The prestigious prize was awarded to Yoshua Bengio, Geoffrey Hinton, John Hopfield, Yann LeCun, Jensen Huang, Bill Dally and Fei-Fei Li at the Science Museum in London last night (4 February).
“The collective efforts of these innovators have been pivotal in advancing the three core pillars of modern machine learning: advanced algorithms, high-performance hardware and high-quality datasets,” a QEPrize announcement said.
“It is the combination of these interrelated breakthroughs that underpins the widespread adoption and application of AI systems. The seamless integration of these contributions has enabled the development of powerful AI systems that are revolutionising industries, transforming daily life, and reshaping how we live and work – all made possible by the pioneering vision of this year’s laureates.”
ChatGPT is perhaps the most well-known example of a technology that relies on machine learning. Already used by hundreds of millions of people just over two years since its launch, it has upended conventional ways of researching and generating information, changing the way we interact with computers and streamlining many processes.
The technique – which enables systems to learn from data, recognise patterns and make predictions without explicit programming – has also revolutionised countless other fields by allowing models to self-improve with new data, including enabling self-driving cars and revolutionising healthcare diagnostics.
Mechanical engineers regularly use technologies that rely on machine learning for processes including design optimisation, predictive maintenance and developing control systems for robots.
The pioneers’ contributions highlight “the transformative potential of AI in shaping a better future,” the announcement said. “The 2025 QEPrize honours their ingenuity and vision, recognising their profound impact on engineering and society as a whole.”
Nobel prize winners Geoffrey Hinton and John Hopfield, and fellow researchers Yoshua Bengio 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 the approach, enabling machines to process and learn from vast amounts of data in ways that were previously impossible.
Nvidia CEO 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 its subsequent architectural advances have been “central” to scaling machine-learning algorithms, the announcement said, making them powerful enough to support today’s AI applications.
“Engineers are builders of the future. And engineering is the art and science of turning imagination into reality, solving challenges once thought impossible, and uplifting the human condition,” said Huang. “I am deeply honoured to receive the Queen Elizabeth Prize for Engineering and to be recognised among the pioneers whose work has shaped the world we live in today.”
Dally added: “Decades of research on parallel computing and stream processing paved the way for the development of the GPUs that enabled existing algorithms and datasets to achieve super-human results on many applications.
“Over the last decade, advances in GPU performance, efficiency and networking have powered today’s language and diffusion models, driving innovations that enhance the human experience in many ways. We continue to apply engineering methods to refine AI hardware and software so that AI can empower people to achieve even greater things.”
Fei-Fei Li recognised the critical 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,” the announcement said.
Dr Li said: “My hope is that this recognition will drive further awareness of the need to continue to keep human values at the centre of AI’s development among engineers, academic researchers, technologists, policymakers and civil society leaders alike to ensure the best future for our children, our parents, for all of us.
“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.”
The 2025 laureates, who will share a £500,000 prize, were introduced by Lord Patrick Vallance, chairman of the QEPrize Foundation, during a reception attended by Princess Anne, a royal fellow of the Royal Academy of Engineering.
Lord Vallance said: “This year, we celebrate the remarkable achievements that these seven engineers have contributed to modern machine learning, a field that has revolutionised artificial intelligence by uniting algorithms, hardware and data. The impact of this innovation is felt across industries, economies and the planet, showcasing the profound role engineering plays in shaping our future.
“Their work exemplifies the power of collaboration and stands as an inspiration to engineers everywhere. We celebrate their extraordinary contributions and their well-earned recognition as Queen Elizabeth Prize for Engineering laureates.”
Judging panel member Dr Abdigani Diriye added: “This particular innovation has probably been one of the most fascinating and exciting breakthroughs over the last 20-30 years.
“Two aspects really resonate with me the most. First, its profound impact on humanity – playing a crucial role in areas such as fraud prevention, improving healthcare and enabling emerging economies to leapfrog limitations by leveraging data. Second, its ability to inspire the next generation of engineers, as this is an incredibly exciting and transformative science. The nominees themselves are also remarkable individuals, truly driving and powering this new era of machine learning.”
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