Recent advances in AI have yielded some impressive results. The use of deep neural networks in particular have led to astounding gains; machines have now approached or surpassed human ability at tasks such as object recognition, video games, and board games.
Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. Humans still excel at learning quickly from only a handful of examples, and applying knowledge to novel situations.
Can reverse engineering human intelligence usefully inform AI and machine learning? The authors believe so, and offer a set of core ingredients that could pave the way for even more human-like, and powerful, AI.
Stephen Spalding is a technologist, cyclist, and Chattanooga native. Over his professional career, he’s enjoyed working with 3d x-ray machines at GE Healthcare, nuclear reactors at TVA, Clojure services at OpenTable, and serving tables at Boathouse Rotisserie. Recently, he has joined the engineering team at Netflix to help deliver continuous streams of entertainment for more compliant humans, amenable to our robot overlords.
LINK TO PAPER: Building Machines That Think and Learn Like People (https://arxiv.org/pdf/1604.00289v3.pdf)