Please note: All events are virtual until otherwise stated.
Location TBA
This talk explores the reflexive nature of consciousness, which consists primarily in the fact that a state of consciousness has a reflexive relation to the subject who has that state, so that the subject can typically be aware of itself as having that state. Comparing Kant’s, Fichte’s, and selected contemporary analytic theories of this reflexivity shows that there is a crucial difference in the way the relation between form (or mode) and content of a state of consciousness is conceived. The first part examines Kant’s formal theory of consciousness: reflexivity is understood not in terms of a self-referential content resulting from a reflection on the state of the subject, but as the universal transcendental form that any content must have in order to be representationally significant and potentially conscious to the subject. The second part examines Fichte’s departure from Kant in his theory of a self-positing consciousness: in the original act of self-positing, the mere form of reflexivity is turned into a self-referential content that determines the subject as an object from the absolute standpoint of consciousness. The third part examines analytic theories that explain the reflexivity (or what is often called the subjective character) of consciousness on a model of mental indexicality. These theories tend to reduce reflexivity to an objective constituent of content that, although often implicit, can be read off from the subject’s contextual situatedness in nature. In conclusion, Kant’s theory can be understood as a moderate, human-centered kind of perspectivism that navigates between Fichtean absolute subjectivity and a naturalist absolute objectivity.
Registration is free but required. A registration link will be shared via email with our department mailing lists a few weeks before the event. Please contact Jack Mikuszewski at jhm378@nyu.edu if you did not receive a registration link.
The Philosophy Department provides reasonable accommodations to people with disabilities. Requests for accommodations should be submitted to philosophy@nyu.edu at least two weeks before the event.
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.
About
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).
Conference speakers
- 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 phildeeplearning@gmail.com by January 22nd, 2023 (11.59pm EST).
https://philevents.org/event/show/106406