QEPrize awarded to AI pioneers

The collective efforts of Yoshua Bengio, Geoffrey Hinton, John Hopfield, Yann LeCun, Jensen Huang, Bill Dally, and Fei-Fei Li have been pivotal in advancing the three core pillars of Modern Machine Learning, namely advanced algorithms, high-performance hardware, and high-quality datasets. The combination of these interrelated breakthroughs has led to the widespread adoption and application of AI systems.

In a statement, Professor Dame Lynn Gladden, Chair of Judging Panel, Queen Elizabeth Prize for Engineering, said: “This year’s Prize celebrates the value of transformative breakthroughs and serves as a reminder of the importance of continuous innovation in engineering.”

Modern Machine Learning enables systems to learn from data, recognise patterns, and make predictions without explicit programming. Consequently, MML has transformed AI by allowing models to self-improve with new data.

"The profound impact of data will continue to fuel AI’s increasing power and technological capabilities, we’ll be able to use it for more scientific discovery, to make education more personalised, improve health and elder care, empower creators and designers, and address the realities of our changing planet and climate, to name just a few,” said Dr Fei-Fei Li, the inaugural Sequoia Professor in the Computer Science Department at Stanford University, and a founding co-director of Stanford’s Human-Centered AI Institute.

Yoshua Bengio, Geoffrey Hinton, John Hopfield, and Yann LeCun have been instrumental in championing artificial neural networks, which are now the dominant model for machine learning. Their research laid the conceptual foundations for this approach, enabling machines to process and learn from vast amounts of data.

Jensen Huang and Bill Dally led developments for the hardware that underpins the operation of modern machine learning algorithms. Their vision of utilising Graphics Processing Units (GPUs) and their subsequent architectural advances, has been central to scaling machine learning algorithms, making them powerful enough to support today’s AI applications.

Fei-Fei Li recognised the need for high-quality datasets to benchmark progress as well as train and evaluate machine learning models effectively. By creating ImageNet, a large-scale image database, she enabled access to millions of labelled images that have become indispensable and instrumental in training and evaluating computer vision algorithms.

"Being curious about the world is the natural state for children. With support and education, this curiosity can develop an engineer or scientist.  Add a sense of social purpose, and you have made a civilisation. It is my joy to be honoured with the 2025 Queen Elizabeth Prize for Engineering alongside six expert engineers who, like me, try to use facts about our brains to make more powerful computers," said Professor Hopfield, joint 2024 Nobel Prize winner in physics for her work enabling machine learning with neural networks.

“The facts about the interconnectivity of brain neuroanatomy become the engineer’s structure of artificial neural networks. The synapses which connect a brain cell to others become the adjustable parameters of the programmer. Learning by exploring the environment is replaced by using massive data sets." 

Now in its twelfth year, the QEPrize has honoured 26 engineers whose innovations have had a significant impact on billions of lives around the world. The 2025 Laureates will share the £500,000 prize.