Josh Tenenbaum, MIT
Past work on human intelligence has framed the underlying processes as pattern recognition engines, as manifested in deep convolutional neural networks; prediction engines, as captured in Bayesian networks, causal models, and predictive coding; or symbol manipulation engines grounded in logic, the lambda calculus, and high-level symbolic programming languages. Systems that can reason broadly about the physical and social world must embody models of the world that enable explanation and understanding of what we sense, prediction of future states, problem solving and action planning, and learning of new models with experience. Such systems must at least embody an intuitive physics and psychology that governs the behavior of objects and agents in the world, which may be created through an approach that starts with the intelligence of a baby and learns like a child.

Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. (2017) Building machines that learn and think like people. Behavioral and Brain Sciences 40:e253.