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SUMMARY:Human-machine mathematical collaboration with modern AI - Virtual 
 presentation - Geordie Williamson (University of Sydney)
DTSTART:20260401T090000Z
DTEND:20260401T100000Z
UID:TALK245737@talks.cam.ac.uk
DESCRIPTION:I grew up fascinated by computers. However\, as I trained as a
  mathematician\, I was consistently surprised by how little use I could ge
 t out of computers. Most examples I cared about were simply intractable. I
 n some examples\, I could compute too much\, and I had no idea where to lo
 ok in the output. Often visualizing the output in any reasonable way was i
 mpossible\, or would involve many weeks of careful programming. In collabo
 ration with DeepMind\, I worked on some of the first applications of neura
 l networks to problems in pure mathematics. Here the potential is obvious\
 , but the engineering difficulties are real\, necessitating collaboration 
 between mathematicians and engineers.\n&nbsp\;\nModern tools (particularly
  coding agents\, and the *Evolve algorithms) provide a genuine paradigm sh
 ift. Suddenly\, it has become much easier to run experiments\, visualize d
 ata\, and connect powerful systems. I will give an overview of some of the
  work I&rsquo\;ve been involved recently. The emphasis will be on three as
 pects: a) the essential role played by specialized software (LP solvers\, 
 SageMath\, Magma\, GAP\, &hellip\;)\; b) the skillset needed to do this wo
 rk is significantly different to that of the typical working pure mathemat
 ician\, which presents challenges for our educational programs at all leve
 ls\, c) the emerging challenges around access to models and compute\, whic
 h threaten the fundamental democracy of mathematics.
LOCATION:Seminar Room 1\, Newton Institute
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