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AI methods are advancing across a range of applications from computer vision and natural language processing to autonomous control. There are many facets to AI’s capabilities that determine how useful it is in our lives. Besides the obvious metrics of peak accuracy or efficacy of an AI system at its task, other facets include: How effectively can it learn a new task from a small amount of data or experience? Can it perform, or even learn, within the limited hardware and battery power available on a handheld device?
TL;DR: We propose channel coding as a novel benchmark to study several aspects of meta-learning, including the impact of task distribution breadth and shift on meta-learner performance, which can be controlled in the coding problem.
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