stressing the cognitive phenomena that
interest me.  They are high-level aspects of thinking such as
language use and problem solving.  No theories of animal intelligence
do justice to the "top end" of human cognition.  This is why I do
not believe theories of human cognition should be built from the
first principles of animal cognition.

Another reference I offered is the first chapter of Marr's (1982)
_Vision_.  There he explicitly confronts the inability of even
seminal findings in the neuroscience of vision to add up to a
satisfactory explanation of vision:

===
The initial discoveries of the 1950s and 1960s were not being
followed by equally dramatic discoveries in the 1970s. No
neuropsychologists had recorded new and clear high-level
correlates of perception. [Š] None of the new studies succeeded
in elucidating the function of the visual cortex. [Š] Suppose,
for example, that one actually found the apocryphal grandmother
cell. Would that really tell us anything much at all? It would
tell us that it existed ­ Gross¹s hand-detectors tell us almost
that ­ but not why or even how such a thing may be constructed
from the outputs of previously discovered cells. [Š] As one
reflected on these sorts of issues in the early 1970s, it
gradually became clear that something important was missing that
was not present in either of the disciplines of neurophysiology
or psychophysics. The key observation is that neurophysiology and
psychophysics have as their business to describe the behavior of
cells or subjects but not to explain such behavior. What are the
visual areas of the cortex actually doing? What are the problems
in doing it that need explaining, and at what level of description
should such explanations be sought? (pp. 14-15)
===

To continue:

===
The reason for this is that the nature of the computations that
underlie perception depends more upon the computational problems
that have to be solved than upon the particular hardware in which
their solutions are implemented. To phrase the matter another way,
an algorithm is likely to understood more readily by understanding
the nature of the problem being solved than by examining the
mechanism (and the hardware) in which it is embodied. In a similar
vein, trying to understand perception by studying only neurons is
like trying to understand bird flight by studying only feathers:
It just cannot be done. In order to understand bird flight, we have
to understand aerodynamics; only then do the structure of feathers
and the different shapes of birds¹ wings make sense. (p. 27)
===

Chomsky, reflecting on the failure of AI, reaches a similar
conclusion:

===
Much of the work in AI seems to me misguided, in that it is too
concreteŠ The AI approaches are too concrete in that they are much
too concerned with the actual algorithm ­ there may be many algorithms
to realize a particular computational theory, and the study of
algorithms requires a prior understanding of the structure of the
problem being addresses. Again, this is a point that Marr has
emphasized. To assume that your program is your theory is simply to
abandon any hope for understanding what people are doing. (p. 348)
===

===
In the days when Sussman was a novice, Minsky once came to him as he sat
hacking at the PDP-6.

"What are you doing?", asked Minsky.

"I am training a randomly wired neural net to play Tic-Tac-Toe", Sussman
replied.

"Why is the net wired randomly?", asked Minsky.

"I do not want it to have any preconceptions of how to play", Sussman said.

Minsky then shut his eyes.

"Why do you close your eyes?", Sussman asked his teacher.

"So that the room will be empty."

At that moment, Sussman was enlightened.
===

Moreover, even if your experiment worked, you wouldn't learn
anything about high-level cognition -- language understanding,
complex problem solving, etc.  Otherwise, why haven't animals
raised in human environments learned language, mathematics, etc.?
(And they haven't -- researchers have raised primates alongside
their own children with disappointing results.)  There is more
to high-level cognition than even superb perception and motor
capabilities.

  Evolution is not about survival of the fittest, but death of the unfit,
  which is quite a different story.  All sorts of things survive, but when
  some illness or catastrophy or other disastrous event occurs, a lot of
  individuals die.  It is entirely random (as far as survival pre-disaster
  is concerned) which factor allows individuals to survive the disaster.

--
Erik Naggum, Oslo, Norway

However, most things which are disasters for an individual or even a
group, are actually quite normal, repeated occurrences on an
evolutionary timescale. For example, when an individual survives an
illness, it's usually because its ancestors survived it on previous
occasions and passed on their genes for a well-adapted immune system.

So to answer the question of what factors drove a complex adaptation
such as intelligence to appear, one often needs to look for things
which might be disasters for an individual, but would be frequent
occurrences over the length of time during which the adaptation
evolved.

* Russell Wallace
| This is true if the disaster is something which recurs at intervals
| longer than normal evolutionary timescales. (E.g. an asteroid hitting
| the Earth.)

  Huh?  What does the timescale have to do with anything?

| However, most things which are disasters for an individual or even a
| group, are actually quite normal, repeated occurrences on an evolutionary
| timescale.  For example, when an individual survives an illness, it's
| usually because its ancestors survived it on previous occasions and
| passed on their genes for a well-adapted immune system.

  How is this not just what I said?

| So to answer the question of what factors drove a complex adaptation such
| as intelligence to appear, one often needs to look for things which might
| be disasters for an individual, but would be frequent occurrences over
| the length of time during which the adaptation evolved.

  Are you sure you read what I wrote?

  I am quite puzzled by your response, which looks like it tries to refute
  something, but does nothing to restate what I wrote as far as I can see.

--
Erik Naggum, Oslo, Norway