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One advantage of studying a focused corpus like the A.I.T. is that we
have an especially good chance of understanding some of these social
relations. A history of A.I. often begins with a seminal conference that
took place at Dartmouth in 1956 [McCorduck85] [Russell95] . If we treat the attendees
at that meeting as ``founding fathers'' (in a population genetic
sense!), we can attempt to track their ``genetic'' impact on the current
field of A.I.
In an attempt to capture other significant intellectual
influences beyond the advisor, the AIG questionnaire asks for committee
members other than the chairman. The role of committee members varies a
great deal from department to department, and campus to campus. But all
of these numbers can be expected to exert some intellectual influence as
well. Asking for committee members is a step towards other, non-advisor
influencors, and it is also a matter of record. Research institutions
are another way to capture intellectual interactions among
collaborators.
Even if/when the AI-PhD family tree is completed, it will
certainly not have captured all of what we mean by ``artificial
intelligence research.'' For example: \item The Dartmouth ``founding
fathers'' probably provide (direct) lineage for a {\em minority} of AI
PhD's currently working in the field.
PhD theses, themselves, are
probably some of the {\em worst} examples of research, in AI or
elsewhere. By definition, we are talking about students who are doing
some of their first work. They had {\em better} improve with time! \item
PhD's account for only a fraction of AI research.
Nevertheless, Science
is primarily concerned with {accumulating} knowledge, at least as much
as it is about its initial discovery. A primary argument for interest in
the AIG is that traditional academic relationships, as embodied by PhD
genealogies, form a sort of ``skeleton'' around which the rest of
scientific knowledge coalesces. Certainly individuals can pursue their
own research program, and corporations can fund extended investigations
into areas that are not represented in academia whatsoever. But it is
hard to imagine extended research programs (like that ``fathered'' by
Roger Schank, for example) that do not involve multiple ``generations''
of investigators; academia is almost certainly the most successful
system for such propagation. Kuhnian and post-Kuhnian [REF623] [REF588] analyses highlight the importance
of {\em social} aspects of Science. ``Paradigms'' are extremely
appealing constructs, but they're also amorphous. For all of its faults,
the AI-PhD tree represents incontrovertible {\em facts}, just as word
frequencies do.
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An emprical foundation for a philosophy of Science