"AI Policymaking: A Tale of Two Domains"

By Barry O'Sullivan, Visiting Professor of Computer Science in the McCormick School of Engineering

O'Sullivan is a professor at the School of Computer Science & IT at University College Cork, a fellow and past president of the European AI Association, and an executive council member of the Association for the Advancement of Artificial Intelligence.

Abstract: Over the last several years, there has been considerable focus on the ethical concerns that arise due to the deployment of artificial intelligence (AI) systems. A variety of policy approaches have emerged internationally in this respect. The European Union (EU) has taken the approach to introduce a specific regulation for AI, the EU AI Act. In this talk, O'Sullivan will present a perspective on the origins of the EU’s approach to AI ethics, its notion of Trustworthy AI, and the development of the EU AI Act. However, most international AI governance efforts have focused on the civilian domain only with little consensus in relation to AI applied in military, intelligence, and national security settings. O'Sullivan will present his experiences of developing a code of conduct for the use of AI in the military domain, and specifically the outcomes to date and current state of a longstanding Track II diplomacy dialogue between U.S., Chinese, and international experts. He will contrast the differences in developing governance mechanisms for AI in these two contrasting domains and highlight some opportunities and challenges in moving things forward on an international level.

This event is part of the Fay Lomax Cook Winter 2025 Colloquium Series, where our researchers from around the University share their latest policy-relevant research.

Please note all colloquia this quarter will be held in-person only.

Title: Inferring thermodynamic limits on non-equilibrium membrane morphologies

Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research.

Jordon Shivers is a Schmidt AI in Science Postdoctoral Fellow at the University of Chicago, where he is jointly mentored by Suri Vaikuntanathan and Aaron Dinner. Shivers is broadly interested in the physics and engineering of active soft and biological matter, and tackles problems in this area using a combination of computer simulations, theoretical soft matter physics, and machine learning techniques, often in collaboration with experimental groups.

Learn more about Jordon Shivers’ research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.

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AI may soon become a key driver of geopolitical competition, with countries vying for technological supremacy and economic dominance. The potential of AI technologies to revolutionize industries, enhance military capabilities and shape societal norms has far-reaching implications for the disruption of traditional geopolitical balances, particularly as AI development outpaces the ability of policymakers to establish comprehensive governance frameworks.

What are the geopolitical risks and opportunities associated with AI development? What strategies are being developed to prevent the misuse of AI? How can states promote responsible and ethical AI development to shape the future of AI in a way that benefits humanity?

Join us for our winter quarter Buffett Symposium on AI and Geopolitics convening leading strategists, researchers and policymakers to discuss the transformative opportunities and profound challenges that AI poses in geopolitics. These leaders will offer insights on the increasing influence of AI technologies on global power dynamics, national security, economic development, international relations and more.

Co-sponsored by the Northwestern Security & AI Lab (NSAIL) and University College Cork.

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The Technology & Social Behavior PhD program is excited to welcome Dr. Tal August of University of Illinois at Urbana-Champaign to campus on Thursday, January 16th. Tal will give a talk, titled AI for Science Communication: Adapting to Different Stakeholders.

The talk is in-person, live, only in the Center for Human-Computer Interaction + Design. Light lunch fare will be served.

Abstract: Communicating complex scientific ideas to the public is critical for an equitable, informed society, but doing so without misleading or overwhelming people is challenging. As large language models become more capable at summarizing and simplifying scientific text, we have a unique opportunity to use these models to make science more accessible. In this talk I will share my group’s research developing language tools and systems to help communicate science to more people. I will highlight two key communication strategies—based on our previous work—focused on different levels of language: explaining new findings from scientific papers and defining individual scientific terms. For both, I will discuss novel techniques we developed for adjusting generated language to fit the needs of different audiences and methods for modeling an individual reader’s background. I will close by discussing how these techniques generalize to other knowledge intensive communication tasks (e.g., legal and educational settings) and the opportunities of developing new techniques for these settings.

Bio: Tal August is an assistant professor at the University of Illinois at Urbana-Champaign. He studies how to adapt language to different audiences, with a focus on knowledge intensive domains like science, health and legal communication. Tal conducts empirical analyses to study how changes in language will affect different audiences, and he builds intelligent reading and writing systems for augmenting our language in new ways. The long term goal of Tal’s research is to improve our communication with—and understanding of—one another through technology. Tal August previously was a Young Investigator at the Allen Institute for AI. Tal received his PhD at the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Katharina Reinecke and Noah Smith.

Sara Solla is a Professor of Physics and Physiology at Northwestern University. Solla’s research interests lie in the application of statistical mechanics to the analysis of complex systems. Her research has led her to the study of neural networks, which are theoretical models that incorporate "fuzzy logic" and are thought to be in some aspects analogous to the way the human brain stores and processes information. She has used spin-glass models (originally developed to explain magnetism in amorphous materials) to describe associative memory, worked on a statistical description of supervised learning, investigated the emergence of generalization abilities in adaptive systems, and studied the dynamics of incremental learning algorithms.

Learn more about Sara Solla's research

The NSF-Simons National Institute for Theory and Mathematics in Biology Seminar Series aims to bring together a mix of mathematicians and biologists to foster discussion and collaboration between the two fields. The seminar series will take place on Fridays from 10am - 11am at the NITMB offices in the John Hancock Center in downtown Chicago. There will be both an in-person and virtual component.

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Are you looking to speed up your coding workflow? GitHub Copilot, an AI-powered coding assistant, can enhance your productivity by helping you write code faster and more efficiently. In this workshop, you’ll get an introduction to GitHub Copilot, including best practices and important considerations for using it effectively in your projects. We’ll also demonstrate how to use GitHub Copilot in VS Code and RStudio.

Prerequisites: Prior to the workshop, participants will receive instructions for obtaining access to GitHub Copilot. While access is not required to attend, having it will ensure you get the most out of the session.

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Abstract: For centuries, researchers have sought out ways to connect disparate areas of knowledge. With the advent of Artificial Intelligence (AI), we can now rigorously explore relationships that span across distinct areas – such as, mechanics and biology, or science and art – to deepen our understanding, to accelerate innovation, and to drive scientific discovery. However, many existing AI methods have limitations when it comes to physical intuition, and often hallucinate. To address these challenges, we present research that blurs the boundary between physics-based and data-driven modeling through a series of physics-inspired multimodal graph-based generative AI models, set forth in a hierarchical multi-agent mixture-of-experts framework. The design of these models follows a biologically inspired approach where we re-use neural structures and dynamically arrange them in different patterns and utility, implementing a manifestation of the universality-diversity-principle that forms a powerful principle in bioinspired materials. This new generation of models is applied to the analysis and design of materials, specifically to mimic and improve upon biological materials. Applied specifically to protein engineering, the talk will cover case studies covering distinct scales, from silk, to collagen, to biomineralized materials, as well as applications to medicine, food and agriculture where materials design is critical to achieve performance targets.

Bio: Markus J. Buehler is the McAfee Professor of Engineering at MIT. Professor Buehler pursues new modeling, design and manufacturing approaches for advanced bio-inspired materials that offer greater resilience and a wide range of controllable properties from the nano- to the macroscale. He received many distinguished awards, including the Feynman Prize, the ASME Drucker Medal, the J.R. Rice Medal, and many others. Buehler is a member of the National Academy of Engineering.

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Members of the NITMB community are invited to join us for Research-In-Progress meetings, an informal venue for members of the NITMB to discuss ongoing and/or planned research. Efe Gökmen is an NITMB Fellow. Gökmen’s expertise lies at the crossroads of machine learning, statistical physics, and information theory. Learn more about Efe Gökmen’s research and engage in discussion with the NITMB community. Research-In-Progress talks take place on Wednesdays at 3pm at the NITMB office (875 N Michigan Ave., Suite 4010). Snacks and coffee will follow.

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Are you looking to teach a class where your students need access to GPUs? Do you need more GPUs than are available on Quest to train new AI models? Do you have specific hardware needs or are you hoping to deploy a science gateway? ACCESS-CI and NAIRR (National AI Research Resource) are two federally-funded computing resources available to support these and other computing needs. This workshop will introduce you to these platforms and cover how to set up accounts and request resources. This workshop will not be recorded.

Prerequisites: It may be helpful to have an account on the ACCESS-CI platform. You can sign up for an account on the ACCESS CI Registration Page if interested.

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The Role of AI in Scientific Discovery: Opportunities and Limitations

Xiangliang Zhang, Leonard C. Bettex Collegiate Professor of Computer Science, University of Notre Dame

Abstract: Artificial Intelligence (AI) is reshaping the landscape of scientific discovery, enabling breakthroughs across diverse fields. However, when these AI tools are applied to scientific problems, gaps and mismatches often arise. The inherent uncertainty in scientific phenomena, coupled with issues like data quality, biases, and interpretability, poses significant challenges. This talk will discuss the transformative potential of AI in scientific discovery, focusing on its applications in predictive modeling, generative tasks, optimization strategies, and literature analysis. Examples will include AI models ranging from traditional neural networks to large language models (LLMs). At the same time, their limitations will be critically examined, calling for collaboration between the AI and scientific communities to address these challenges and unlock AI’s full potential in advancing scientific discovery.

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Would you like to simplify your machine learning code and minimize repetitive tasks? Scikit-Learn's pipelines can help you organize and streamline your data processing and model training, as well as make your code cleaner and easier to manage. In this workshop, we will cover why and how to use pipelines in your machine learning code.

Prerequisites: Participants should be familiar with Python at the level of the Python Fundamentals Bootcamp, another introductory Python workshop, or be a self-taught Python coder. Basic familiarity with machine learning and Scikit-Learn is required.

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In recent years generative artificial intelligence (GAI) tools have increased in availability and popularity at universities, but not everyone knows how to use them for better library search results. In this 60-minute hybrid session we will demonstrate how to use Microsoft Copilot, a free GAI tool for all NU students and faculty, to improve searches with the gold standard of reliable content, library databases. This session is geared toward participants with little-to-no familiarity with generative artificial intelligence, and will also address the basics of what generative artificial intelligence is, how it works, its risks, and limitations. Students can learn about NU’s Copilot accounts here.

This workshop is presented by Tracy Coyne, Distance Learning and Professional Studies Librarian; Frank Sweis, User Experience Librarian; and Jeannette Moss, User Education Librarian.

A Northwestern Zoom Account is required to access this session.

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Drawing on the Northwestern Engineering whole-brain philosophy and leadership in cognitive science, the Master of Science in Artificial Intelligence program would like to invite you to learn more at our upcoming webinar.

Take this opportunity to join Dr. Kristian Hammond, Professor of Computer Science and director of the MSAI program, as he discusses the complexities of this field, and how this newly offered program at Northwestern Engineering will prepare students for a career in artificial intelligence. At the end of the presentation, we will offer an open Q&A where you will be able to have your specific questions answered. You are also welcome to email your questions to us ahead of the session (msai@northwestern.edu).

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Curious about how AI can transform your text data analysis? Large Language Models (LLMs), like those behind ChatGPT, offer powerful ways to extract information from text, such as identifying key individuals, finding information about events, or selecting sections of documents. Learn how LLMs can assist with information extraction, and how they compare with other approaches such as rule-based methods, regular expressions, and pre-trained entity recognition models.

Prerequisites: Open to anyone working with or interested in text data, this workshop will provide hands-on examples in Python, though the concepts apply across various programming languages. While prior experience in natural language processing is helpful, it’s not required to participate.

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