Out-of-distribution generalization and adaptation in natural and artificial intelligence

Joshua T Vogelstein · Weiwei Yang · Soledad Villar · Zenna Tavares · Johnathan Flowers · Onyema Osuagwu · Weishung Liu

Abstract Workshop Website
Tue 14 Dec, 6 a.m. PST


Out-of-distribution (OOD) generalization and adaptation is a key challenge the field of machine learning (ML) must overcome to achieve its eventual aims associated with artificial intelligence (AI). Humans, and possibly non-human animals, exhibit OOD capabilities far beyond modern ML solutions. It is natural, therefore, to wonder (i) what properties of natural intelligence enable OOD learning (for example, is a cortex required, can human organoids achieve OOD capabilities, etc.), and (ii) what research programs can most effectively identify and extract those properties to inform future ML solutions? Although many workshops have focused on aspects of (i), it is through the additional focus of (ii) that this workshop will best foster collaborations and research to advance the capabilities of ML.

This workshop is designed to bring together the foremost leaders in natural and artificial intelligence, along with the known and unknown upcoming stars in the fields, to answer the above two questions. Our hope is that at the end of the workshop, we will have a head start on identifying a vision that will (1) formalize hypothetical learning mechanisms that enable OOD generalization and adaptation, and characterize their capabilities and limitations; (2) propose experiments to measure, manipulate, and model biological systems to inspire, test, and validate such hypotheses; and (3) implement those hypotheses in hardware/software/wetware solutions to close the empirical gap between natural and artificial intelligence capabilities.

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