Abstract: The faster AI automation spreads through the economy, the more profound its potential impacts, both positive (improved productivity) and negative (worker displacement). The previous literature on AI Exposure cannot predict this pace of automation since it attempts to measure an overall potential for AI to affect an area, not the technical feasibility and economic attractiveness of building such systems. In this work, we present a new type of AI task automation model that is end-to-end, estimating: the level of technical performance needed to do a task, the characteristics of an AI system capable of that performance, and the economic choice of whether to build and deploy such a system. The result is a first estimate of which tasks are technically feasible and economically attractive to automate - and which are not. We focus on computer vision, where cost modeling is more developed. We find that at today's costs U.S. businesses would choose not to automate most vision tasks that have AI Exposure, and that only 23% of worker wages being paid for vision tasks would be attractive to automate. This slower roll-out of AI can be accelerated if costs fall rapidly or if it is deployed via AI-as-a-service platforms that have greater scale than individual firms, both of which we quantify. Overall, our findings suggest that AI job displacement will be substantial, but also gradual - and therefore there is room for policy and retraining to mitigate unemployment impacts.

Details of this work can be found here.

Speaker Bio: Neil Thompson is the Director of the FutureTech research project at MIT's Computer Science and Artificial Intelligence Lab and a Principal Investigator at MIT's Initiative on the Digital Economy.

Previously, he was an Assistant Professor of Innovation and Strategy at the MIT Sloan School of Management, where he co-directed the Experimental Innovation Lab (X-Lab), and a Visiting Professor at the Laboratory for Innovation Science at Harvard. He has advised businesses and government on the future of Moore's Law, has been on National Academies panels on transformational technologies and scientific reliability, and is part of the Council on Competitiveness' National Commission on Innovation & Competitiveness Frontiers.

He has a PhD in Business and Public Policy from Berkeley, where he also did Masters degrees in Computer Science and Statistics. He also has a masters in Economics from the London School of Economics, and undergraduate degrees in Physics and International Development. Prior to academia, He worked at organizations such as Lawrence Livermore National Laboratory, Bain and Company, the United Nations, the World Bank, and the Canadian Parliament.

Location: IACS Seminar Room
AI + Music Seminar - The meeting will consist of introductions and organizational discussions, aimed at understanding participants' interests. We'll discuss what the seminars can focus on going forward.

Abstract: The remarkable success of large foundational models, such as LLMs and diffusion models, is built on their learning over vast amounts of static data from the Internet. However, human learning and problem-solving are fundamentally interactive processes--humans learn by engaging with their environment, tools, search engine, and feedback loops, iteratively refining their understanding and decisions. This gap between the interactivity of human learning and the static nature of model training raises a critical question: how can we imbue foundational models with the capacity for meaningful interaction?

In this talk, I will explore methods to enhance foundational models by incorporating interaction with the external environment. I will discuss strategies such as leveraging external tools, compilers, function calls to provide dynamic feedback to enhance foundation models. By drawing inspiration from human's interactive learning processes, I demonstrate how interaction-driven learning can lead to models that are not only more accurate but also more adaptable to real-world applications.

This work bridges the gap between static training paradigms and the dynamic, iterative nature of human intelligence, paving the way for a new generation of interactive AI systems.

Bio: Wenhu Chen has been an assistant professor at the Computer Science Department in University of Waterloo and Vector Institute since 2022. He obtained the Canada CIFAR AI Chair Award in 2022 and CIFAR Catalyst Award in 2024. He has worked for Google Deepmind as a part-time research scientist since 2021. Before that, he obtained his PhD from the University of California, Santa Barbara under the supervision of William Wang and Xifeng Yan. His research interest lies in natural language processing, deep learning and multimodal learning. He aims to design models to handle complex reasoning scenarios like math problem-solving, structure knowledge grounding, etc. He is also interested in building more powerful multimodal models to bridge different modalities. He received the Area Chair Award in AACL 2023, the Best Paper Honorable Mention in WACV 2021, the Best Paper Finalist in CVPR 2024, and the UCSB CS Outstanding Dissertation Award in 2021.

Ready for Round Two? Dr. Zach Justus Returns! Join us on October 30, 2025, in the SBU Hilton Garden Inn. Buckle up your curiosity for a high-energy morning session with the engaging Dr. Zach Justus as we navigate how GenAI is reshaping not just how we teach, but what we teach. With real talk and questions that hit hard like Are students learning what we think we're teaching? This is your chance to rethink your program's true destination. Whether you're looking to pick up a few takeaways or chart a new direction entirely, this symposium is your space to explore, reflect, and act.

Check-in and breakfast will begin at 8:30 a.m. in order to begin our program promptly at 9:00 a.m.

Registration will remain open until October 15 or until the event reaches capacity. If closed, please contact educationaleffectiveness@stonybrook.edu to request a spot on the waitlist.

The GE Vernova Advanced Research Center invites you to the 2026 AI EDGE Symposium.

This collaborative and hands-on learning experience connects 300+ industry thought leaders in the fields of AI, Edge, Robotics, Cybersecurity, and Controls & Optimization.


Attendees will learn about the latest challenges and innovations with luminaries from key government agencies, industry customers, and technology partners as they discuss the latest advancements and trends in these fields.

Register here.
The Division of Educational & Institutional Effectiveness is excited to host International Love Data Week at SBU, February 9-13, 2026!
Join us for a mix of 30-minute virtual sessions, an in-person kickoff on Monday, and a student-focused event on Wednesday celebrating data and data-informed decision-making.
Wrap up the week at the Love Data Week Open House on Friday, 2/13 with light refreshments, data-themed swag, photos with Wolfie, and time to connect with presenters.
Learn more and register on https://www.stonybrook.edu/commcms/oee/recognition/Love%20Data%20Week%202026%20Save%20the%20Date%20Placeholder.php
Abstract Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life- like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506 https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG95UT09 Bio Professor Henry Liu is a professor in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at the University of Michigan Transportation Research Institute and the Director for the Center for Connected and Automated Transportation (USDOT Region 5 University Transportation Center). Prof. Liu conducts interdisciplinary research at the interface between civil and mechanical engineering. Specifically, his scholarly interests concern traffic flow monitoring, modeling, and control, as well as testing and evaluation of connected and automated vehicles. He has published more than 100 refereed journal papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019) in the prestigious Transportation Research journal. Professor Liu and his work have been widely recognized in public media for promoting smart transportation innovations. He has appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor Liu was invited to testify on national transportation research agenda in front of the US House Subcommittee on Research and Technology. Professor Liu has nurtured a new generation of scholars, and some of his PhD students and postdocs have joined first class universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the managing editor of Journal of Intelligent Transportation Systems.