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BEGIN:VEVENT
UID:4c3da0988dee994a96532bf3c3f73a71
CATEGORIES:Mathematical Physics Seminar
CREATED:20241110T190746
SUMMARY:Webinar: Scott Aaronson - AI Safety and Theoretical Computer Science
LOCATION:Zoom
DESCRIPTION:<p style="text-align: center;"><strong>Scott Aaronson – University of Texas
  at Austin</strong></p><p style="text-align: center;"><strong>&nbsp;</stron
 g></p><p style="text-align: center;"><strong>Wednesday,&nbsp;November 27th 
 ,&nbsp;10:45AM EST</strong></p><p style="text-align: center;"><strong>&nbsp
 ;</strong></p><p style="text-align: center;"><strong>AI Safety and Theoreti
 cal Computer Science</strong></p><p style="text-align: center;"><strong>&nb
 sp;</strong></p><p>Progress on AI safety and alignment, like the current AI
  revolution more generally, has been almost entirely empirical.&nbsp; In th
 is talk, &nbsp;however, I'll survey a few areas where I think theoretical c
 omputer science can contribute to AI safety, including:</p><p>&nbsp;- How c
 an we robustly watermark the outputs of Large Language Models and other gen
 erative AI systems, to help identify academic cheating, deepfakes, and AI-e
 nabled fraud?&nbsp; I'll explain my proposal and its basic mathematical pro
 perties, as well as what remains to be done.</p><p>- Can one insert undetec
 table cryptographic backdoors into neural nets, for good or ill?&nbsp; In w
 hat senses can those backdoors also be unremovable?&nbsp; How robust are th
 ey against fine-tuning?</p><p>&nbsp;- Should we expect neural nets to be "g
 enerically" interpretable? &nbsp;I'll discuss a beautiful formalization of 
 that question due to Paul Christiano, along with some initial progress on i
 t, and an unexpected connection to quantum computing.</p>
DTSTAMP:20260829T023909
DTSTART;TZID=America/New_York:20241127T104500
DTEND;TZID=America/New_York:20241127T120000
SEQUENCE:0
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