A Berkeley mash-up of AI approaches promises continuous learning ZDNet

The challenge of the latest work can be summed up as how to give neural networks an ability not just to generalize from one learned task to another, but to continually sharpen that ability to generalize over time, with exposure to new tasks. And, to do so with a minimum of data required as examples, given that many new tasks a neural network confronts over time may not have a lot of training data available, or, at least, not a lot of "labeled" training data. The result is described in a paper out last week, "Online Meta-Learning," posted on the arXiv pre-print server. The current research has echoes in Levine's other work that's closer to robotics per se. ZDNet back in October related how Levine trains robot simulations -- agents -- to infer movement from multiple frames of video from YouTube. There's a parallel with online meta-learning, in that the computer is learning how to extend its understanding across examples in time, sharpening its ability to understand, in a sense. The approach that lead authors Finn and Rajeswaran pursue is to combine two different approaches that the teams have explored extensively in recent years: meta-learning and online learning.