Keynote Speakers
Prof. Michael Jordan
UC Berkeley, USA
Title
A Collectivist View on AI: Collaborative Learning, Statistical Incentives, and Social Welfare
Abstract
Artificial intelligence (AI) has focused on a paradigm in which intelligence
inheres in a single, autonomous agent. Social issues are entirely secondary
in this paradigm. When AI systems are deployed in social contexts, however,
the overall design of such systems is often naive—a centralized entity provides
services to passive agents and reaps the rewards. Such a paradigm need not be
the dominant paradigm for information technology. In a broader framing, agents are
active, they are cooperative, and they wish to obtain value from their participation
in learning-based systems. Agents may supply data and other resources to the system,
only if it is in their interest to do so. Critically, intelligence inheres as much
in the overall system as it does in individual agents, be they humans or computers.
This is a perspective that is familiar in the social sciences, and a key theme in
my work is that of bringing economics into contact with foundational issues in
computing and data sciences. I’ll emphasize some of the design challenges
that arise at this tripartite interface.
Deepak Bansal
Corporate Vice-President, Microsoft Azure, USA
Title
Leveraging LLMs for Networking & Security in Cloud Environments
Abstract
Title
Visual Discovery and Understanding in Satellite Imagery
Abstract
We are capturing visual data of the planet at an unprecedented scale through satellite imagery, drones, social media photo collections, and more. Tools to understand and discover insights from this visual data are of immense value to scientists, cultural anthropologists, and policy makers. In this talk I will describe research from my group that analyzes spatio-temporal image collections to understand a wide range of phenomena including style trends, cultural events, crop cycles, natural disasters like wildfires, and more. We introduce new vision-language models to allow unsupervised discovery of open-world concepts, to discover spatio-temporal trends and events, and new datasets and benchmarks for satellite events. I will describe potential applications of these tools to crop science, climate science, disaster discovery, and cultural erasure.
Bio
Kavita Bala is the inaugural dean of the Cornell Bowers College of Computing and Information Science at Cornell University. Bala received her S.M. and Ph.D. from the Massachusetts Institute of Technology (MIT). Before becoming dean, she served as the chair of the Cornell Computer Science department. Bala leads research in computer vision and computer graphics in visual discovery and recognition; material modeling and acquisition; physically based rendering; and perception. She co-founded GrokStyle, a visual recognition AI company, which drew IKEA as a client, and was acquired by Facebook in 2019. Bala is the recipient of the SIGGRAPH Computer Graphics Achievement Award, the IIT Bombay Distinguished Alumnus Award, and is a Fellow of the Association for Computing Machinery (ACM) and the SIGGRAPH Academy. Bala has received multiple teaching awards, has served as the Editor-in-Chief of Transactions on Graphics (TOG), and serves on the boards of the Toyota Technological Institute at Chicago (TTIC), and non-profits Colorstack, aimed at increasing representation in computer science, and the Ithaca Sciencenter.
Prof. Dan Roth
Oracle and the University of Pennsylvania
Title
Reasoning Myths about Language Models: What is Next?
Abstract
The rapid progress made over the last few years in generating linguistically coherent natural language has blurred, in the mind of many, the difference between natural language generation, understanding, and the ability to reason with respect to the world. Nevertheless, robust support of high-level decisions that depend on natural language understanding, and one that requires dealing with “truthfulness” are still beyond our capabilities, partly since most of these tasks are very sparse, often require grounding, and may depend on new types of supervision signals.
I will discuss some of the challenges underlying reasoning and argue that we should focus on LLMs as orchestrators – coordinating and managing multiple models and special purpose agents. I will discuss some of the challenges and present some of our work in this space, focusing on supporting planning and a range of quantitative, visual, and spatial reasoning tasks.
Bio
Dan is a Fellow of the AAAS, ACM, AAAI, and ACL. In 2017, Dan was awarded the John McCarthy Award; he was recognized for “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.” He has published broadly in natural language processing, machine learning, knowledge representation and reasoning, and learning theory, was the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR) and has served as a Program Chair and Conference Chair for the major conferences in his research areas. Roth has been involved in several startups; most recently he was a co-founder and chief scientist of NexLP, a startup that leverages the latest advances in Natural Language Processing, Cognitive Analytics, and Machine Learning in the legal and compliance domains. NexLP was acquired by Reveal. Dan received his B.A Summa cum laude in Mathematics from the Technion, Israel and his Ph.D. in Computer Science from Harvard University in 1995.
Prof. Shree Nayar
Columbia University, USA
Title
Computational Imaging and Future Cameras
Abstract
Computational imaging uses new optics to capture a coded image, and an appropriate algorithm to decode the captured image. This approach has enabled mobile devices to produce images that are rich, immersive and interactive. In this talk, we will show examples of computational cameras that are transforming the way visual information is captured, communicated and used by both humans and machines.