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Jason Yosinski, "Good and bad assumptions in model design and interpretability"
Jason Yosinski

Sat Dec 08 06:30 AM -- 07:00 AM (PST) @

The seduction of large neural nets is that one simply has to throw input data into a big network and magic comes out the other end. If the output is not magic enough, just add more layers. This simple approach works just well enough that it can lure us into a few bad assumptions, which we’ll discuss in this talk. One is that learning everything end-to-end is always best. We’ll look at an example where it isn’t. Another is that careful manual architecture design is useless because either one big stack of layers will work just fine, or if it doesn’t, we should just give up and use random architecture search and a bunch of computers. But perhaps we just need better tools and mental models to analyze the architectures we’re building; in this talk we’ll talk about one simple such tool. A final assumption is that as our models become large, they become inscrutable. This may turn out to be true for large models, but attempts at understanding persist, and in this talk, we’ll look at how the assumptions we put into our methods of interpretability color the results.

Author Information

Jason Yosinski (Uber AI Labs; Recursion)

Dr. Jason Yosinski is a machine learning researcher, was a founding member of Uber AI Labs, and is scientific adviser to Recursion Pharmaceuticals and several other companies. His work focuses on building more capable and more understandable AI. As scientists and engineers build increasingly powerful AI systems, the abilities of these systems increase faster than does our understanding of them, motivating much of his work on AI Neuroscience: an emerging field of study that investigates fundamental properties and behaviors of AI systems. Dr. Yosinski completed his PhD as a NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, Caltech/NASA Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, XKCD, and on the BBC. Prior to his academic career, Jason cofounded two web technology companies and started a program in the Los Angeles school district that teaches students algebra via hands-on robotics. In his free time, Jason enjoys cooking, sailing, motorcycling, reading, paragliding, and sometimes pretending he's an artist.

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