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SUMMARY:Cambridge Ellis Seminar Series- Dr. Nicholas Lane- 30 May 2022- 2 
 pm - Speaker to be confirmed
DTSTART:20220530T130000Z
DTEND:20220530T140000Z
UID:TALK174968@talks.cam.ac.uk
CONTACT:Kimberly Cole
DESCRIPTION:The Cambridge ELLIS Unit has started a Seminar Series that wil
 l include talks by leading researchers in the area of machine learning and
  AI. Our next speaker of the year will be Dr. Nicholas Land. Details of hi
 s talk can be found below.\n\n \n\nTitle: “The Machine Learning Data Cen
 ter is a Cancer: What is the Cure?”\n\n \n\nAbstract: The vast majority 
 of machine learning (ML) occurs today in a data center. But there is a ver
 y real possibility that in the (near?) future\, we will view this situatio
 n similarly to how we now view lead paint\, fossil fuels and asbestos: a t
 echnological means to an end\, that was used for a time because\, at that 
 stage\, we did not have viable alternatives – and we did not fully appre
 ciate the negative externalities that were being caused. Awareness of the 
 unwanted side effects of the current ML data center centric paradigm is bu
 ilding. It couples to ML an alarming carbon footprint\, a reliance to bias
 ed close-world datasets\, serious risks to user privacy – and promotes c
 entralized control by large organizations due to the assumed extreme compu
 te resources. In this talk\, I will sketch some thoughts regarding how a d
 ata center free future for ML might come about\, and how some of our recen
 t research results (e.g.\, http://flower.dev) might offer a foundation alo
 ng this path.\n\nBio: Nic Lane (http://niclane.org) is an Associate Profes
 sor in the department of Computer Science and Technology at the University
  of Cambridge where he leads the Machine Learning Systems Lab (CaMLSys —
  http://http://mlsys.cst.cam.ac.uk/). Alongside his academic role\, Nic is
  the Lab Director at Samsung AI in Cambridge. This 50-person lab studies a
  variety of open problems in ML\, and in addition to leading the lab — h
 e personally directs teams focused on distributed and on-device forms of l
 earning. 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 SI
 GMOBILE Test-of-Time award for pioneering research\, performed during his 
 PhD thesis\, that devised machine learning algorithms used today on device
 s like smartphones. Most recently\, Nic was the 2020 ACM SIGMOBILE Rocksta
 r award winner for his contributions to “the understanding of how resour
 ce-constrained mobile devices can robustly understand\, reason and react t
 o complex user behaviors and environments through new paradigms in learnin
 g algorithms and system design.”\n\nhttps://eng-cam.zoom.us/j/8682052280
 2?pwd=rCEqeC3Q8ivTHUfNq7H-JGUnCxWLSm.1
LOCATION:https://eng-cam.zoom.us/j/86820522802?pwd=rCEqeC3Q8ivTHUfNq7H-JGU
 nCxWLSm.1
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