Sponsored by Evolution AI: https://www.evolution.ai
Abstract: How can advances in machine learning best advance our understanding of human learning and development? In this talk, I'll describe two case studies using deep neural networks to address classic debates in cognitive science:
What ingredients do children need to learn early vocabulary words? How much is learnable from sensory input with relatively general neural networks, and how much requires stronger inductive biases (e.g., innate knowledge, domain-specific constraints, social reasoning)? Using head-mounted video recordings from a single child (61 hours of video slices over 19 months), we show how deep neural networks can acquire many word-referent mappings, generalize to novel visual referents, and achieve multi-modal alignment. These results show how critical aspects of word meaning are learnable without strong inductive biases.
Can neural networks capture human-like systematic generalization? We address a 35-year-old debate catalyzed by Fodor and Pylyshyn's classic article, which argued that standard neural networks are not viable cognitive models because they lack systematic compositionality -- the algebraic ability to understand and produce novel combinations from known components. We'll show how neural network can achieve human-like systematic generalization when trained through meta-learning for compositionality (MLC), a new method for optimizing the compositional skills of neural networks through practice. With MLC, a neural network can match human performance and inductive biases in a head-to-head comparison of artificial language learning.
These findings emphasize the power of neural network learners, even when trained on child-scale data, and their increasing capability for addressing longstanding issues in cognitive science.
Speaker: Brenden M. Lake is an Assistant Professor of Psychology and Data Science at New York University. He received his M.S. and B.S. in Symbolic Systems from Stanford University in 2009, and his Ph.D. in Cognitive Science from MIT in 2014. He was a postdoctoral Data Science Fellow at NYU from 2014-2017. Brenden is a recipient of the Robert J. Glushko Prize for Outstanding Doctoral Dissertation in Cognitive Science, he is a MIT Technology Review Innovator Under 35, and his research was selected by Scientific American as one of the 10 most important advances of 2016. Brenden's research focuses on computational problems that are easier for people than they are for machines, such as learning new concepts, creating new concepts, learning-to-learn, and asking questions.