A central problem in artificial intelligence today is that machine learning algorithms often require supervised training with huge amounts of hand-curated data. As a result, such algorithms are largely limited in scope to domains where well-funded organizations can build massive, expertly-annotated, and typically proprietary, labelled datasets. In contrast, real biological systems such as human infants learn much more efficiently, combining a small amount of explicit supervision with powerful -- but not fully understood -- mechanisms of self-supervision. This proposal seeks to build biologically-inspired general-purpose self-supervised systems that can learn without needing to be spoon-fed millions of labeled examples.
The basic strategy to achieve this goal will be to develop and refine techniques in the emerging field of unsupervised deep learning, in which neural networks train themselves to capture the subtle statistical patterns present in their sensory surroundings. These networks will be augmented to operate as agents in a rich interactive physical domain, where they will seek out challenging but ultimately solvable self-supervised "goals" that will teach them to flexibly represent and respond to their environment. If successful, such systems will have the ability to use the wealth of unlabeled data that is ubiquitously available in the physical world. The proposal also seeks to use these algorithmic ideas as hypotheses for quantitative models of learning in real biological systems. Using recently developed techniques from computational neuroscience, the neural networks will be compared to neural and behavioral data collected using a wide spectrum of experimental paradigms. It will then be determined which self-supervised neural network learning models best capture the empirical data -- and equally importantly, where the most glaring mismatches between experiment and computational models lie. Quantifying these model-data comparisons will in turn allow for feedback to build better neural network algorithms. The ultimate goal of this work is to set up a tight loop between experimental observation and computational algorithm development, accelerating progress both in artificial intelligence and neuroscience.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.