HAI Weekly Seminar with Chris Re
Software 2.0: Machine Learning is Changing Software
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Software 2.0: Machine Learning is Changing Software
This session is specifically designed for full-time graduate students within one year of obtaining their PhD, as well as current postdoctoral scholars, fellows, and researchers.

This session is specifically designed for full-time graduate students within one year of obtaining their PhD, as well as current postdoctoral scholars, fellows, and researchers.
Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.

Save the Date. Artificial intelligence is transforming how researchers collect, analyze, and learn from data. As AI systems become increasingly integrated into scientific discovery, business decision-making, and policy analysis, they are reshaping both the questions researchers can ask and the methods they use to answer them.
The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.

The rapid acceleration of AI comes with a profound wave of anxiety. Across every sector of society, people are facing unsettling questions about their worth and their place in a shifting world.
Software has been "eating the world" for the last ten years. In the last few years, a new phenomenon has started to emerge: machine learning is eating software. That is, machine learning is radically changing how one builds, deploys, and maintains software--leading some to use the loosely defined phrase Software 2.0. Rather than conventional programming, Software 2.0 systems often accept high-level domain knowledge or are programmed by simply feeding them copious amounts of data. We describe the foundational challenges that these systems present including a theory of weak supervision, guiding self-supervised systems, and high-level abstractions to monitor these systems over time. This builds on our experience with systems including Snorkel, Overton, and Bootleg, which are in use in flagship products at Google, Apple, and many more.