Foundations of AI
Understanding How Animals Think to Improve AI
Timothy Verstynen, associate professor in the Psychology Department and Neuroscience Institute, and his collaborators are exploring how animals think to find new ways to improve artificial intelligence. Verstynen and colleagues at University of Pennsylvania and Johns Hopkins University are designing a new class of Turing Tests that assess both biological intelligence and AI. By understanding AI performance from the biological vantagepoint, the team hopes to set new milestones for researchers to work to improve this technology.
Learn More About Research at the Cognitive Axion (CoAx) Lab
Employing AI to Enhance Cultural Rights, Human Rights and Social
Justice
The Global Humanities and Inclusive Artificial Intelligence Initiative (GHIAI) is dedicated to reshaping the foundations of AI research and development. Faculty, students and staff from Dietrich College and University Libraries scrutinize the methods by which data is obtained and the ways AI products are built, programmed, deployed and surveilled with the goal of incorporating global humanities research and frameworks to enhance cultural rights, human rights and social justice. GHIAI is reshaping the next frontier of AI research and policy in the humanities, advancing responsible AI-driven solutions to critical societal challenges.
Learn more about GHIAI
Exploring How Human Thought Works
John R. Anderson, the Richard King Mellon University Professor of Psychology and Computer Science, has developed ACT-R, a unified theory of cognition in a platform that explores how human thought works. On the exterior, ACT-R looks like a programming language, but its constructs reflect assumptions about human cognition. This platform has been applied to model errors (e.g., errors of omission and errors of commission), as well as Strategy-Based Learning (SBL) approach and the Instance-Based Learning (IBL) approach [pdf], laying the groundwork to better understand dynamic decision making.
Learn more about ACT-R and its applications, including Cognitive Tutors
Graphing Causation to Find Solutions Faster
The Tetrad Automated Casual Discovery Platform is an open-source software platform for simulating, estimating and searching for graphical causal models of statistical data. Developed by a research team at Dietrich College, Tetrad consists of algorithms that take measured data and background knowledge as input, and then compute the set of underlying causal systems ⏤ in essence, inferring "what causes what.” The project can be used to clarify experiments that will get researchers to the final answer faster to bring resources, like new drugs and therapies, to society sooner.
Learn More About How to Use Tetrad
