Deep, fast and shallow learning in humans and machines

Learning – adaptive, intelligent change in response to experience – is a core property of human cognition, and the questions surrounding it have a long history of a shared pursuit between computer science, psychology, and neuroscience. However, these fields seemed to be moving apart in their approaches to learning – still borrowing from each other and with individual scientists sometimes jumping from one field to the other – but with big knowledge advances occurring in discipline-specific directions. Despite this, there is general agreement that the advances in all three fields place us at the tipping point for powerful and consequential new insights into mechanisms of (and algorithms for) learning. These advances have the potential to revolutionize teaching and education, to yield behavioral interventions that precisely target change in specific neural processes, and (through powerful new machine learning approaches) to impact all of science as well as everyday life and society as a whole. In this spirit we call for joining efforts across disciplines more than ever to pursue a unified theory of learning. This workshop will breakthrough disciplinary boundaries to bring together computer scientists, psychologists and neuroscientists to address modern issues regarding how learning is implemented in algorithms, in the brain and in human behavior. See Schedule for Workshop here.