All known intelligent systems are collectives. Individual organisms are collectives of cells, which develop, heal, sense, and act. Groups of human and non-human animals use a range of mechanisms to coordinate their behavior across space and time, from flocks and swarms to organizations, institutions, and cultural traditions. Deep learning—the dominant approach to artificial intelligence—gains its power from combining simple units into complex architectures; many contemporary architectures (e.g., GANs) combine multiple learners, and multi-agent settings are a critical research frontier for AI, especially settings that integrate human and artificial agents.
These examples imply that a scientific understanding of intelligence must fundamentally grapple with collective intelligence—in a much broader sense than typical usage of the term would suggest. A variety of mathematical and computational models have been developed to explain and design intelligent behavior in particular collectives. Several mathematical fields source the ideas used to build and understand these models, from dynamical systems, statistical mechanics, network science, and random matrix theory to information theory, optimization, Bayesian statistics, and game theory–even applied category theory, in recent years. It remains unclear, however, whether we have the right mathematical language to provide a unified, abstract account of collective intelligence–or whether such a language is even possible! This workshop will bring together leading experts who study and model collective intelligence, as well as those who seek to understand such models. Its goal is to explore the advantages and disadvantages of existing modeling frameworks; to find collective implementations of models of individual cognition; to expand the systems and settings understood as manifesting collective intelligence; and to grow the community of researchers who study the mathematical foundations of collective intelligence.
(University of California, Riverside (UC Riverside))
Pranab Das (Elon University)
Jessica Flack (Santa Fe Institute)
Jacob Foster (University of California, Los Angeles (UCLA))
Alison Gopnik (University of California, Berkeley (UC Berkeley))
Max Kleiman-Weiner (Common Sense Machines)
Lakshminarayanan Mahadevan (Harvard University)
Josh Tenenbaum (Massachusetts Institute of Technology)