A number of puzzles concerning how truth-ascriptions are grounded have recently been discovered by several theorists, following Fine (2010). Most previous commentators on these puzzles have taken them to shed light on the theory of ground. In this paper, I argue that they also shed light on the theory of truth. In particular, I argue that the notion of ground can be deployed to clearly articulate one strand of deflationary thinking about truth, according to which truth is “metaphysically lightweight.” I will propose a ground-theoretic explication of the (entirely bearable) lightness of truth, and then show how this broadly deflationary view yields a novel solution to the puzzles concerning how truth is grounded. So, if the proposal I sketch is on target, the theory of truth and the theory of ground interact fruitfully: we can apply the notion of ground to offer a clear explication of the deflationist claim that truth is “metaphysically lightweight” that both captures the motivations for that claim and solves the puzzles.
In public discourse, but also in political theory, the opinion prevails, that democracy is incompatible with aspirations of truth. Some assume, in the Hobbesian tradition, that civic peace requires that truth assertions be restricted to science and religion (normative positivism), whereas the political sphere is constituted by interests, bargaining and collective decisions based on interests, bargaining and rules of aggregation, be they implicit or explicit. In this perspective Collective Choice as preference aggregation is paradigmatic for the understanding of democracy. Postmodernist and neo-pragmatist thought dismisses truth, because it threatens solidarity and belonging. Libertarian political thought relies on market mechanisms reducing citizens to consumers and producers of material and immaterial goods like security and welfare. Accounts of deliberative democracy focus on reasoning in the public sphere but dismiss a realistic understanding of truth, because it is thought to threaten collective and individual self-determination.
In my talk I will argue that a realistic understanding of empirical and normative truth is compatible, even necessary, for an adequate understanding of democracy, that truth assertions do not threaten civic peace, that postmodernist relativity undermines democratic practice, that libertarian market-orientation is incompatible with the status of citizens in democracy and that even deliberative, but anti-realist, accounts of democracy do not allow for an adequate understanding of democracy. My argument is based on a Davidsonian, or pragmatist, understanding of truth, therefore one might say: it critizises normative positivism, postmodernism, libertarianism, and critical theory using pragmatist insights.
Julian Nida-Rümelin presently holds a chair for philosophy and political theory at Ludwig-Maximilians-Universität München, is a member of the European Academy of Sciences, was president of the German Philosophical Association (DGphil) and state-minister for culture and media in the first government of Gerhard Schröder. The topics of his books include Democracy as Cooperation (1999); Democracy and Truth (2006), translated in Chinese and Italian, Philosophy and the form of Life (2009), Realism (2018) and A Theory of Practical Reason (2020, forthcoming, de Gruyter and PUP).
Generous support provided by the New York Institute of Philosophy.
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
Yejin Choi is Wissner-Slivka Professor and a MacArthur Fellow at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. She is also a senior director at AI2 overseeing the project Mosaic and a Distinguished Research Fellow at the Institute for Ethics in AI at the University of Oxford. Her research investigates if (and how) AI systems can learn commonsense knowledge and reasoning, if machines can (and should) learn moral reasoning, and various other problems in NLP, AI, and Vision including neuro-symbolic integration, language grounding with vision and interactions, and AI for social good. She is a co-recipient of 2 Test of Time Awards (at ACL 2021 and ICCV 2021), 7 Best/Outstanding Paper Awards (at ACL 2023, NAACL 2022, ICML 2022, NeurIPS 2021, AAAI 2019, and ICCV 2013), the Borg Early Career Award (BECA) in 2018, the inaugural Alexa Prize Challenge in 2017, and IEEE AI’s 10 to Watch in 2016.
Join us for a special live taping of the Clearer Thinking podcast. Host Spencer Greenberg and guest Jeff Sebo will discuss the moral status of insects and AI systems, as well as other thorny questions in global priorities research.
About the speakers
Jeff Sebo is Associate Professor of Environmental Studies, Affiliated Professor of Bioethics, Medical Ethics, Philosophy, and Law, Director of the Animal Studies M.A. Program, Director of the Mind, Ethics, and Policy Program, and Co-Director of the Wild Animal Welfare Program at New York University. He is the author of Saving Animals, Saving Ourselves (2022) and co-author of Chimpanzee Rights (2018) and Food, Animals, and the Environment (2018). He is also an executive committee member at the NYU Center for Environmental and Animal Protection, a board member at Minding Animals International, an advisory board member at the Insect Welfare Research Society, a senior research fellow at the Legal Priorities Project, and a mentor at Sentient Media.
Spencer Greenberg is an entrepreneur and mathematician with a focus on improving human well-being. He’s the founder of ClearerThinking.org, which provides 70 free, digital tools to help people make better decisions and improve their lives, as well as the host of the Clearer Thinking podcast. Spencer is also the founder of Spark Wave, an organization that conducts psychology research and builds psychology-related products designed to help benefit the world. He has a Ph.D. in applied math from New York University, with a specialty in machine learning, and his work has been featured by numerous major media outlets, including The Wall Street Journal, the Independent, the New York Times, Gizmodo, and more.
Thank you to Effective Altruism New York City for their generous support of this event.