One of the central questions facing human beings is how we should respond to the humanity of others. Since the enlightenment, secular Western ethics has gravitated towards two kinds of answer: we should care for others’ well-being, or we should respect them as autonomous agents. Largely neglected is an answer we can find the religious traditions of Judaism, Christianity and Buddhism: we should love all. Analytic philosophers have started to pay more attention to love. But unlike those working within religious traditions, for whom an ideal of love for all serves as the central, organizing ideal in ethics, most of these philosophers see love as confined to the domain of intimate relationships between friends, family, romantic partners and the like. This paper argues that an ideal of love for all, of agape, can be understood apart from its more typical religious contexts and moreover provides a unified and illuminating account of the the nature and grounds of morality. Against challenges to the idea that love for all is possible, I offer a novel account of what it would be to love all. I go on to argue that while it is possible to love all, most of us should not, as doing so would rule out the possibility of loving particular friends and families. Instead, we should approximate love for all. I argue that the minimal approximation of love for all is, surprisingly, respect, deriving the basic, structural features of deontological ethics (including anti-welfarism and anti-aggregation) from my account of love for all.
Reception to follow.
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
riday, November 10
9:30–9:55 Check–in and Coffee
9:55 Welcome
10:00–12:00 Adam Smith
Speaker: Ryan Patrick Hanley (Boston College)
Commentator: Samuel Fleischacker (University of Illinois Chicago)
12:00–2:00 Lunch Break
2:00–4:00 Immanuel Kant
Speaker: Marcia Baron (Indiana University Bloomington)
Commentator: Kyla Ebels–Duggan (Northwestern University)
4:00–4:30 Coffee Break
4:30–6:30 German Romanticism
Speaker: Frederick Beiser (Syracuse University)
Commentator: Owen Ware (University of Toronto)
6:30–7:30 Reception
Saturday, November 11
9:30–10:00 Check–in and Coffee
10:00–12:00 Friedrich Nietzsche
Speaker: Andrew Huddleston (University of Warwick)
Commentator: Claire Kirwin (Northwestern University)
12:00–2:00 Lunch Break
2:00–4:00 Simone De Beauvoir
Speaker: Michelle Kosch (Cornell University)
Commentator: Susan J. Brison (Dartmouth University)
4:00–4:30 Coffee Break
4:30–6:30 Contemporary
Speaker: Simon May (King’s College London)
Commentator: Alecxander Nehamas (Princeton University)
6:30–7:30 Reception
The Arendt Circle meets annually to share and discuss research related to any aspect of Hannah Arendt’s work. This year’s conference will be held in person at New York University (NYU) from April 4th – April 6th with presentations in English.
Deadline for Submission: Friday, December 1st, 2023
Please submit a 750-word abstract prepared for anonymous review as a Word Document or PDF to: thehannaharendtcircle@gmail.com
– 2024 Organizing Committee
Magnus Ferguson (University of Chicago)
Valentina Moro (DePaul University and University of Verona)
Tal Correm (New York University)
Sanjana Rajagopal (Fordham University)
The official Call for Abstracts is posted here.
https://philevents.org/event/show/114362