Public Lecture – Deep Learning: What Could Go Wrong?

About this event

Join us online or in-person for this public lecture at ICMS.
Online tickets will receive the zoom link to join on the day of the event.
In-person attendance will be in the ICMS Lecture Theatre, Floor 5, the Bayes Centre, 47 Potterrow, Edinburgh EH89BT.

About the talk:

A traffic “Stop” sign on the roadside can be misinterpreted by a driverless vehicle as a speed limit sign when minimal graffiti is added. Wearing a pair of adversarial spectacles can fool facial recognition software into thinking that we are Brad Pitt. The vulnerability of artificial intelligence (AI) systems to such adversarial interventions raises questions around security and ethics, and many governments are now considering proposals for their regulation. I believe that mathematicians can contribute to this landscape. We can certainly get involved in the conflict escalation issue, where new defence strategies are needed to counter an increasingly sophisticated range of attacks. Perhaps more importantly, we also have the tools to address big picture questions, such as: What is the trade-off between robustness and accuracy? Can any AI system be fooled? Do proposed regulations make sense? Focussing on deep learning algorithms, I will describe how mathematical concepts can help us to understand and, where possible, ameliorate current limitations in AI technology.

About the speaker:

Des Higham (he/him), University of Edinburgh

Des Higham is a Professor of Numerical Analysis in the School of Mathematics at the University of Edinburgh. He has research interests in the design and evaluation of computational methods, and their applications in network science, data analytics and machine learning. He is a Fellow of the Royal Society of Edinburgh, of the Alan Turing Institute, and of the Society for Industrial and Applied Mathematics (SIAM). He is Editor-in-Chief of the journal SIAM Review and recently held an Established Career Fellowship from UK Research and Innovation. In 2020 he was awarded a Shephard Prize from the London Mathematical Society for research making a contribution to mathematics with a strong intuitive component which can be explained to those with little or no knowledge of university mathematics.