Professor Jimmy Lin holds the David R. Cheriton Chair in the David R. Cheriton School of Computer Science at the University of Waterloo. Lin's research aims to build tools that help users make sense of large amounts of data. His work mostly lies at the intersection of information retrieval and natural language processing, with a focus on data-driven approaches and infrastructure issues. Although most of Lin's work deals with text, he's also worked on relational data, semi-structured data, log data, speech, and graphs. From 2010-2012, Lin spent an extended sabbatical at Twitter, where he worked on services designed to connect users with relevant content and analytics infrastructure to support data science. He currently serves as the Chief Scientist of RSVP.ai, a Waterloo-based startup that aims to build deep natural language understanding technologies to facilitate seamless dialogues between users and systems. Experience in academia and industry guides him in building useful applications that solve real-world user problems while addressing fundamental challenges in computer science and information science. A few years ago, Lin realized that big data wasn't cool anymore, so he switched to deep learning (just like everyone else these days).

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Public Audience

English

60 Minutes

Saarbrücken

Deep learning for tackling NLP and IR problems shares many common characteristics with alchemy and astrology from centuries past: both scientists and practitioners often don't know what they're doing. The "state of the art" today is full of folklore, secret tricks, superstitions, mysterious incantations, and other black magic that's necessary to "coax" a neural network into behaving. I'm not the only one who feels this way: I'll begin with overviews of recent discussions in the community along these lines, followed by my own experiences (vicariously through students) trying to gain a rigorous understanding of what is going on. My approach and philosophy can be captured thusly, to corrupt a quote by Antoine de Saint-Exupery: "It seems that true understanding is attained, not when there is nothing more to add, but when there is nothing more to take away". This talk presents work in progress, so don't come seeking resolution, cathartic revelations, or answers.

Petra Schaaf

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