The first AGI will be Federated

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• Professor Nic Lane - Department of Computer Science and Technology, University of Cambridge
• Wednesday 27 November 2024, 15:05-15:55
• Lecture Theatre 1, Computer Laboratory, William Gates Building.

If you have a question about this talk, please contact Ben Karniely.

Abstract: As established scaling laws indicate, the future performance improvements of AI depend on the amount of computing and data sources 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 techniques in machine learning will be how the strongest LLMs (and foundation models more generally) are trained; and in time, aspirational capabilities like AGI will finally be achieved, in part, due to the adoption of federated methodologies. In this talk, I will describe why the future of AI will be federated, and describe early solutions developed by Flower Labs and CaMLSys that address the underlying technical challenges that the world will face as we shift from a centralized data-center mindset to de-centralized alternatives.

Bio: Nic Lane (http://niclane.org) is a full Professor in the department of Computer Science and Technology at the University of Cambridge and holds a Royal Academy of Engineering Chair in De-centralized AI. He is also a Fellow of St. John’s College. At Cambridge, Nic leads the Cambridge Machine Learning Systems lab (CaMLSys; https://mlsys.cst.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 federated learning framework. Flower Labs seeks to enable an AI future that is collaborative, open and decentralized. Nic has received multiple best paper awards, including ACM /IEEE IPSN 2017 and two from ACM UbiComp (2012 and 2015). In 2018 and 2019, he (and his co-authors) received the ACM SenSys 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 SIGMOBILE Rockstar award winner for his contributions to “the understanding of how resource-constrained mobile devices can robustly understand, reason and react to complex user behaviors and environments through new paradigms in learning algorithms and system design.”

Link to join virtually: https://cam-ac-uk.zoom.us/j/81322468305

A recording of this talk is available at the following link: https://www.cl.cam.ac.uk/seminars/wednesday/video/

This 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 in the lecture theatre. Any questions asked will also be included in the recording. The recording will be made available on the Department’s webpage

This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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