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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Assyr Abdulle Lecture 2026
DTSTART:20260928T163000
DTEND:20260928T183000
DTSTAMP:20260920T014410Z
UID:2b050932f4da2a7c11a191890ccd95e780183eb41443437dd58ba1fe
CATEGORIES:Conferences - Seminars
DESCRIPTION:Professor Weinan E\nFor the 2026 Assyr Abdulle Lecture\, we 
 will be happy to welcome Professor Weinan E  from Peking University.\nT
 he lecture will take place on Monday 28 September 2026 from 16:30 to 17:3
 0 at the Rolex Learning Centre\, Forum Rolex (RLC E1 240)\, and will be f
 ollowed by an informal aperitif.\n\nTitle: Mathematics\, Science and Artif
 icial intelligence\nSince Newton established modern science and mathematic
 s\, tremendous progress has been made on both fronts by formulating the fi
 rst principles of science as mathematical objects\, developing the mathema
 tical tools needed\, and using them to solve scientific problems. One cons
 equence is that we can now label a large part of mechanics as “engineeri
 ng“\, where mathematical and computational tools are very effective.  H
 owever\, many problems\, such as drug discovery\, the design of new materi
 als or catalysts\, still remain difficult and resist mathematical or compu
 tational treatment. The difference between these two kinds of problems is 
 that the former\, the ones that we can handle\, are “easy”\, and the l
 atter\, the ones that remain difficult\, are “complex”.\nThis complexi
 ty comes in several aspects: high dimensionality\, non-locality\, large hi
 dden space\, etc. Classical mathematical tools\, such as polynomials and d
 ifferential equations\, are ineffective in these situations.\nDeep learnin
 g has brought us new hope. This is a new class of mathematical tools that 
 seem to be able to handle complex problems in artificial intelligence. By 
 extending these methods to science\, we have entered the new era of “AI 
 for Science”. This is indeed a paradigm change for science. Tremendous p
 rogress has already been made and much more is still to come.\nYet the iro
 ny is that AI itself is a singular subject that relies almost entirely on 
 experience\, on trial and error. It lacks the guidance from first principl
 es. For this reason\, it suffered severe ups and downs in the past\, and i
 t has become an extremely costly business nowadays. Why is this so? Is it 
 really the case that the first principles of AI are just too difficult and
  out of reach at the present time?\nIn this talk\, we will go through thes
 e topics with a focus on the issue of complexity: The complexity that we n
 eed to deal with in scientific research\, AI as an effective tool to deal 
 with complex problems\, and the new mathematics that needs to be developed
  to handle complexity.
LOCATION:RLC E1 240 https://plan.epfl.ch/?room==RLC%20E1%20240
STATUS:CONFIRMED
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