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Software 2.0: Machine Learning is Changing Software
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
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.
