The Big World Hypothesis and its Ramifications for Artificial Intelligence
Keywords: Big world hypothesis, Continual learning, Online learning
Abstract: The big world hypothesis says that for many learning problems, the world is multiple orders of magnitude larger than the agent. The agent neither fully perceives the state of the world nor can it learn the correct value or optimal action for each state. It has to rely on approximate solutions to achieve its goals. In this paper, we make a case for embracing the big world hypothesis. We argue that even as computational resources grow, the big world hypothesis remains relevant. We conclude by discussing the implications of accepting the big world hypothesis on the design and evaluation of algorithms.
Submission Number: 30
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