A two-day conference on the philosophy of deep learning, organized by Ned Block (New York University), David Chalmers (New York University) and Raphaël Millière (Columbia University), and jointly sponsored by the Presidential Scholars in Society and Neuroscience program at Columbia University and the Center for Mind, Brain, and Consciousness at New York University.
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 ?”. Speakers include Jacob Browning (New York University), David Chalmers (New York University), Yann LeCun (New York University), and Ellie Pavlick (Brown University / Google AI).
- Cameron Buckner (University of Houston)
- Rosa Cao (Stanford University)
- Ishita Dasgupta (DeepMind)
- Nikolaus Kriegeskorte (Columbia University)
- Brenden Lake (New York University / Meta AI)
- Grace Lindsay (New York University)
- Tal Linzen (New York University / Google AI)
- Raphaël Millière (Columbia University)
- Nicholas Shea (Institute of Philosophy, University of London)
Call for abstracts
We invite abstract submissions for a few short talks and poster presentations related to the topic of the conference. Submissions from graduate students and early career researchers are particularly encouraged. Please send a title and abstract (500-750 words) to firstname.lastname@example.org by January 22nd, 2023 (11.59pm EST).
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