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From: markrose@spss.com (Mark Rosenfelder)
Subject: Re: Turing test (was Penrose and Searle)
Message-ID: <D0Cqoy.BLr@spss.com>
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References: <38tqh6$5qk@percy.cs.bham.ac.uk> <hubey.786306282@pegasus.montclair.edu> <D05IFt.CwK@spss.com> <hubey.786396076@pegasus.montclair.edu>
Date: Mon, 5 Dec 1994 19:12:32 GMT
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In article <hubey.786396076@pegasus.montclair.edu>,
H. M. Hubey <hubey@pegasus.montclair.edu> wrote:
>markrose@spss.com (Mark Rosenfelder) writes:
>>There are statistical tests to show whether or not a measuring system is
>>capable of making the discriminations asked of it.  (I helped write a
>>product here that makes such tests.)  Any measurement process will have
>>some fuzz-- some randomness in its results.  If the spread of this fuzz
>>is large enough in comparison with the thing measured, the measurement
>>process is useless.  
>
>Yes, so?

Nothing in your reply addresses this objection.  Your original posting 
claimed that errors in the test results don't matter because "It's a 
statistical test."  In fact statistical methods don't help a bit if a
measurement process cannot produce the discriminations that are asked of it.

Look this up in a good book on statistical process control (look under "Gage
R&R" or "Measurement Systems Analysis").  The statistic of interest is the
NDDC (number of distinct data categories), which tells how many categories
of data the process is capable of distinguishing.  If the NDDC for the
Turing Test is less than 2, it's incapable of distinguishing between
intelligent and non-intelligent systems.  

(A nice thing about the NDDC statistic, by the way, is that it doesn't
require a master value; it just relies on a collection of measurements
from the process being investigated.  In the case of the TT, this means
that you don't need to know whether a given measurement is "correct" or
not (whether the thing being tested really is intelligent).)

We don't know what the NDDC for the Turing Test is, of course.  Till we
do, it's a thought experiment, not a scientific test.
