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VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:AI meets Source Code: status quo and outlooks
DTSTART:20220720T140000
DTEND:20220720T150000
DTSTAMP:20260504T213841Z
UID:14e84b2b2d11baa4bee8e3f866fe0cf21e4d7b356f74868fea851b69
CATEGORIES:Conferences - Seminars
DESCRIPTION:Michele Catasta\nIn 2011\, Marc Andreessen (A16Z) famously wro
 te a prescient claim that “software is eating the world”.  In 2017\,
  Jensen Huang (Nvidia CEO) followed up with "software is eating the worl
 d\, but AI is going to eat software".  Fast forward 5 years\, and this te
 ch-driven "banquet" seems to be bound to happen\, thanks to the exponentia
 l growth of Deep Learning.  Researchers from the ML and PL communities ha
 ve joined efforts to advance the AI4Code field at a breakneck pace\, while
  several companies have recently released their own flavor of AI pair-prog
 rammers (e.g.\, GitHub Copilot\, Amazon Code Whisperer\, etc.)\nIn this 
 talk\, I will give an overview of the novel tasks enabled by AI4Code\, wi
 th a focus on 2 of my recent works:\n- Code Transformer (ICLR 2021): a Sot
 A\, language-agnostic encoder model for representation learning of source
  code\;\n- PaLM: Scaling Language Modeling with Pathways (2022): a 540-b
 illion parameter\, dense decoder-only transformer model with breakthrough 
 capabilities on code tasks.\nI will discuss the scientific and technical i
 nsights that led to the success of Large Language Models of Code (e.g.\,
  OpenAI Codex\, Google PaLM\, etc.)\, while at the same time highlightin
 g current limitations and future research directions.\n\nMichele (pirroh) 
 Catasta is Head of Applied Research at X\, the moonshot factory (formerly 
 Google[x])\, where he is focusing on AI applied to Source Code. Previously
 \, he worked at Stanford University as a Research Scientist and Instructor
 \, with affiliations to the Statistical Machine Learning group and SNAP (P
 rof. Jure Leskovec). Michele graduated at EPFL with a Ph.D. in Computer Sc
 ience and worked also for MIT Media Lab\, Google\, and Yahoo Labs. His res
 earch expertise encompasses Machine Learning applied to different domains-
 -from Information Retrieval to Recommender Systems--and data types--from s
 ource code to graph data. In the past few years\, he has been covering the
  role of Advisor and Early Investor for several AI startups.\n\nNot on cam
 pus? Follow this talk on ZOOM.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420 https://epfl.zoom.us/
 j/66428467576?pwd=WGV0K2dFbVMwRGVWRUI1SVA1VXdWdz09
STATUS:CONFIRMED
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