An important scientific goal of SCIoI is to unite the disciplines of intelligence research. This, of course, is a substantial challenge. To help us address it, we have assembled a scientific advisory board. As you can see below, the board consists of pioneers in the study of intelligence. The members, all external to SCIoI, are highly accomplished scientists and also possess extensive interdisciplinary research experience. Each of them comes from a different area of intelligence research, covering both the study of natural intelligence and the study of artificial intelligence. Each board member contributes extensive experience in building bridges between disciplines. Together with these inspiring persons, we know we will be able to achieve our goal of building a unified Science of Intelligence.
Meet our Scientific Advisory Board:
For decades, Patricia Churchland has contributed to the fields of philosophy of neuroscience, philosophy of the mind and neuroethics. Her research has centered on the interface between neuroscience and philosophy with a current focus on the association of morality and the social brain. A Professor Emeritus of Philosophy at the University of California, San Diego and Adjunct Professor at the Salk Institute, Pat holds degrees from Oxford University, the University of Pittsburg and the University of British Columbia. She has been awarded the MacArthur Prize, The Rossi Prize for Neuroscience and the Prose Prize for Science. She has authored multiple pioneering books, her most recent being Touching a Nerve. She has served as President of the American Philosophical Association and the Society for Philosophy and Psychology. Pat lives in Solana Beach, California, with her husband Paul, a neurophilosopher, and their labradoodle Millie. They have two children, Anne and Mark, both neuroscientists. Read more about her work on her website.
Naomi Ehrich Leonard is a control theorist whose work involves analysis and design of feedback and interconnection in complex, dynamical systems. She uses mathematical models and methods to study mechanisms of collective motion and collective decision making for multi-agent systems in nature (analysis of animal and human groups) and in engineering (design of autonomous robotic teams and mobile sensor networks). She has applied her work to the collective dynamics of killifish, starlings, honeybees, zebras, and desert harvester ants, as well as to rule-based improvisational dance. She led a multidisciplinary ocean sensing project with a month-long deployment of an automated, adaptive network of underwater robotic vehicles in Monterey Bay, CA. Leonard is the Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering and associated faculty member of the Program in Applied and Computational Mathematics at Princeton University. She is a MacArthur Fellow, a Fellow of the American Academy of Arts and Sciences, IEEE, SIAM, ASME, and IFAC. Visit her website here.
Linda B. Smith is a Professor of Psychology and Cognitive Science at Indiana University. Smith earned her Ph.D. from the University of Pennsylvania. Smith is the author of more than 100 publications on cognitive and linguistic development in young children.
Her central theoretical question is the study of developmental process and mechanisms of change. Her work focuses on early changes in perception, language, and action and how those changes in these areas support each other particularly around the age (12 months to 24 months) that children break into language. Her research takes a systems approach, seeking to understand how multiple components interact over nested time scales and levels of analysis and how, in so doing, they yield an individual’s developmental path. Click here to visit her lab website
Joshua Tenenbaum is Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences, the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds and Machines (CBMM). He received his PhD from MIT in 1999, and taught at Stanford from 1999 to 2002. His long-term goal is to reverse-engineer intelligence in the human mind and brain, and use these insights to engineer more human-like machine intelligence. In cognitive science, he is best known for developing theories of cognition as probabilistic inference in structured generative models, and applications to concept learning, causal reasoning, language acquisition, visual perception, intuitive physics, and theory of mind. In AI, he and his group have developed widely used models for nonlinear dimensionality reduction, probabilistic programming, and Bayesian unsupervised learning and structure discovery. His current research focuses on the development of common sense in children and machines, the neural basis of common sense, and models of learning as Bayesian program synthesis. He and his students’ research papers have been recognized with many awards at conferences in Cognitive Science, Computer Vision, Neural Information Processing Systems, Reinforcement Learning and Decision Making, and Robotics. He is the recipient of the Distinguished Scientific Award for Early Career Contributions in Psychology from the American Psychological Association (2008), the Troland Research Award from the National Academy of Sciences (2011), the Howard Crosby Warren Medal from the Society of Experimental Psychologists (2016), the R&D Magazine Innovator of the Year award (2018), and a MacArthur Fellowship (2019). He is a fellow of the Cognitive Science Society, the Society for Experimental Psychologists, and a member of the American Academy of Arts and Sciences.
Click here for his website.