From d2e6d03669201240ed0f7cb4fd9374c7a76f9ae3 Mon Sep 17 00:00:00 2001 From: mptouzel Date: Fri, 16 Oct 2026 08:50:03 -0400 Subject: [PATCH] STAMINA: add Tan Zhi Xuan talk (Nov 3) Co-Authored-By: Claude Opus 5.5 --- _stamina_talks/2026-11-03-tan.md | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 _stamina_talks/2026-11-03-tan.md diff --git a/_stamina_talks/2026-11-03-tan.md b/_stamina_talks/2026-11-03-tan.md new file mode 100644 index 00000000..edebb833 --- /dev/null +++ b/_stamina_talks/2026-11-03-tan.md @@ -0,0 +1,9 @@ +--- +date: 2026-11-03 +title: Scaling Rational Cooperative Intelligence for Pluralistic AI Futures +presenters: +- name: Tan Zhi Xuan +affiliation: National University of Singapore +bio: Tan Zhi Xuan is an Assistant Professor in the Department of Computer Science of the National University of Singapore. Xuan's research focuses on scaling cooperative intelligence via the rational analysis and design of intelligent systems. Together with her research group, the Cooperative Systems & Intelligence (CoSI) lab, Xuan aims to enable the development of AI systems that can cooperate as proficiently as humans, and to design institutions and incentives that promote mutual benefit and flourishing in an increasingly automated future. +--- +One plausible extrapolation of current AI trends is that we are headed towards pluralistic AI futures: scenarios where a plethora of AI agents, systems and services are deployed towards various ends by various actors. How can we influence this process so that it occurs not just safely — avoiding AI-powered conflict and anti-human equilibria — but also in the direction of mutual flourishing? In order to address this challenge, I argue that we need to scale cooperative intelligence, advancing the cooperative capacities and dispositions of the agents we create, while creating institutions and incentives that promote AI and human cooperation at scale. To develop these capacities, I will introduce recent research on rational cooperative AI — a paradigm for building coherent AI agents that are cooperative-by-design. Across domains like assistive planning and natural language contracting, rational cooperative AI avoids the jaggedness and unreliability of current generative AI. Taking a rational approach also allows us to build "self-governing" AI agents that learn to follow and enforce societal norms. I will close by discussing the deeper connections between reasoning, rationality, and cooperation, and how a cooperative theory of reasoning could ultimately lead us to more human-aligned reasoners.