演讲摘要：Software has been "eating the world" for the last ten years, which has had profound changes for how science, business, and government operate. In the last few years, a new phenomenon has started to emerge: machine learning has begun to eat software. Machine learning is radically changing how one builds, deploys, and maintains software--leading some to use the loosely defined phrase Software 2.0. One example difference is that in Software 2.0 systems one expresses domain knowledge by creating and shaping training sets (examples) instead of programmatically as in more classical software development. This talk describes our work on Snorkel, a first-of-its-kind prototype Software 2.0 system, that is in use with academic collaborators and at some of the largest corporations in the world. Our goal is to understand the foundational theoretical challenges posed by these new systems and practically understand how these new techniques changes not only how large companies build software, but how it may enable scientists and academics to more rapidly build machine-learning based applications.
讲者简介：Christopher (Chris) Re is an associate professor in the Department of Computer Science at Stanford University. He is in the Stanford AI Lab and is affiliated with the Statistical Machine Learning Group. His recent work is to understand how software and hardware systems will change as a result of machine learning along with a continuing, petulant drive to work on math problems. Research from his group has been incorporated into scientific and humanitarian efforts, such as the fight against human trafficking, along with widely used products from technology and enterprise companies including Google Ads, GMail, YouTube, and Apple. He has cofounded four companies based on his research into machine learning systems,SambaNova and Snorkel, along with two companies that are now part of Apple, Lattice (DeepDive) in 2017 and Inductiv (HoloClean) in 2020. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016. His research contributions have spanned database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016.
第37届CCF中国数据库学术会议（NDBC 2020）
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