Presentations

Abstract: Attention shapes what we notice, what we can take in, and what we value. It is also the name of a key process in modern AI, although it means something different there. This talk explores these connections and differences to ask how AI works, how it is changing our everyday lives, and what deserves our attention. We begin with a simple question: how do people and computers choose what to focus on when there is too much information to process? We explore how human attention helps us make sense of the world, alongside computer models of visual attention, including work on predicting which parts of an image will attract our gaze. We then look inside the AI systems behind many familiar chatbots, explaining how their version of attention connects and combines information, how this supports many different tasks, and where comparisons with human attention can be misleading. Finally, we place AI within a longer story about technology and human attention, from social media designed to keep us engaged to tools that make polished writing and images easy to produce. As appearances become easier to reproduce, they tell us less about the care, skill, or understanding behind what we encounter. What, then, makes something worth creating, sharing, or paying attention to? I argue for more deliberate choices about what we communicate and how, and for recognizing the special value of human time, care, and emotional investment. The talk closes by asking how we might direct our attention in ways that respect our human needs and limitations while deepening our connections with what matters to ourselves and to one another.

Bio: Neil Bruce is a Professor in the School of Computer Science at the University of Guelph, a Faculty Affiliate of the Vector Institute, and a member of the Centre for Advancing Responsible and Ethical Artificial Intelligence. His research explores how people and computers make sense of visual information, with a particular focus on attention. His work includes developing models that predict what attracts our gaze and investigating how AI systems learn and use information from images and video. He holds a PhD in Computer Science from York University and a master’s degree in engineering from the University of Waterloo.

Abstract: Generative AI presents exciting opportunities to rethink how we teach, design, and evaluate, but realizing its potential requires thoughtful implementation. Drawing on practical experiences in engineering education, this talk explores applications ranging from course development and design visualization to custom AI agents supporting student feedback and evaluation. Alongside these examples, we will examine challenges related to reliability, data privacy, and accountability, emphasizing the importance of human judgment in AI-assisted workflows. The goal is to move beyond the hype and encourage informed, responsible experimentation with these emerging technologies.

Bio: Dr. Jake Reeves is an Assistant Professor in the John M. Thompson Centre for Engineering Leadership & Innovation at Western University and a member of the governing board of the Global Design Thinking Alliance. With a background in mechanical and biomedical engineering, he brings design thinking and entrepreneurial perspectives to his research and teaching. His research interests primarily focus on open-source hardware development, with the aim of making engineering technologies more accessible. He teaches undergraduate and graduate courses in engineering design and innovation management, with a particular interest in exploring how emerging technologies, including generative AI, can enhance engineering education, design processes, and innovation.