The ultimate goal of AI is to build a machine that does cognitive functions that are more associated with humans, such as thinking, learning, problem-solving, and reasoning. However, despite the remarkable success of the unfolding AI wave empowered by deep learning, the current AI practices starkly contrast with human intelligence. Existing AI models mostly have static architectures and static functional capabilities, limiting their application to learning from only stationary batches of training data whose complexity does not reflect the richness and uncertainty in real-world cognitive tasks.

The fields of cognitive neuroscience and AI have a long and intertwined history. Many of the early pioneers straddled both fields. However, this strong interaction has become less common as both fields have grown enormously in complexity and disciplinary boundaries have solidified. With the advent of new experimental and analysis techniques and with the emergence of large international research initiatives, alongside recent advances in computer architectures, the resurgence of interest in cognitive neuroscience as a rich source for revolutionizing foundational AI studies has led to an exciting new synergy between the two fields. This workshop would provide a venue that largely facilitates collaboration of researchers from both communities.