Building Machines That Learn like Humans
What is the essence of human intelligence — what makes any human child smarter than any artificial intelligence system that has ever been built? Recent advances in machine learning and computer vision are extremely impressive as engineering accomplishments, but are far from approaching learning and perception the way humans do. I will talk about this gap, highlighting the difference between a view of intelligence as pattern recognition, where the goal is to find invariant features for classification, and intelligence as causal modeling, where the goal is to build and reason with generative models of the world’s causal structure. I will talk about the ways cognitive scientists are beginning to reverse-engineer human scene understanding and concept learning using methods from probabilistic programs and program induction — often complemented by deep learning, nonparametric Bayes, and other more conventional machine learning approaches. I hope to convince you that a deeper conversation between these fields can benefit us all, laying the foundations for more human-like approaches to artificial intelligence as well as a better understanding of human minds and brains in computational terms.