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A fascinating hypothesis is that human and animal intelligence could beexplained by a few principles (rather than an encyclopedic list of heuristics).If that hypothesis was correct, we could more easily both understand our ownintelligence and build intelligent machines. Just like in physics, theprinciples themselves would not be sufficient to predict the behavior ofcomplex systems like brains, and substantial computation might be needed tosimulate human-like intelligence. This hypothesis would suggest that studyingthe kind of inductive biases that humans and animals exploit could help bothclarify these principles and provide inspiration for AI research andneuroscience theories. Deep learning already exploits several key inductivebiases, and this work considers a larger list, focusing on those which concernmostly higher-level and sequential conscious processing. The objective ofclarifying these particular principles is that they could potentially help usbuild AI systems benefiting from humans' abilities in terms of flexibleout-of-distribution and systematic generalization, which is currently an areawhere a large gap exists between state-of-the-art machine learning and humanintelligence.

Inductive Biases for Deep Learning of Higher-Level Cognition