Patrick - (01)
>I attended a presentation on the use of ontologies at NASA and the
>speaker took great pains to point out that a single ontology (well,
>multiple ontologies but with mappings to a single master ontology) was a
>prerequisite to success. (02)
A prerequisite to success of what? (03)
The "ontology police" want to legislate that we build and then
conform OTEAO (Ontology to End All Ontologies), and if it isn't in
ONTEO, you are not allowed to say it. Like all caricatures, this one
has a basis in reality. There are some, unfortunately, who come all
too close to fitting the caricature. But most people I talk to have
realized by now that this is a dead end -- it is a recipe for failure
rather than a prerequisite to success, except in very limited and
highly constrained problems. (04)
>When I asked if it wasn't possible to have
>mappings between multiple ontologies that did not share a common basis,
>he said that was possible, but that it was a difficult problem. (05)
Do a google search on "ontology mapping" and you will see that it's a
*very* active research area. (06)
>...until that something is described in
> > ways that we can analyze with enough mathematical precision to be the
>> foundation of writing correct code, interoperation with TMs must
>> always be a matter of guesswork. Which is a poor basis on which to try
>> to build a planet-wide system of communication.
>>
>Is interoperation a matter of correct code? Or is it understanding the
>semantics of what is to be communicated? (07)
You need to understand precisely what it is you are communicating if
you are going to write correct code. If what you mean is not
precisely and formally defined, I might interpret it my way when I
write my software, and Pat might interpret it differently when he
writes his software, and our respective systems will give different
results when applied to the inputs you provide to us. (08)
>I think I have a better understanding of why you have placed such
>emphasis on a "common semantic base." And it would have (I think) the
>advantages that you ascribe for it, but at the cost (unknown) of
>excluding notations that don't share that "common semantic base." (09)
If a notation does not share a semantic base that enables me to
define what it means to process it correctly, then how can we
implement software to process it? You do it your way, I do it mine,
and we get inconsistent answers. Who adjudicates? (010)
>... 'Amplified Intelligence'.
> > Ken calls this 'human-centered computing': the idea is create machine
>> systems which can act as "cognitive prostheses" or amplifiers of human
>> abilities, so that the entire system of (person + AImachine) is
>> capable of more than either can achieve alone. I can go an about this
>> idea at length: too much length for this message. But the point I want
>> to get across is that it is helpful to think of AI methods, including
>> mechanical inference, as aids to people rather than competitors to
>> human dominance. Forget that damn silly Turing Test
>> (http://dli.iiit.ac.in/ijcai/IJCAI-95-VOL%201/pdf/125.pdf), and stop
>> worrying about the inhumanity of the machines. Backhoes and eyeglasses
>> aren't human either, but they are very useful muscle- and
>> vision-enhancers. What we need now are mind-enhancers :-) (011)
This parallels the debate that raged in the decision theory community
in the 70's about normative versus descriptive theories of decision
making. Ward Edwards, one of the founders of the field of decision
analysis, viewed decision analysis as a set of cognitive tools for
helping people to come closer to achieving the norm of logically
coherent decisions that serve their values. Notice that the norm is
*not* just logically coherent! Logical coherence for its own sake
can be disastrous, as history will attest. Just as you would not
think of building a house without a hammer and saw, why would we
think we can solve extremely challenging and complex decision
problems without tools? (012)
There were some in the early days of AI who disparaged decision
analysis because it involved probability and the maximization of
expected utility, which smacked of "number crunching" rather than the
AI ideal of symbolic computing. Papers on probability used to be
rejected by AI conferences because "AI is about symbols and not
numbers". Those days are long gone. There is now a very active
decision theoretic community within AI, to the benefit of both AI and
decision analysis. (013)
>...understood as you
>explain it, "human-centered computing" has much to offer and I really
>should do serious reading on it. (014)
We might all do better if our first reaction to someone disparaging
an entire field of study as wrongheaded, if we kept an open mind and
looked for the value that led thousands of people to devote their
professional lives to it. (015)
Kathy (016)
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