TITLE: A Good Course in Epistemology
AUTHOR: Eugene Wallingford
DATE: March 06, 2018  4:11 PM
DESC: 
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BODY:
 Theoretical physicist Marcelo Gleiser, in
<A HREF="https://blogs.scientificamerican.com/cross-check/the-more-we-know-the-more-mystery-there-is/">
   The More We Know, the More Mystery There Is</A>:

<BLOCKQUOTE><EM>
 But even if we did [bring the four fundamental forces together
 in a common framework], and it's a big if right now, this
 "unified theory" would be limited.  For how could we be
 certain that a more powerful accelerator or dark matter
 detector wouldn't find evidence of new forces and particles
 that are not part of the current unification?  We can't.  So,
 dreamers of a final theory need to recalibrate their
 expectations and, perhaps, learn a bit of epistemology.  To
 understand how we know is essential to understand how much we
 can know.
</EM></BLOCKQUOTE>

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 People are often surprised to hear that, in all my years
 of school, my favorite course was probably PHL 440
 Epistemology, which I took in grad school as a cognate
 to my CS courses.  I certainly enjoyed the CS courses I
 took as a grad student, and as an undergrad, too, and but
 my study of AI was enhanced significantly by courses in
 epistemology and cognitive psychology.  The prof for PHL
 440, Dr. Rich Hall, became a close advisor to my graduate
 work and a member of my dissertation committee.  Dr. Hall
 introduced me to the work of 
<A HREF="https://en.wikipedia.org/wiki/Stephen_Toulmin">
   Stephen Toulmin</A>,
 whose model of argument influenced my work immensely.

 I still have the primary volume of readings that Dr. Hall
 assigned in the course.  Looking back now, I'd forgotten
 how many of
<A HREF="https://en.wikipedia.org/wiki/Willard_Van_Orman_Quine">
   W.V.O. Quine</A>'s
 papers we'd read... but I enjoyed them all.  The course
 challenged most of my assumptions about what it means "to
 know".  As I came to appreciate different views of what
 knowledge might be and how we come by it, my expectations
 of human behavior -- and my expectations for what AI could
 be -- changed.  As Gleiser suggests, to understand
 <EM>how</EM> we know is essential to understanding
 <EM>what</EM> we can know, and how much.

 Gleiser's epistemology meshes pretty well with my
 pragmatic view of science: it is descriptive, within a
 particular framework and necessarily limited by experience.
 This view may be why I gravitated to the pragmatists in my
 epistemology course (Peirce, James, Rorty), or perhaps the
 pragmatists persuaded me better than the others.

 In any case, the Gleiser interview is a delightful and
 interesting read throughout.  His humble of science may
 get you thinking about epistemology, too.

 ... and, yes, that's the person for whom
<A HREF="https://en.wikipedia.org/wiki/Quine_(computing)">
   a quine in programming</A>
 is named.  Thanks to
<A HREF="http://www.cs.uni.edu/~wallingf/blog/archives/monthly/2012-03.html#e2012-03-07T17_35_34.htm">
   Douglas Hofstadter</A>
 for coining the term and for giving us programming
 nuts a puzzle to solve in every new language we learn.
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