Join Stony Brook University's Center for Excellence in Learning and Teaching (CELT) for a bootcamp on how to use AI to enhance your teaching and courses. This event will demonstrate how ChatGPT, Microsoft Copilot, and other generative AI platforms can support you in crafting learning objectives, writing exam questions, composing rubrics, and designing course content such as lesson plans, in-class activities, instructional videos, and more.

Register here.

The Della Pietra Lecture Series is pleased to present this lecture by Scott Aaronson:

Abstract: I'll survey some areas where I think theoretical computer science, math, and statistics can potentially contribute to the urgent quest to align powerful AI with humane values. These areas include: the watermarking of AI outputs, mechanistic interpretability (including Paul Christiano's No-Coincidence Principle, and succinct digests of the training process to aid interpretability), and theoretical guarantees for out-of-distribution generalization.

Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.

Location: Della Pietra Family Auditorium (SCGP 103)
Abstract: Reward hacking, where a reasoning model exploits loopholes in a reward function to achieve high rewards without solving the intended task, poses a significant threat. This behavior may be explicit, i.e. verbalized in the model's chain-of-thought (CoT), or implicit, where the CoT appears benign thus bypasses CoT monitors. To detect implicit reward hacking, we propose TRACE (Truncated Reasoning AUC Evaluation). Our key observation is that hacking occurs when exploiting the loophole is easier than solving the actual task. This means that the model is using less effort than required to achieve high reward. TRACE quantifies effort by measuring how early a model's reasoning becomes sufficient to obtain the reward. We progressively truncate a model's CoT at various lengths, force the model to answer, and estimate the expected reward at each cut-off. A hacking model, which takes a shortcut, will achieve a high expected reward with only a small fraction of its CoT, yielding a large area under the reward-vs-length curve. TRACE achieves over 65% gains over our strongest 72B CoT monitor in math reasoning, and over 30% gains over a 32B monitor in coding. We further show that TRACE can discover unknown loopholes during training. Overall, TRACE offers a scalable unsupervised approach for oversight where current monitoring methods prove ineffective.

Speaker: Dikshya

Location: Old Computer Science Building - CS2311

AI on Campus: Your Thoughts, Your Future

Join the Conversation: Share Your Thoughts about Learning, Academics, and AI

The world of college is changing fast, and Artificial Intelligence (AI) is at the center of it. We are part of the Institute on AI, Pedagogy, and the Curriculum with AAC&U, and we need to hear from the people AI affects most: you!

This is an open discussion for all students to share their honest experiences, their top concerns, and their best ideas about AI in our academic environment. We'll be diving into these key questions:

  • How can AI actually make learning better or easier? What opportunities do you see for using AI tools to enhance your assignments, research, or skills?

  • What are your biggest worries about AI? Is it about cheating, being graded fairly, or preparing for the job market? How is AI impacting your workload or stress levels?

  • What specific tools, workshops, or policies would help you use AI responsibly and successfully? (Think training, software, or clear rules.)

Dates/Times:

  • Wednesday, 2/4 at 2pm

  • Thursday, 2/5 at 12pm

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

Don't worry if you can't attend! You can still share your thoughts via video in our AI Zoom Room or via email: rose.tirotta-esposito@stonybrook.edu.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your voice matters. Come tell us how AI is affecting your studies, your stress, and your success!

  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)

  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)

  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)

  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)

  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)

Abstract: Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into \textit{why} a computation fails. We introduce a white-box method: \textbf{Circuit-based Reasoning Verification (CRV)}. We hypothesize that attribution graphs of correct CoT steps, viewed as \textit{execution traces} of the model's latent reasoning circuits, possess distinct structural fingerprints from those of incorrect steps. By training a classifier on structural features of these graphs, we show that these traces contain a powerful signal of reasoning errors. Our white-box approach yields novel scientific insights unattainable by other methods. (1) We demonstrate that structural signatures of error are highly predictive, establishing the viability of verifying reasoning directly via its computational graph. (2) We find these signatures to be highly domain-specific, revealing that failures in different reasoning tasks manifest as distinct computational patterns. (3) We provide evidence that these signatures are not merely correlational; by using our analysis to guide targeted interventions on individual transcoder features, we successfully correct the model's faulty reasoning. Our work shows that, by scrutinizing a model's computational process, we can move from simple error detection to a deeper, causal understanding of LLM reasoning.

Speaker: Xianjun Yang

Location: Old Computer Science Building - CS2311

The Department of AI and Society (AIS) at the University at Buffalo is hosting a two-day AI and Society Workshop focused on building AI systems by society, for society. This workshop brings together researchers and community organizers to explore how AI systems can be developed through meaningful collaboration across disciplines.

Topics include:

  • Labor and AI
  • Public services and AI
  • Community-centered AI systems
  • Intersections of humanities, social sciences, arts, and computing

The vision of UB's Department of AI and Society is to create a future where AI systems are built by society, for society. AIS centers community engagement at every stage of AI development through collaboration across disciplines and sectors. AIS was established with a $5 million grant from SUNY, and this workshop is made possible through that support.

Who Should Attend?

  • Researchers
  • Students
  • Community organizers
  • Practitioners interested in AI's societal impact

More about the event

Register here


The Office of Proposal Development is pleased to announce the next installation of the series, Translating the Funding Landscape for Stony Brook Researchers. This virtual presentation series is designed to inform the Stony Brook University research community about funding trends across key topic areas. Our Strategic Research Initiatives team will provide insight into the current funding environment using policy briefs, budget priorities, and relevant legislation. We will highlight federal and state priorities for the current and upcoming years to help Stony Brook researchers develop strategies for pursuing funding in a competitive environment. This series is moderated by Mónica Bugallo, Interim Vice President for Research & Innovation.

Join us for the third in the series, focused on artificial intelligence:

Translating the Funding Landscape for Stony Brook Researchers: Artificial Intelligence

Presented by Catherine Chen, PhD, Research Development Associate

Faculty Respondent: Nav Nidhi Rajput, Assistant Professor, Department of Materials Science and Chemical Engineering

Mind Brain Lecture: Constructing the World of Taste in Your Head You fork the morsel into your mouth and say yum...chocolate cake. The appreciation of your dessert's taste seems to follow directly, quickly and simply from the placement of the food on your tongue. The truth, however, is far more interesting and complex: your brain actually begins determining whether you will enjoy a bite of food even before the fork approaches your mouth and continues to work the problem well after. Information about your food's color, smell, texture and taste activates multiple parts of your brain, where that information collides with your pre-mouthful beliefs about how it should taste. The coming-together and shuffling of that information around the brain takes time, as networks of neurons work together to help you decide whether the morsel in your mouth is worth swallowing. Referring to work from psychology, biology and computational neuroscience, Professor Katz will de-mystify and reveal the beauty of these complexities of the neuroscience of taste. Donald Katz, Professor of Psychology, Departments of Neuroscience, Psychology, and the Volen National Center for Complex Systems, Brandeis University Free presentation intended for a general audience. Reception to follow. https://www.stonybrook.edu/commcms/mind/