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SUMMARY:The first AGI will be Federated - Professor Nic Lane - Department 
 of Computer Science and Technology\, University of Cambridge
DTSTART:20241127T150500Z
DTEND:20241127T155500Z
UID:TALK221089@talks.cam.ac.uk
CONTACT:Ben Karniely
DESCRIPTION:Abstract: As established scaling laws indicate\, the future pe
 rformance improvements of AI depend on the amount of computing and data so
 urces we can leverage. Where will we get the necessary compute and data to
  drive the continued advances in AI that the world now has grown to expect
 ? I believe all roads lead to federated learning\, and approaches of this 
 kind. In the relatively near future\, decentralized and federated techniqu
 es in machine learning will be how the strongest LLMs (and foundation mode
 ls more generally) are trained\; and in time\, aspirational capabilities l
 ike AGI will finally be achieved\, in part\, due to the adoption of federa
 ted methodologies. In this talk\, I will describe why the future of AI wil
 l be federated\, and describe early solutions developed by Flower Labs and
  CaMLSys that address the underlying technical challenges that the world w
 ill face as we shift from a centralized data-center mindset to de-centrali
 zed alternatives. \n\nBio: Nic Lane (http://niclane.org) is a full Profess
 or in the department of Computer Science and Technology at the University 
 of Cambridge and holds a Royal Academy of Engineering Chair in De-centrali
 zed AI. He is also a Fellow of St. John’s College. At Cambridge\, Nic le
 ads the Cambridge Machine Learning Systems lab (CaMLSys\; https://mlsys.cs
 t.cam.ac.uk/). The mission of CaMLSys is to invent the next-generation of 
 breakthrough ML-centric systems. Alongside his academic roles\, Nic is the
  co-founder and Chief Scientific Officer of Flower Labs (https://flower.ai
 )\, a venture-backed AI company (YCW23) behind the Flower open-source fede
 rated learning framework. Flower Labs seeks to enable an AI future that is
  collaborative\, open and decentralized. Nic has received multiple best pa
 per awards\, including ACM/IEEE IPSN 2017 and two from ACM UbiComp (2012 a
 nd 2015). In 2018 and 2019\, he (and his co-authors) received the ACM SenS
 ys Test-of-Time award and ACM SIGMOBILE Test-of-Time award for pioneering 
 research\, performed during his PhD thesis\, that devised machine learning
  algorithms used today on devices like smartphones. Nic was the 2020 ACM S
 IGMOBILE Rockstar award winner for his contributions to “the understandi
 ng of how resource-constrained mobile devices can robustly understand\, re
 ason and react to complex user behaviors and environments through new para
 digms in learning algorithms and system design.” \n\nLink to join virtua
 lly: https://cam-ac-uk.zoom.us/j/81322468305\n\nA recording of this talk i
 s available at the following link: https://www.cl.cam.ac.uk/seminars/wedne
 sday/video/\n\nThis talk is being recorded. If you do not wish to be seen 
 in the recording\, please avoid sitting in the front three rows of seats i
 n the lecture theatre. Any questions asked will also be included in the re
 cording. The recording will be made available on the Department’s webpag
 e
LOCATION:Lecture Theatre 1\, Computer Laboratory\, William Gates Building
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