A primary goal of research in AI is to develop more reliable, general, and adaptable systems, ones that are trustworthy and that can more successfully collaborate with humans. However, an obstacle to making our machines more intelligent is the fact that intelligence itself is an ill-understood concept, and science is far from a principled understanding of the properties of intelligence and the mechanisms that generate intelligence in natural systems. We believe that new breakthroughs in AI research are most likely to come from a concentrated collaborative effort between AI specialists and researchers who think deeply about the nature of intelligence in different disciplines.
Our Foundations of Intelligence project explores five research themes central to the study of intelligence:
1. Taxonomy of Intelligence across Disciplines
2. Development and Life History of Intelligent Systems
3. Concept Formation, Abstraction, and Analogy
4. Collective Intelligence
5. Evolutionary and Co-Evolutionary Intelligence
We are exploring these themes via workshops that bring together researchers from diverse disciplines to map out questions and methods as well as to engage in research driven by these questions.
This material is based upon work supported by the National Science Foundation under Award No. (#2020103)
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Project PI Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute. Her research focuses on conceptual abstraction, analogy-making, and visual recognition in AI systems.
Project Co-PI Melanie Moses is a Professor in the UNM Department of Computer Science. Her research focuses on developing models of biological systems and applying biologically-inspired computation (esp. in swarm robotics).
Tyler Millhouse is a postdoctoral fellow at the Santa Fe Institute. His research focuses on understanding what models in machine learning and artificial intelligence can tell us about models in science and cognition.
Millhouse, T., Moses, M., & Mitchell, M. (2022). "Embodied, Situated, and Grounded Intelligence: Implications for AI." https://arxiv.org/abs/2210.13589.
Millhouse, T., Moses, M., & Mitchell, M. (2021). "Frontiers in Collective Intelligence: A Workshop Report." https://arxiv.org/abs/2112.06864.
Millhouse, T., Moses, M., & Mitchell, M. (2021). "Frontiers in Evolutionary Computation: A Workshop Report."https://arxiv.org/abs/2110.10320.
Millhouse, T., Moses, M., & Mitchell, M. (2021). "Foundations of Intelligence in Natural and Artificial Systems: A Workshop Report." https://arxiv.org/abs/2105.02198.
Contact Us
To get in touch, please email project postdoc Tyler Millhouse.
