New York University (Room 101), 19 West 4th Street, New York
The conference will explore current issues in AI research from a philosophical perspective, with particular attention to recent work on deep artificial neural networks. The goal is to bring together philosophers and scientists who are thinking about these systems in order to gain a better understanding of their capacities, their limitations, and their relationship to human cognition.
The conference will focus especially on topics in the philosophy of cognitive science (rather than on topics in AI ethics and safety). It will explore questions such as:
What cognitive capacities, if any, do current deep learning systems possess?
What cognitive capacities might future deep learning systems possess?
What kind of representations can we ascribe to artificial neural networks?
Could a large language model genuinely understand language?
What do deep learning systems tell us about human cognition, and vice versa?
How can we develop a theoretical understanding of deep learning systems?
How do deep learning systems bear on philosophical debates such as rationalism vs empiricism and classical vs. nonclassical views of cognition.
What are the key obstacles on the path from current deep learning systems to human-level cognition?
A pre-conference debate on Friday, March 24th will tackle the question “Do large language models need sensory grounding for meaning and understanding?”.
Cameron Buckner, Associate Professor of Philosophy at the University of Houston
Rosa Cao, Assistant Professor of Philosophy at Stanford University