Just saw this: https://identi.ca/renatocan/note/NAtsLZcKSiqSqXBvXITsvg

"Slight Street Sign Modifications Can Completely Fool Machine Learning Algorithms http://spectrum.ieee.org/cars-that-think/transportation/sensors/slight-street-sign-modifications-can..."

My comments:

I was just talking to a smart bunch of SMU students about this yesterday. It's a subtle but powerful attack. At a recent meeting of machine learning scientists, it became clear to all in the audience that without a theory of learning (a real mathematical framework that explains how to optimally design architectures with a goal in mind) or without an alternative to "gradient-descent" based learning update methods, this attack currently cannot be automatically defended against. It's a trial-and-error race to understand why this works and how to protect against it.

This attack is not new, and it was inevitable that someone would find a way to slightly alter street signs to mess with the image recognition net. Now the arms race is on. A theory of learning or an alternative to gradient descent would greatly aid this.

#pumpiverse