AI increasingly augments research practices, redefines scientific workflows, and evolves teaching and training—requiring new skills, ethical frameworks, and collaborative infrastructures across disciplines.

Join us on Monday, March 16 for Kate Smith-Miles’ lecture, Beyond the Hype: Towards Trustworthy AI and Quantum Technologies through Rigorous “Stress‑Testing”!

Technologies such as AI and quantum computing promise to transform science, engineering, and society—but bold claims often outpace what these technologies can actually deliver. How do we separate genuine breakthroughs from hype? And what does it take to build technologies we can truly trust?

This lecture explores these questions through one deceptively simple challenge: the travelling salesperson problem (TSP). For decades, the TSP has been held up as a showcase for new technologies for several reasons: it is a computationally challenging problem; it is easy to explain; and it has significant industrial relevance to real‑world logistics. After two decades of effort however, today’s quantum optimization methods still struggle fundamentally with the mathematical structure of the problem, just as neural networks did forty years ago. The gap between promise and reality offers an important lesson about how current quantum technologies are particularly vulnerable to hype, inflated claims, and misplaced expectations while we await true breakthroughs. AI, too, has cycled repeatedly through periods of over‑claiming and subsequent correction—reminding us that such dynamics are not unique to quantum computing but characteristic of rapidly evolving technologies.

In this lecture, I explore how trust and integrity in algorithmic innovation can be strengthened through rigorous performance evaluation and transparent “stress‑testing”. To address these challenges, I introduce Instance Space Analysis, a framework for mapping the strengths and limitations of algorithms across diverse problem instances. This methodology helps distinguish genuine capability from over‑claiming, and offers a blueprint for building trustworthy algorithmic tools. By examining both AI and quantum optimization through this lens, the lecture highlights how scientific integrity can guide innovation beyond the hype cycle.

About

Professor Kate Smith-Miles is a Melbourne Laureate Professor of Applied Mathematics at the University of Melbourne, where she is also Pro Vice-Chancellor (Research Capability), responsible for supporting researchers across all disciplines to deliver research of the highest quality, integrity and impact. She also works closely with industry as Director of the ARC Training Centre for Optimisation Technologies, Integrated Methodologies and Applications (OPTIMA). Kate is internationally recognized for her contributions to optimisation, neural networks, machine learning, applied mathematics, and the development of methodologies for trustworthy algorithm evaluation. Her research has pioneered Instance Space Analysis, a framework that reveals when and why algorithms succeed or fail across diverse problem landscapes. This work led to the creation of MATILDA, an online platform supporting rigorous algorithm benchmarking and transparent stress‑testing. Kate has published two books and over 300 refereed papers, supervised more than 30 PhD graduates, and attracted over AUD$70 million in competitive research funding. She has received numerous medals and awards for her research, including a prestigious Australian Laureate Fellowship. She is a Fellow of the Australian Academy of Science, a former President of the Australian Mathematical Society, and was appointed an Officer of the Order of Australia in 2024 for distinguished service to applied mathematics and advocacy for women in STEM.

Hybrid Event

Whether you’re a student or simply interested in how Artificial Intelligence is changing science and engineering practices–this lecture series is open to everyone:

• We look forward to meeting you in person in TU Wien’s beautiful Festsaal at 1040 Vienna, Karlsplatz 13, 1st floor, room AA0148.
• If you can’t make it person, switch on your laptop and join us via Zoom (Meeting ID: 661 3918 8284, Passcode: serwyN6e).

Public Lecture Series AI in Science and Engineering

Our public lecture series AI in Science and Engineering explores how AI is transforming these disciplines. Bringing together computer science, mathematics, engineering, and science in general, the series highlights how AI is reshaping discovery, design, and deployment in science and engineering.

Engaging with these changes is essential: AI already permeates daily life and has fundamentally altered how science and engineering are practiced, from data collection and analysis to experiment design, simulation, and validation. Understanding AI’s capabilities and limits helps researchers build systems that are safe, robust, and equitable. It also informs how we teach and train the next generation—integrating responsible innovation and interdisciplinary collaboration so that the scientific and engineering communities can advance AI that serves people and strengthens the foundations of science and engineering.

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