
From Aaron Mon Jan 29 00:25:10 GMT 1996
Newsgroups: comp.ai,comp.ai.philosophy
References: <4dtge9$566@spool.cs.wisc.edu> <4e2th9$lkm@cantaloupe.srv.cs.cmu.edu> <1996Jan23.184658.3864@media.mit.edu>
Subject: Re: who first used "scruffy" and "neat"?

[Some thoughts and reminiscences]

minsky@media.mit.edu (Marvin Minsky) writes:

> Date: Tue, 23 Jan 1996 18:46:58 GMT
> Organization: MIT Media Laboratory
>
> In article <4e2th9$lkm@cantaloupe.srv.cs.cmu.edu> Lonnie Chrisman <ldc+@cs.cmu.edu> writes:
> >so@brownie.cs.wisc.edu (Bryan So) wrote:

[BS]
> >>A question of curiosity.  Who first used the terms "scruffy" and "neat"?

See below

> >>And in what document?  How about "strong" and "weak"?

I think it was John Searle who first described a (rather confused)
distinction between Strong AI and Weak AI in his 1980 paper:

        John R Searle, `Minds Brains and Programs' in
        The Behavioral and Brain Sciences,
        3,3, 1980.

though before that there had been (for a long time) discussion of
the differences between trying to produce *simulations* of human
behaviour and trying to produce machines with *their own* mental
states.

Before Searle introduced those terms, people used to talk about the
difference between "simulation" and "replication" of mental
processes, I seem to recall. The general idea is pretty old. It also
marked a division between different kinds of AI researchers:

 1. Those who merely wished to produce machines that could do useful
    things which previously only people (and other animals) could
    do, i.e. simulating intelligence. (AI as engineering)

 2. Those who wished to replicate (and explain) mental states and
    processes, or at least wished to discover whether and how it
    might be done (which is NOT the same as wanting to do it).
    (AI as science and as philosophy)

There was a third group (strongest at CMU I think, led by Simon and
Newell):

 3. Those who wanted to *model* and explain the internal cognitive
    processes (i.e. not just simulate the external behaviour) but
    without necessarily claiming that the processes thus modelled
    were replicated. (AI as psychology)


John Haugeland used different terminology for talking about some of
the options (e.g. in his introduction to the book Mind Design) when
he asked whether computers, or their contents, could have "original
intentionality", like the mental states of people and other animals
or only "derivative intentionality" like books and records in filing
cabinets, which had meaning only because people interpreted them as
have meaning.

Dennett (see Brainstorms, 1978) appeared to claim that this was not
a question of FACT, but something to be settled by adopting "the
intentional stance" to the machines in question and finding out if
that proved more convenient than other stances. (What's convenient
can depend on your purposes.)

This later became (misleadingly) known as the "symbol grounding"
problem after Harnad introduced the latter phrase (which is
misleading because it starts by *presupposing* that meaning needs
some sort of "grounding", a view I have previously attacked in a
posting to comp.ai.philosophy, with which some people disagreed!).

Searle (in 1980, and in his BBC Reith lectures a few years later)
gave all this a confusing twist because of his insistence that
Strong AI was committed to getting semantics out of pure syntax,
which was a great surprise to many people working in AI, e.g.
building robots with TV cameras, motors, etc. I.e. they used a lot
more than pure syntax, and would not dream of relying only on
syntax.

[LC I think, wrote:]
> >Since I don't see a response yet, I'll take a stab.  The earliest use of
> >"scruffy" and "neat" that comes to my mind was in David Chapman's "Planning
> >for Conjunctive Goals", Artificial Intelligence 32:333-377, 1987.  "Weak"
> >evidence for this being the earliest use is that he does not cite any earlier
> >use of the terms, but perhaps someone else will correct me and give an
> >earlier citation.
>

[MM]
> I think it first appeared in a paper by Robert Abelson cqlled
> something like "Constraint, Construal and Cognitive Science".  A long
> time ago, but I don't recall the date.

I remember hearing Abelson giving a talk about this at the 1981
conference of the Cognitive Science Society (at Berkeley I think). I
think he then attributed the distinction to Roger Schank.

After an intellectual historian gets to work, many of these things
could turn out to have a much longer history than anyone remembers.

E.g. in the 50s (or earlier) there was much discussion of the
difference between mathematical proofs that were "perspicuous" and
those that were messy and hard to follow (See Wittgenstein's Remarks
on the Foundations of Mathematics). I don't know if the words "neat"
and "scruffy" were used in that context, but they might have been!
(Compare the recent proof of Fermat's last theorem?)

I think similar contrasts were often made regarding engineering
designs with varying degrees of economy, elegance, generality.

I.e. the neat/scruffy distinction may be much older than the labels.

E.g. during most of the 1970s there was an evident and conscious
difference of approach (neat vs scruffy) between most of the work
done at two of the leading AI labs: people at Stanford University
(and SRI?) (inspired by McCarthy and Nilsson, among others?) tended
to make a lot of use of logic, theorem provers and general purpose
methods (e.g. logic-based planners), whereas work on AI at MIT (led
by Minsky and Papert in those days) tended to be characterised by
the notion that clean and general methods of representation and
general-purpose algorithms could not work, so that a lot of
domain-specific knowledge and know-how and representational
apparatus was required.

(There were always exceptions at both places, I guess. E.g. Schank
came from Stanford (before he went to Yale) and he backed the
scruffies. Marr, at MIT in the late 70s, tried to make vision neat,
but that turned out not to work.)

The contrast took a strange twist in the 1980s with the rebirth of
connectionism, which made use of much neat mathematics as a basis
for designing neural networks, whose actual behaviour on non-trivial
problems turned out to be pretty scruffy and hard to understand.

A personal note:
I like, and wish-for, neat theories and neat systems, but, alas,
I think there's a neat theory to explain why ACTUAL working
intelligent systems will necessarily have a lot of scruffiness,
because of:

(a) the intractability of detecting inconsistences in complex
    information stores

(b) the inevitability of errors in a complex and changing and
    only partly perceived environment

(c) the intractability of the task of working out exactly what to
    undo when mistakes or inconsistencies are discovered, etc.
    (This partly follows from the fact that the general need for
    speed in a real-time system leads to lots of cache-ing
    of results of computations and derivations, for future use, so
    that mistaken theories and percepts can lead to LARGE numbers of
    incorrect items of information scattered around the system,
    which are then difficult to detect and undo, except in a piecemeal
    fashion as and when they cause trouble)

The inevitable consequence of this, except in infinitely fast
brains/computers, is steadily growing scruffiness throughout much of
one's life. And the same will be true for intelligent robots.

I think that's one reason why we are not immortal: it's easier to
make new models with relatively clean sheets and start again every
now and again than to cope with patching ever-increasing mess.

Alan Bundy and I had a debate about neat and scruffy AI at a
conference in 1989, published in the conference proceedings:

    Evolving Knowledge in Natural Science and Artificial Intelligence,
    eds J.E.Tiles, G.T.McKee, G.C.Dean,
    London: Pitman, 1990

Alan was more optimistic about the prospects for neatness than I
was. What may have been a nearly final draft of my contribution can
be found in:

    http://www.cs.bham.ac.uk/~axs/misc/scruffy.ai.text

Aaron
---

From article: 28146 in comp.ai.philosophy
Newsgroups: comp.ai.philosophy
Message-ID: <4elob8$dqs@usenet.srv.cis.pitt.edu>
References: <4dtge9$566@spool.cs.wisc.edu> <4e2th9$lkm@cantaloupe.srv.cs.cmu.edu> <1996Jan23.184658.3864@media.mit.edu> <4eh44k$jf3@percy.cs.bham.ac.uk>
Date: 30 Jan 1996 18:34:16 GMT
Organization: University of Pittsburgh
Subject: Re: who first used "scruffy" and "neat"?
From: andersw+@pitt.edu (Anders N Weinstein)

In article <4eh44k$jf3@percy.cs.bham.ac.uk>,
Aaron Sloman <A.Sloman@cs.bham.ac.uk> wrote:
>
>Searle (in 1980, and in his BBC Reith lectures a few years later)
>gave all this a confusing twist because of his insistence that
>Strong AI was committed to getting semantics out of pure syntax,
>which was a great surprise to many people working in AI, e.g.
>building robots with TV cameras, motors, etc. I.e. they used a lot
>more than pure syntax, and would not dream of relying only on
>syntax.

Um, isn't this just "syntax" (computation) plus "grounding", (causal
embedding at the input and output sides)? If so, I don't understand
your point against Searle, for two reasons.

(1) you pooh-poohed the need for grounding earlier in your note. Why?
Although I do not myself accept any form of computationalism as a
theory of the nature of ordinary language mental states (belief,
intention, etc), I do think that the only version worth talking about
would be a grounded version. Only causal embedding can determine any
reference to anything in the world, which is what original
intentionality requires.

I had thought from the prominence of the "control system" concept in
your writing that you would be sympathetic to this line of thought,  since
precisely the difference between a "control system" and an arbitrary
computer program is the fact that the former is causally embedded, i.e.
actually controlling something non-symbolic.

(2) In any case, Searle's argument does squarely address the idea of
tacking on real causal embedding at the periphery of the computation
(the Robot Reply).

It seems to me that if your surprised AI workers believe any version of
computationalism as a theory of the nature of intentional states then
the Searle argument is relevant. Of course if they don't, then I
suppose they are, like most AI workers, practicing "weak" (merely
technological) AI as Searle understands it.

Obviously Searle is only interested in the philosophical thesis -- that
thought and computation are "radically the same" (Haugeland) -- which
he took to be the official party line of a certain movement. I think,
in fairness to Searle, that many philosophically inclined AI workers
and cognitive scientists really do believe this.
