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SUMMARY:The quiet AI revolution in weather forecasting - Professor Richard
  Turner\, Professor of Machine Learning\, Department of Engineering 
DTSTART:20231120T180000Z
DTEND:20231120T190000Z
UID:TALK205324@talks.cam.ac.uk
CONTACT:Beverley Larner
DESCRIPTION:Abstract: Over the last 18 months a quiet AI revolution has be
 gun in the field of numerical weather prediction. Medium-term weather pred
 iction involves forecasting several days to a couple of weeks in the futur
 e and these forecasts are critical for making many social and economic dec
 isions. The standard approach to this problem is to run detailed global si
 mulations of the earth's atmosphere using a supercomputer\, so-called nume
 rical weather prediction (NWP). As little as one year ago\, researchers in
  this field had thought it unlikely that machine learning approaches would
  be competitive with numerical weather prediction any time soon. However\,
  over the last year\, the same advances that underpin large language model
 s\, like ChatGPT\, have been applied to weather prediction. Surprisingly\,
  these models achieve a performance which is already competitive with stan
 dard NWP\, but with a computational cost that is 1000s of times cheaper. T
 he deep learning based forecasts have also been shown to be surprisingly r
 obust\, performing reasonably even when faced with rare or extreme events.
  Consequently\, weather prediction centres like the World Meteorological O
 rganisation and the European Centre for Medium-Range Weather Forecasts (EC
 MWF) are now racing to build machine learning teams and publicly testing A
 I forecasts. This talk will describe this quieter AI revolution and it wil
 l end with a discussion of the opportunities for AI and machine learning i
 n weather and climate\, and speak a little more widely about the balancing
  act that must be struck between regulation and adoption of AI technology.
LOCATION:Bristol-Myers Squibb Lecture Theatre\, Department of Chemistry
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