Research talk 6 - Active Optimization for Embedded Learning Systems
An excellent research talk from Jeff Schneider at CMU. He presented his research on active learning as well as some amazing applications on embedded systems, including the recently emerged Uber self-driving car.
The active optimization mainly concerns the problem of learning and optimizing in an ever-changing environment, so it’s a perpetual on-line activity rather than a one-off task. He started with the development of the autonomous vehicle technologies, in which CMU has been making great progresses. The changes on car and technology, e.g. more data, more computing power, more precise and less costly sensors, bring opportunities of developing a real self-driving car.
The active optimization methods are the main topic of this talk, and also one of the most important algorithms driving the Uber self-driving car. Jeff introduced the basic idea of active optimization and an interesting application robot snake. However the classic models are not capable of handling the high dimensionality and multi-fidelity environment. So he referred to two algorithms which focus on scaling up the dimensionality and managing multi-fidelity evaluations. These algorithms work well on different tasks and are beneficial to autopilot.