Social learning is powerful: agents that can learn from each other typically outperform similar agents who must go it alone. Across the animal kingdom, social learning takes many forms, ranging from emulation to gaze following to deliberate signalling. Human social cognition is particularly remarkable: unlike other animals, we query and correct each other, and work together to build a shared understanding of reality. But how exactly are we able to manage this, and why is it so helpful to distribute problem-solving among multiple agents? New research in artificial intelligence is generating insight into these questions, by developing algorithms which attempt to endow AI agents with social learning abilities, and studying what incentives can improve social abilities like cooperation in AI. This session looks at how social learning can help us build better AI and what insights we can gain from those AI systems about one of the most remarkable features of natural intelligence.
Natural and artificial social learning | Absolutely Interdisciplinary 2022
Moderator: Sheila McIlraith
Speakers: Natasha Jaques, Jennifer Nagel
Sheila McIlraith is a professor in the Department of Computer Science at the University of Toronto, CIFAR AI Chair, a faculty member at the Vector Institute for Artificial Intelligence, and an associate director and research lead at the Schwartz Reisman Institute for Technology and Society. McIlraith’s work focuses on AI sequential decision making, broadly construed through the lens of human-compatible AI. She is the author of over 100 scholarly publications in the area of knowledge representation, automated reasoning, and machine learning, and her research has made practical contributions to the development of next-generation NASA space systems and emerging web standards.
Jennifer Nagel is a professor in the Department of Philosophy at the University of Toronto, and a faculty affiliate at the Schwartz Reisman Institute for Technology and Society. Her research specializes in epistemology and philosophy of mind, with her most recent work focusing on intuitive impressions of knowledge and belief, on the guidance that these impressions provide in the ordinary course of conversation and social interaction, and on what these impressions can tell us about knowledge itself. Nagel is the author of Knowledge: A Very Short Introduction (2014), and president of the Canadian Philosophical Association.
Natasha Jaques is a senior research scientist at Google Brain, where her research focuses on social reinforcement learning in multi-agent and human-AI interactions. Jaques completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing, and postdoc at UC Berkeley. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, Quartz, IEEE Spectrum, MIT Technology Review, Boston Magazine, and on CBC radio.
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The Schwartz Reisman Institute for Technology and Society is a research institute at the University of Toronto that explores the ethical and societal implications of technology. Our mission is to deepen knowledge of technologies, societies, and humanity by integrating research across traditional boundaries to build human-centred solutions. Our research community seeks to rethink technology’s role in society, the needs of human communities, and the systems that govern them. We are investigating how best to align technology with human values and deploy it accordingly. Across all our activities, SRI convenes world-class expertise and diverse perspectives from universities, government, industry, and beyond to develop new modes of thinking about powerful technologies and their role in what it means to be human in the 21st century. We are defining what’s possible, determining what’s at stake, and devising implementable solutions to make sure technologies like AI are effective, safe, fair, and beneficial—for everyone.
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