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SUMMARY:Meeting the Challenges of Research Integrity and Knowledge Assimil
 ation - Thomas Dietterich (Oregon State University)
DTSTART:20260330T150000Z
DTEND:20260330T151500Z
UID:TALK245983@talks.cam.ac.uk
DESCRIPTION:Machine-learning-based AI is trained to mimic human research o
 utputs. This challenges our traditional methods for assessing published re
 search\, because we have often relied on secondary signals (writing fluenc
 y) to set our degree of trust in the results. Fortunately\, in mathematics
  it is becoming possible to mechanically check proofs\, which largely meet
 s this challenge. AI is also likely to increase the rate at which new rese
 arch is created and published. How can human mathematicians assimilate all
  of these new results? In other fields where research has exploded\, resea
 rchers have become increasingly specialized. This increases the need for s
 cholars who can summarize results and communicate them across the speciali
 zed subfields. We will need more mathematicians to write review articles\,
  monographs\, and textbooks. Perhaps other forms of summarization and comm
 unication\, such as knowledge bases\, may become valuable. Can AI tools he
 lp with this process?
LOCATION:Seminar Room 1\, Newton Institute
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