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From: David@longley.demon.co.uk (David Longley)
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Subject: Re: Grounding Representations: ("Grounding" is the wrong word)
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In article <3nk7t4$mnu@mp.cs.niu.edu> rickert@cs.niu.edu "Neil Rickert" writes:

> If you are referring to the human brain as a badly designed computer
> system, I must disagree.  It is badly designed only from the
> perspective of some specific problems, most of which are not central
> to human existence.  Yet that same brain was able to pose these
> problems, and to construct computers which could solve the problems.
> The electronic computers we have constructed do not come close.
> 
> 
Yes, of course,  you're  right. What I was really suggesting was that from a
formal, Church-Turing  perspective, it's  not a very  good machine. To start
with, it's  not  very good at number  crunching. What  *IS* important to get
across to people is the fact that *science* as a body of objective knowledge
is a far better framework to work within than that of intuition. 

    'Humans  did not "make it to the moon" (or  unravel  the 
    mysteries of the double helix or deduce the existence of 
    quarks)    by    trusting    the    availability     and 
    representativeness  heuristics  or  by  relying  on  the 
    vagaries of informal data collection and interpretation. 
    On the contrary, these triumphs were achieved by the use 
    of formal research methodology and normative  principles 
    of  scientific inference. Furthermore, as  Dawes  (1976) 
    pointed out, no single person could have solved all  the 
    problems involved in such necessarily collective efforts 
    as  space exploration. Getting to the moon was  a  joint 
    project, if not of 'idiots savants', at least of savants 
    whose  individual  areas  of  expertise  were  extremely 
    limited  -  one savant who knew a great deal  about  the 
    propellant  properties of solid fuels but  little  about 
    the  guidance capabilities of small  computers,  another 
    savant  who  knew  a  great  deal  about  the   guidance 
    capabilities  of small computers but  virtually  nothing 
    about  gravitational effects on moving objects,  and  so 
    forth.  Finally,  those  savants  included  people   who 
    believed  that  redheads are  hot-tempered,  who  bought 
    their  last  car  on the  cocktail-party  advice  of  an 
    acquaintance's brother-in-law, and whose mastery of  the 
    formal  rules  of scientific inference did  not  notably 
    spare  them  from  the  social  conflicts  and  personal 
    disappointments experienced by their fellow humans.  The 
    very   impressive  results  of  organised   intellectual 
    endeavour, in short, provide no basis for  contradicting 
    our    generalizations    about    human     inferential 
    shortcomings.  Those accomplishments are collective,  at 
    least in the sense that we all stand on the shoulders of 
    those  who have gone before; and most of them have  been 
    achieved  by  using normative  principles  of  inference 
    often  conspicuously  absent from  everyday  life.  Most 
    importantly,  there is no logical contradiction  between 
    the  assertion  that  people can  be  very  impressively 
    intelligent on some occasions or in some domains and the 
    assertion that they can make howling inferential  errors 
    on other occasions or in other domains.'
    
    R. Nisbett and L. Ross (1980)
    Human  Inference: Strategies and Shortcomings of  Social 
    Judgment

My general conclusion is that *many* psychologists have in   fact 
misread what their discipline has all been about since the 1960s. 
As an ampirical discipline, and in contrast to the technology  of 
AI,  it  is  a catalogue of the failings  of  a  system  (*human* 
information    processing)   optimised   for   fault    tolerance 
biologically,  but minimally rational without the support of  the 
ediface of scientific method.

Grounding of 'representations', must, I believe, come from a reli
ance on extensionality (cf. Quine 1992). Fodor may be right  that
that Methodological Solipsism is the strategy for  psychology  to
follow as an empirical  discipline  ('description of disasters'), 
but surely AI must  stay on the hard track of where the Predicate
Calculus takes us?

Quine *does* seem to be a voice in the wilderness on this point.

-- 
David Longley
