2025 QEPrize Winners
Modern Machine Learning

The 2025 Queen Elizabeth Prize for Engineering is awarded to seven engineers who have made seminal contributions to the development of Modern Machine Learning, a core component of artificial intelligence (AI) advancements.

Modern machine learning enables systems to learn from data, identify patterns, and make decisions or predictions without being explicitly programmed for every scenario. This capacity for self-improvement is crucial in advancing AI, as it allows models to adapt and improve over time as they encounter new data.

The recent advances in machine learning rely on innovations in algorithms, processing power and benchmark datasets. It is the combination of these interrelated breakthroughs that underpins the widespread adoption and application of AI systems.

Yoshua Bengio, Geoffrey Hinton, John Hopfield and Yann LeCun have long championed artificial neural networks as an effective model for machine learning and this is now the dominant paradigm. Together they are responsible for the conceptual foundations of this approach.

Jensen Huang and Bill Dally have led developments in the hardware platforms that underpin the operation of modern machine learning algorithms. The vision of exploiting Graphics Processing Units and their subsequent architectural advances have enabled the scaling that has been central to their successful application.

Fei-Fei Li established the importance of providing high quality datasets, both to benchmark progress and underpin the training of machine learning algorithms. By creating ImageNet, a large-scale image database used for object recognition software research, she enabled access to millions of labelled images that have been instrumental in training and evaluating computer vision algorithms.

Together, the work of these engineers has laid the foundations for the machine learning that lies behind many of the most exciting innovations shaping the world today.