AI Innovation Institute

Abstract: In today's digital era, language functions not only as a medium of information transmission but also as a mechanism of persuasion, framing, and control. The proliferation of online platforms has amplified this dual role: while enabling unprecedented access to knowledge, it has also exacerbated challenges such as misinformation, rhetorical manipulation, and cultural or linguistic disparities in information access. As a result, pragmatic language understanding and information integrity have emerged as central concerns for both computational linguistics and society at large. This research follows how claims are produced, reframed, and contested online through three interconnected threads. First, it models pragmatic deflection in discourse by investigating whataboutism, a rhetorical device that deflects criticism by redirecting discourse, and introduced novel datasets from Twitter (now X) and YouTube. This work underscores how subtle pragmatic maneuvers can erode discourse integrity without relying on outright falsehoods. Second, it advances retrieval and alignment for information integrity in health and news communication. These systems trace claims and narratives across genres (e.g., social posts and news reports) and languages (Chinese and English), linking social posts with journalistic reporting and aligning Chinese news with English biomedical evidence. By accounting for cultural context, assertions can be linked to reliable evidence and organized for systematic comparison. This work surfaces the risks of missing sources, unverifiable claims, and framing disparities in global health discourse, and demonstrates computational solutions that enhance both the credibility and accessibility of information. Third, the methodological centerpiece is Class Distillation (ClaD), a geometry-aware training paradigm for distilling a small, well-defined target class from a large, heterogeneous background. ClaD couples a distribution-aware contrastive loss (instantiated here in a Mahalanobis form when its assumptions fit the data) with an interpretable decision algorithm tuned for class separation. Evaluated on sarcasm, metaphor, and sexism detection, ClaD delivers strong efficiency and robustness, matching or surpassing larger models while using fewer computational resources, making these pipelines practical by learning reliably from small, sharply defined classes. In sum, this research presents an integrated account of language understanding in the digital age. It exposes how integrity falters through pragmatic deflection, cross-genre drift, and cross-lingual misalignment, and translates these insights to move pragmatic language understanding to systems for evidence retrieval, alignment, and verification; and it sheds light on where and how integrity is threatened, and delivers methods that leverage pragmatic language use.

Speaker: Chenlu Wang

Location: (Old) Computer Science Building, Room 2311

The International Neuroethics Society (INS) Speaker Series on AI & Consciousness

Abstract: Colln Allen and I noted in our 2008 book Moral Machines: Teaching Robots Right From Wrong, that consciousness, a theory of mind, sociability, situational awareness and embodiment are all supra-rational (beyond reason) capabilities that contribute to making ethical decision Whether any of these can be fully instantiated in machines remains an open question. Nevertheless, moral decision making in the digital age will require an evolution in and refinement of specific skills for both humans and for AI. I call one of these evolutions in moral decision making capabilities tradeoff ethics and another a silent ethics. Aspects of this social, and not just technological evolution, will require research by neuroscientists.

Speaker Bio: Wendell Wallach has an international reputation as an expert on the ethics and governance of emerging technologies, particularly artificial intelligence and biotechnologies. He is also senior advisor to The Hastings Center and a scholar at the Yale University Interdisciplinary Center for Bioethics where he chaired Technology and Ethics studies for eleven years. Wallach's latest book, a primer on emerging technologies, is entitled, A Dangerous Master: How to keep technology from slipping beyond our control. He co-authored (with Colin Allen) Moral Machines: Teaching Robots Right From Wrong. Wallach has been referred to as, a Godfather of AI Ethics.

Pre-register here (required): https://umaryland.zoom.us/meeting/register/sPpiR_drR4-9JYDhI2NhJg

Title: Cyberinfrastructure for forward prediction and inversion estimation with uncertainty quantification

Seminar Speaker: Dr. Mengyang Gu, Assistant Professor, Department of Statistics and Applied Probability, University of California, Santa Barbara

Abstract: In this talk, we introduce four useful tools for forward prediction and inversion estimation. The first tool is the parallel partial Gaussian process surrogate model for emulating expensive computer simulations with massive coordinates. The tool is implemented in the RobustGaSP package available in R, MATLAB, and Python, for predicting both scalar- and vector-valued outputs with uncertainty assessment. The second tool is implemented in the RobustCalibration package, which handles Bayesian data inversion or model calibration by one or multiple types of experimental observations. A unique feature of the package is the inclusion of fast surrogate models of both scalar- and vector-valued computer simulations that bypass the expensive simulation in one line of code. The third tool is implemented in the AIUQ package, available in both R and MATLAB. In this approach, we show that differential dynamic microscopy, a scattering-based analysis tool that extracts dynamical information from microscopy videos, is equivalent to fitting the temporal auto-covariance in Fourier space, based on a latent factor model we construct. We develop a more efficient estimator and reduce the computational cost to pseudolinear order with respect to the number of observations without approximation, by utilizing the generalized Schur algorithm for the Toeplitz covariance. In the last tool, we developed a new method called the inverse Kalman filter, which enables fast matrix-vector multiplication between a covariance matrix from a dynamic linear model and any real-valued vector with a linear computational cost. These new approaches outline a wide range of applications that include emulating expensive simulation at molecular-, meso- and macro-scales, active learning with error control, nonparametric estimation of particle interaction functions, and data inversion from microscopy and velocity fields.

Join Zoom Meeting: https://bnl.zoomgov.com/j/1606285496?pwd=2yJYSG6lx8gMPiibzgAIBQtKHIjuHV.1
Meeting ID: 160 628 5496
Passcode: 472506

Join CELT on Tuesday, March 31 for a focused, one-hour overview on how to redesign and future-proof assessments in the age of AI! This session will cover three key areas: leveraging AI as a co-pilot for developing effective exam questions, designing authentic assessments, and exploring how AI can strategically support active learning structures like Team-Based Learning (TBL), Project-Based Learning (PBL), and Scenario-Based Learning (SBL).

Register here.

Title: Building foundation models for scientific data Seminar

Speaker: Ruben Ohana, Ph.D. and Michael McCabe, Ph.D - Flatiron Institute, New York

Abstract: Foundation models are very large architectures trained on large-scale datasets and can be used to transfer knowledge from a domain to another. Scientific data, particularly numerical simulations of partial differential equations (PDEs), presents unique challenges due to its complexity and the need for domain expertise to assess prediction quality, complicating the building of the first foundation models in this field. In this talk, we will develop our approach of building foundation models for scientific data, highlighting the requirements and expectations for achieving meaningful results. We will also introduce The Well, a comprehensive collection of datasets encompassing multi-scale simulations of fluid dynamics, astrophysics, and biological systems. The Well serves as a foundation for developing models that generalize across diverse physical phenomena, aiming to accelerate scientific discovery through large-scale learning.

Join Zoom Meeting: https://bnl.zoomgov.com/j/1606898802?pwd=GbbPiLGHlEokDskxjeFheMFWfuboxO.1
Meeting ID: 160 689 8802
Passcode: 281575

The Program in Writing and Rhetoric
Invites you to
A Rhetorical/Deliberative Framework for AI Language Model Alignment
featuring
Prof Zoltan Majdik Professor
North Dakota State University
In this talk, Prof. Majdik proposes a framework for aligning LLMs with values grounded in the norms of rhetorical culture and deliberative democracy. Alongside long-standing AI alignment value targets like safety and transparency, this AI alignment framework assesses to what extent a language model exhibits human and humane values that foster communicative engagement, and it codifies approaches to tuning existing models to better align with such values.

Location: Humanities 1008

Join the Department of Computer Science as we welcome Lyle Ungar, University of Pennsylvania, who will be delivering a lecture on 'Measuring Cultural Variation using Natural Language Processing.' When: 11/08/24 @ 2:30 PM Where: New Computer Science Building, Room 120. Reception to follow. Abstract: Cultures vary widely in how they view the world, for example being more individualist or collectivist. Such cultural differences are, of course, reflected in the words that people use. We first show a variety of ways in which multilingual language models are not multicultural; they speak Hindi or Mandarin, but still think like Americans. In contrast, we then present a scalable method that uses embedding-derived lexica to successfully measure regional variation in culture. Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group uses natural language processing and explainable AI for psychological research, including analyzing social media and cell phone sensor data to better understand the drivers of physical and mental well-being. They are currently building socio-emotionally sensitive GPT-based tutors and coaches.

https://stonybrook.zoom.us/j/98652715794?pwd=WE5JdzFSaVZod0dvZW5vcUFyTUk5Zz09 Meeting ID: 986 5271 5794 Passcode: 542894