Artificial intelligence, machine learning, and deep learning go back 60, 30, and 10 years, respectively. But their antecedents go much further back in time, drawing upon a centuries-old tradition of philosophical inquiry into the nature of intelligence, as well as a more controversial one over what is and is not a machine. But epistemology is only part of the problem; humans have often aggravated this gap in understanding by shrouding scientific knowledge behind ivory towers, paywalls, proprietary software, and private firms.
This talk will present a short history of this knowledge gap. It will peel off the glitter from these powerful machine learning algorithms, and show what kinds of (sometimes humorous, and sometimes dangerous) mistakes they are capable of, presenting case studies of neural nets in the wild: their interactions with people, and perhaps more interestingly, their interactions with each other. Adversarial nets, indeed!
