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SUMMARY:Technology Readiness Levels for Machine Learning Systems - Alex La
 vin - Latent Sciences
DTSTART:20201120T131500Z
DTEND:20201120T140000Z
UID:TALK154228@talks.cam.ac.uk
CONTACT:Francisco Vargas
DESCRIPTION:*Paper:*\n\nNA\n\n\n*Abstract:*\n\nThe development and deploym
 ent of machine learning systems can be executed easily with modern tools\,
  but the process is typically rushed and means-to-an-end. The lack of dili
 gence can lead to technical debt\, scope creep and misaligned objectives\,
  model misuse and failures\, and expensive consequences. Engineering syste
 ms\, on the other hand\, follow well-defined processes and testing standar
 ds to streamline development for high-quality\, reliable results. The extr
 eme is spacecraft systems\, where mission critical measures and robustness
  are engrained in the development process. Drawing on experience in both s
 pacecraft engineering and AI/ML research through product\, we have develop
 ed a proven systems engineering approach for machine learning development 
 and deployment. Our "Technology Readiness Levels for Machine Learning" (TR
 L4ML) framework defines a principled process to ensure robust systems whil
 e being streamlined for ML research and product\, including key distinctio
 ns from traditional software engineering. Even more\, TRL4ML defines a lin
 gua franca for people across teams and organizations to work collaborative
 ly on AI/ML technologies. In this talk I elucidate TRL4ML with several rea
 l world use-cases developing ML algorithms and models from basic research 
 through productization and deployment.\n\n*Keywords:* Systems ML\, Develop
 ment and deployment\, software engineering.\n\n*About the Speaker:*\n\nAle
 xander Lavin is a leading AI researcher and software engineer\, specializi
 ng in probabilistic machine learning and human-centric AI systems. Lavin f
 ounded Latent Sciences\, a startup commercializing his patented AI platfor
 m for predictive modeling neurodegenerative diseases\, which was acquihire
 d into a stealth enterprise AI company where he served as Chief Scientist.
  Previously he was a Senior Research Engineer at both Vicarious AI and Num
 enta\, building artificial general intelligence for robotics\, and develop
 ing biologically-derived ML algorithms\, respectively. Lavin used to work 
 in spacecraft systems\, and he is now an AI Advisor for NASA. Lavin earned
  his Masters in Mechanical Engineering at Carnegie Mellon\, a Masters in E
 ngineering Management with Duke University\, and Bachelors in Mech&Aero En
 gineering at Cornell University. He has won several awards for work in spa
 ce robotics and rocket propulsion\, published in top journals and conferen
 ces across AI/ML and neuroscience\, and was a Forbes 30 Under 30 honoree i
 n Science. In his free time\, Lavin enjoys running\, yoga\, live music\, a
 nd reading sci-fi and theoretical physics books.\n\n*Website:* NA\n\nPart 
 of ML@CL Seminar Series focusing on early career researchers in topics rel
 evant to machine learning and statistics.
LOCATION:https://dtudk.zoom.us/j/66026628895
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