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Re: [ontolog-forum] History of AI and Commercial Data Processing

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: Ed Barkmeyer <edbark@xxxxxxxx>
Date: Tue, 23 Jun 2009 18:46:05 -0400
Message-id: <4A415B2D.6050105@xxxxxxxx>
John Bottoms wrote:    (01)

> Clearly the future of AI, cognitive science or semantic processing
> must include tight coupling with real world problems.     (02)

I think the scope of this statement is too grand.
Do we regard AI as
  - primarily a (natural) science?
  - primarily a formal or philosophical discipline?
  - primarily an engineering discipline?    (03)

Cognitive science is a science at the neuroscience (natural science) 
level and at the psychological (social science) level.  It is a study of 
how things in nature and society behave, and is thus tightly coupled to 
the real world, but not necessarily to "real world problems".    (04)

Mathematics and semantics are primarily formal philosophical 
disciplines.  They are of themselves entirely about organizing 
abstractions.  They may have real world applications, but the nature of 
the discipline itself is entirely divorced from "real world problems". 
And some part of AI is a discipline of this kind.  30+ years ago, it was 
inclusion of disciplines of this kind that justified the term "computer 
science".    (05)

The development of AI technologies -- algorithms for reasoning -- is an 
engineering discipline.  Its objective is to produce useful tools for 
reasoning to effect about real-world problems.  In a similar way, 
"knowledge engineering" is about the capture of human knowledge in such 
a way as to enable the tools to reason to useful effect about real-world 
problems.  So, yes, this part of AI must be tightly coupled to the real 
world concerns.    (06)

>  I don't think we have a full
> understanding yet of how our large brains were justified.    (07)

Agree.    (08)

> In looking at commercial computing, it might be that we will only
> see some major developments in cognitive science when we have
> truly massive data sets that absolutely dictate processing
> efficiencies.    (09)

I can't make sense of this sentence, and I don't see any connection to 
the previous one.  I'm a bit slow; you'll have to fill in some of the 
mental leaps for me.    (010)

> Is that where cloud computing finds its raison d'etre?    (011)

<heresy>IMO, the principal raison d'etre for "cloud computing" was the 
need for a new buzzword to generate a new set of funding.  "SOA" and 
"net-centric" were fueling others.</heresy> ;-)    (012)

John Sowa wrote:    (013)

>> AI is dominated by brilliant people who are totally out of touch
>> with anything and everything that goes on in the field of commercial
>> data processing.     (014)

My gut reaction is: and rightly so.  Most commercial data processing is 
not very interesting.  The technologies needed to do it well were 
devised over the 30 years 1965-1995 and they are heavily and reliably used.    (015)

The really interesting commercial processing began to benefit from AI 
and OR technologies 30 years ago, and there is much more commercial use 
of AI (of the rule engine kind, and some others) in the last 15 years, 
as memory sizes and processing speeds have made it practical.    (016)

>> There is no question that many of their ideas
>> could, if properly implemented, revolutionize commercial systems.    (017)

I'm willing to believe this, but you would have to put a lot of effort 
into formalizing the reference knowledge for the domain.  The cost of 
enabling the great ideas to be useful is very high.  And the putative 
revolution has to produce a return on that investment.    (018)

An EU study ending in 2007 concluded that we now have a lot of AI 
tooling, but we don't have much encoded knowledge.  We haven't been 
training commercial knowledge engineers; doing that doesn't get advanced 
degrees.  But generating more AI tooling for toy knowledge bases does.
If we want to bring the benefits of AI to commercial data processing, we 
have to train students to do knowledge engineering, instead of tool 
building, and pay them to get their hands dirty.  FP7 is doing some of 
that in Europe.  What US funding source is?  (Jus' askin'.)    (019)

-Ed    (020)

Edward J. Barkmeyer                        Email: edbark@xxxxxxxx
National Institute of Standards & Technology
Manufacturing Systems Integration Division
100 Bureau Drive, Stop 8263                Tel: +1 301-975-3528
Gaithersburg, MD 20899-8263                FAX: +1 301-975-4694    (021)

"The opinions expressed above do not reflect consensus of NIST,
  and have not been reviewed by any Government authority."    (022)

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