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Invited Talks

6 Events
Invited Talk Jul 7, 8:30 AM - 9:30 AM HALL C

Towards AI Agents In the Real World

About the speaker Pascale FUNG

Pascale FUNG
Pascale Fung’s long term research background is in multimodal interactive systems including audio, speech, text and video. She started research on world modeling after studying the limitation of generative models due to hallucinations. She is the Co-founder and Chief Research & Innovation Officer at AMI Labs. She was previously the Senior Director of AI Research at Meta-FAIR, leading research on embodied AI agents. She is also a Chair Professor of ECE at The Hong Kong University of Science & Technology (HKUST). She is a Fellow of the ACL, AAAI, IEEE, and ISCA for her significant contribution to human-machine interactions.

Abstract

Recent advances in AI agents have been driven by imitation learning with reinforcement learning in the digital world, based on large scale generative models, yielding strong performance in many online tasks but limited capability in physical world settings. I argue for a shift toward AI agents grounded in world modeling, allowing them to understand the physical environment, to understand user intentions and social contexts, thereby enhancing their ability to perform complex tasks autonomously in the real world. World modeling encompasses the integration of multimodal perception, planning through reasoning for action and control, and memory to create a comprehensive understanding of the physical world. I argue that achieving advanced machine intelligence requires modeling both the physical world and the mental world, including latent variables such as intent, attention, and context. I outline key challenges toward building context-aware, interactive agents in the real world. This essential trajectory demands continued efforts to develop robust world models and embodied agents that can truly assist humans with real tasks in the real world.
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Invited Talk Jul 7, 4:00 PM - 5:00 PM HALL C

Causal Inference with Transformer Models

About the speaker Susan Athey

Susan Athey
Professor Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University. Her current research focuses on the economics of digitization and the intersection of causal inference and artificial intelligence. She has worked on several application areas, including timber auctions, internet search, online advertising, the news media, labor market transitions, health, and digital technology for social impact. As one of the first “tech economists,” she served as consulting chief economist for Microsoft Corporation for six years, and has served on the boards of multiple private and public technology firms. She also served as a long-term advisor to the British Columbia Ministry of Forests, helping architect and implement their auction-based pricing system. She was a founding associate director of the Stanford Institute for Human-Centered Artificial Intelligence, where she currently serves as senior fellow, and she is the founding director of the Golub Capital Social Impact Lab at Stanford GSB. From 2022 to 2024, she took leave from Stanford to serve as Chief Economist at the U.S. Department of Justice Antitrust Division. Professor Athey was the 2023 President of the American Economics Association, where she previously served as vice president and elected member of the Executive Committee.

Abstract

How do we answer causal questions about sequence data such as text, career job sequences, or customer journeys? This talk will consider methods for estimating average treatment effects, conditional average treatment effects, and decompositions of differences across groups in average outcomes. It will consider both experimental and observational data.
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Invited Talk Jul 8, 8:30 AM - 9:30 AM HALL C

How Far Can Quadratics Take Us? Lessons for LLM Pretraining

About the speaker Sham Kakade

Sham Kakade
I work on advancing the fundamental capabilities needed to develop artificial general intelligence and create systems that can effectively interact with and add value to the real world. My current interests include: (i) developing full-stack training pipelines for foundation models, with particular focus on distributed systems architecture, scalable optimization algorithms, and principled approaches to data curation and composition. (ii) investigating the mathematical and scientific principles that govern large-scale learning systems, with emphasis on understanding emergent capabilities, scaling laws, and fundamental limits of neural architectures. (iii) advancing autonomous agent architectures that can reason, plan, and learn from interaction, with focus on bridging the gap between language models and embodied intelligence in complex environments.

Abstract

Modern large language model pretraining is governed by complex heuristics — from cosine learning-rate decay to batch-size scheduling. Yet, a growing body of work suggests that an analytically simple quadratic model can accurately predict much of this large-scale optimization behavior. In this talk, I will argue that the quadratic model is not merely a convenient theoretical toy, but a useful lens for pretraining practice — both for compute efficiency and for serial runtime. We will begin with exact computations of critical batch size and time-dependent learning rates in linear systems, establishing a principled foundation. From there, we will see how the same analysis yields batch-size scaling laws (where we estimate batch-size exponents in LLMs) and motivates two pretraining improvements: SeeSaw, a scheduler that trades learning-rate decay for batch-size growth and matches loss at lower serial runtime; and Horizon-Free Pretraining, which shows how anytime schedules with weight averaging can match carefully tuned cosine decay without committing to a horizon in advance. We will close with lower bounds on the interaction between momentum and batch size, which suggest the quadratic model captures fundamental limits about what any first-order method can achieve. Taken together, these results make the case that quadratics deserve a more central place in how we think about pretraining.
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Invited Talk Jul 8, 1:30 PM - 2:30 PM HALL C

Lab-in-the-Loop for Drug R&D with AI

About the speaker Aviv Regev

Aviv Regev
Aviv Regev is a computational biologist and systems biologist and Executive Vice President and Head of Genentech Research and Early Development in Genentech/Roche. She is a core member at the Broad Institute of MIT and Harvard and professor at the Department of Biology of the Massachusetts Institute of Technology. In 2020, Regev became the Head and Executive Vice President of Genentech Research and Early Development, based in South San Francisco, and a member of the extended Corporate Executive Committee of Roche. Previously, she was a Core Institute Member (now on leave), Chair of the Faculty, Founding Director of the Klarman Cell Observatory and co-Director Cell Circuits Program at the Broad Institute of MIT and Harvard. She was also a professor in the Department of Biology at the Massachusetts Institute of Technology (now on leave), as well as an Investigator at the Howard Hughes Medical Institute. Regev's research includes work on gene expression (with Eran Segal and David Botstein), and the use of π-calculus to represent biochemical processes. Regev’s team has been a leading pioneer of single-cell genomics experimental and computational methods.

Abstract

Making effective medicines is challenging: more than 90 percent of drug candidates fail in pre-clinical research or clinical trials. A major contributor to this low success rate is the enormous space of biological and therapeutic possibilities. In the underlying biology of disease, there are thousands of different cell types and states, about 20,000 genes in our genome, more than 105 disease associated loci, and perhaps 1013 or more ways in which they could meaningfully combine. To make medicines targeting this biology, one could consider at least 1060 possible small molecules with medicine-like properties, approximately 2032 relevant antibodies to consider, billions of people, and about 10,000 different diseases. Now, however, we are at a major inflection point: we can collect large-scale data, at high-resolution, from human biology, and crucially, combine these large datasets with AI to be able to represent, reason and generate over these enormous spaces to yield testable predictions of missing or nonexistent information and iteratively improve our models. Although it is not possible to test every possibility in a lab, clinical trial, or even an entire population, with the scale of data it is currently possible to generate, we can use AI to bridge different layers of biology, determine the impact of combinations of genetic mutations or drug perturbations, predict disease progression, and generate therapeutic molecules de novo or through optimization. Key to the success of this approach is an integrated interplay between data and AI, or a “Lab in the Loop,” where experimental or clinical data are used to train models, the models are used to help predict and design the next set of experiments, and the process is iterated, at scale, both to yield key predictions in any specific project and improve the model for all projects. In this talk, I will describe how we built such a Lab in the Loop of experiments and AI in Genentech across our target discovery, drug discovery and drug development efforts to serve patients across therapeutic areas.
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Invited Talk Jul 9, 8:30 AM - 9:30 AM HALL C

Normative alignment: A new paradigm for principled autonomous agents

About the speaker Verena Rieser

Verena Rieser
Verena Rieser is a Research Lead at Google DeepMind, where she leads efforts in responsible alignment for Gemini. She was previously a full Professor of Artificial Intelligence at Heriot-Watt University and Co-founder of a conversational AI startup. She earned her PhD from Saarland University in 2008, pioneering the use of Reinforcement Learning in dialogue systems. Her contributions to Generative and Conversational AI have been recognized with numerous international awards, including a Royal Society Leverhulme Senior Research Fellowship. Following her ACL 2025 Keynote on reimagining alignment for truly beneficial AI, her ICML 2026 address expands this vision into a human-centred research roadmap, navigating the shift from instruction-following assistants to principled, autonomous agents.

Abstract

How can agents make safe autonomous decisions in complex dynamic environments? While significant progress has been made in establishing safety guardrails to enforce compliance in generative models, these negative constraints often prove brittle in open-world environments. I argue that achieving generalisable agentic safety requires Normative Alignment: a new paradigm bridging positive alignment goals with context-dependent values to actively support human flourishing. Realising this paradigm presents a triple challenge of capability, measurement, and governance. First, it requires a shift in capability toward normative competence beyond generic reward maximisation. Anchoring agents in “thick” value concepts (such as duty of care or human autonomy) provides the contextual reasoning needed to adjudicate complex trade-offs in non-verifiable domains. Second, it demands new metrics that move optimisation targets beyond immediate preference satisfaction toward long-term human well-being. Third, it requires democratic governance. To ensure that this framework avoids algorithmic paternalism, these capabilities and metrics must be grounded in pluralism and representative societal input.
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Invited Talk Jul 9, 1:30 PM - 2:30 PM HALL C

What will be left for us to work on?

About the speaker Arvind Narayanan

Arvind Narayanan
Arvind Narayanan is a professor of computer science at Princeton University and the director of the Center for Information Technology Policy. He is a co-author of the book *AI Snake Oil*, the essay *AI as Normal Technology*, and a newsletter of the same name which is read by over 75,000 researchers, policy makers, journalists, and AI enthusiasts. He previously co-authored two widely used computer science textbooks: *Bitcoin and Cryptocurrency Technologies* and *Fairness in Machine Learning*. Narayanan led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes. Narayanan was one of TIME's inaugural list of 100 most influential people in AI. He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE).

Abstract

Given rapid advances in AI, how should researchers and developers shift how we allocate our time? What new skills should we build so that we’re not obsolete in the future? I argue that there will be plenty for us to work on, grounded in the “AI as normal technology” thesis, which holds that there are many bottlenecks between AI capability improvements and automation of tasks or jobs. The evidence suggests that AI is better seen as an augmentation than an automation technology. The balance of human effort will shift towards tasks that are less verifiable — from developing models to scaffolds, and from building towards evaluation and monitoring. Over the long term, as purely technical skills are devalued, both researchers and developers will have to adapt. In research, human effort will migrate from problem solving to question asking and conceptual progress; in industry, relational skills, domain knowledge, aesthetic and normative judgment will gain in importance.
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