Can AI Tell the Difference Between a Polar Bear and a Can Opener?

Scarcely a day goes by without another headline about neural networks: some new task that deep learning algorithms can excel at, approaching or even surpassing human competence. As the application of this approach to computer vision has continued to improve, with algorithms capable of specialized recognition tasks like those found in medicine, the software is getting closer to widespread commercial use--for example, in self-driving cars. Our ability to recognize patterns is a huge part of human intelligence: if this can be done faster by machines, the consequences will be profound. Yet, as ever with algorithms, there are deep concerns about their reliability, especially when we don't know precisely how they work. State-of-the-art neural networks will confidently--and incorrectly--classify images that look like television static or abstract art as real-world objects like school-buses or armadillos. Specific algorithms could be targeted by "adversarial examples," where adding an imperceptible amount of noise to an image can cause an algorithm to completely mistake one object for another.