09: Gary Marcus -- Making AI More Human

AMLG: Gary I'm super excited to have you today, thanks for coming on the show. We first met a few years ago in New York when I was running a tech meetup, the Singularity society, and you kindly came and spoke. You've been a professor of psychology at NYU for many years where your work has focused on language, biology, and the human mind. You've spent decades studying how children learn, and then in 2015 you founded this startup called Geometric Intelligence, focused on mining cognitive psychology for insights into building better machine learning techniques. Just this past December you were acquired by Uber to run their newly founded AI labs -- congratulations on that exit. So your algorithms offer an alternative approach to what is now a very popular branch of machine learning, called deep learning. Let's talk about deep learning -- it's a sexy buzzword which is thrown into about every startup pitch I see these days, and many corporate presentations, so I'm sure listeners have heard the term. What it really is is a rebranding of an old technique of using neural nets, which dates back to the 50s. Neural nets basically mimic the human neocortex, and by feeding in massive amounts, gigabytes of data and using tons of computational power, the algorithms are able to recognize patterns. Part of the reason why this technique is back in vogue is the combination of increasingly powerful computers combined with the massive training datasets that companies are building up. So there's been a flurry of activity, and the Googles and Facebooks of the world are throwing resources at the technique. As just one example, Facebook, using the over 400 billion photos people have uploaded, has built something called DeepFace, an image recognition tool that's now better than humans at recognizing whether two different images are of the same person. Gary you are well known as a critic of this technique, you've said that it's over-hyped. That there's some low hanging fruit that deep learning's good at -- specific narrow tasks like perception and categorization, and maybe beating humans at chess, but you felt that this deep learning mania was taking the field of AI in the wrong direction, that we're not making progress on cognition and strong AI. Or as you've put it, "we wanted Rosie the robot, and instead we got the roomba."