The Philosophy of Deep Learning – NYU Center for Mind, Brain, and Consciousness (original) (raw)

The Center is co-sponsoring a two-day conference on the philosophy of deep learning, organized by Ned Block (NYU), David Chalmers (NYU) and Raphaël Millière (Columbia), co-sponsored by the Presidential Scholars in Society and Neuroscience program at Columbia University. For more information visit: https://phildeeplearning.github.io/

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 language models need sensory grounding for meaning and understanding?” Speakers include Jacob Browning (New York University), David Chalmers (New York University), Brenden Lake (New York University), Yann LeCun (New York University/Meta AI), Gary Lupyan (University of Wisconsin–Madison), and Ellie Pavlick (Brown University/Google AI).

Recordings

All talks in this workshop can be viewed online via YouTube.

Conference speakers

Lectures

• Cameron Buckner (University of Houston) (video)
• Rosa Cao (Stanford University) (video)
• Grace Lindsay (New York University) (video)
• Raphaël Millière (Columbia University) (video)
• Nicholas Shea (Institute of Philosophy, University of London) (video)

Panel on Deep Learning and Cognitive Science (watch video here)

• Ishita Dasgupta (DeepMind)
• Nikolaus Kriegeskorte (Columbia University)
• Tal Linzen (New York University / Google AI)
• Robert Long (Center for AI Safety)
• Ida Momennejad (Microsoft Research)

Symposium on Representation in Deep Learning Systems (watch video here)

• Fintan Mallory (University of Oslo)
• Jacqueline Harding (Stanford University)
• Anders Søgaard (University of Copenhagen)
• Tony Chen (MIT)

Symposium on Capacities of Large Language Models (watch video here)

• Anna Ivanova (MIT)
• Nuhu Osman Attah (University of Pittsburgh)
• Patrick Butlin (University of Oxford)
• Philippe Verreault-Julien (Eindhoven University of Technology)

Poster presentations

• Atoosa Kasirzadeh (University of Edinburgh)
• Wai Keen Vong (New York University)
• Sreejan Kumar (Princeton University)
• Will Merrill (New York University)
• Julia Minarik (University of Toronto)
• Jared Moore (University of Washington)
• Emin Orhan (New York University)
• Stephan Pohl (New York University)
• Hokyung Sung (MIT)
• Justin Tiehen (Puget Sound)

Program

Friday, March 24th (Cantor Film Center, Room 200)

• 5:30-7:30pm • Debate: Do Language Models Need Sensory Grounding for Meaning and Understanding?
  • Speakers: Jacob Browning (NYU), David Chalmers (NYU), Brenden Lake (NYU), Yann LeCun (NYU / Meta AI), Gary Lupyan (Wisconsin), Ellie Pavlick (Brown / Google AI)

Saturday, March 25th (19 West 4th Street, Room 101)

• 9:00-9:30am • Coffee/Registration
• 9:30-10:40am • Lecture: Cameron Buckner (Houston) – “Moderate Empiricism and Machine Learning”
• 10:40-11:00am • Coffee Break
• 11:00am-12:10pm • Lecture: Rosa Cao (Stanford) – “Are (apparently) successful DNN models also genuinely explanatory?”
• 12:10-1:20pm • Lunch Break
• 1:20-3:00pm • Symposium: Representation in Deep Learning Systems
  • Speakers • Tony Chen (MIT), Jacqueline Harding (Stanford), Fintan Mallory (Oslo), Anders Søgaard (Copenhagen).
• 3:00-4:15pm • Poster Session
  • Presenters: Atoosa Kasirzadeh (Edinburgh), Wai Keen Vong (NYU), Sreejan Kumar (Princeton), Will Merrill (NYU), Julia Minarik (Toronto), Jared Moore (Washington), Emin Orhan (NYU), Stephan Pohl (NYU), Hokyung Sung (MIT), Justin Tiehen (Puget Sound)
• 4:15-6:15pm • Panel: What Can Deep Learning Do for Cognitive Science and Vice Versa?
  • Speakers: Ishita Dasgupta (DeepMind), Niko Kriegeskorte (Columbia), Tal Linzen (NYU / Google AI), Robert Long (Center for AI Safety), Ida Momennejad (Microsoft Research)

Sunday, March 26th (19 West 4th Street, Room 101)

• 9:30-10:00am • Coffee
• 10:00am-11:10pm • Lecture: Nick Shea (London) – “The Importance of Logical Reasoning and Its Emergence in Deep Neural Networks”
• 11:10-11:30am • Coffee Break
• 11:30am-12:40pm • Lecture: Raphaël Millière (Columbia) – “Compositionality in Deep Neural Networks”
• 12:40-2:10pm • Lunch Break
• 2:10-3:20pm • Lecture: Grace Lindsay (NYU) – “Developing Neural Systems Understanding”
• 3:20-4:00pm • Coffee Break
• 4:00-5:40 pm • Symposium: Linguistic and Cognitive Capacities of Large Language Models
  • Speakers • Nuhu Osman Attah (Pittsburgh), Patrick Butlin (Oxford), Philippe Verreault-Julien (Eindhoven), Anna Ivanova (MIT)