AI innovators win Princess Royal Silver Medals for brain-inspired systems and autonomous driving

Alex Kendall: From Cambridge lab to the open road    

Wayve is one of the UK’s most valuable deep tech startups, backed by $1 billion in Series C funding—led by SoftBank with support from NVIDIA and Microsoft. Alex Kendall co-founded the company in 2017 following his PhD at the University of Cambridge, where he pioneered a contrarian approach to self-driving cars.    

At a time when the industry relied heavily on rule-based systems, maps and multiple sensors, he proposed a different vision powered by deep learning—where a single neural network could learn to drive from raw data without human intervention.   

Wayve’s approach, known as AV2.0, leverages end-to-end AI to create a general-purpose driving intelligence that can adapt to new environments.  Its models are trained on tens of petabytes of real-world data from its team of safety drivers. Wayve tests its models in both real-world driving setting and in simulation. Real-world testing exposes AI to diverse conditions, while simulation enables efficient, large-scale validation.   

Synthetic data on rare or unseen scenarios are used to train their technology to safely navigate the real world.  Wayve tests these safety-critical scenarios, such as near collisions or unpredictable pedestrian behaviour, using GAIA-2, a cutting-edge generative world model.  

Wayve’s autonomous cars have been navigating the complex streets of London since 2019, overseen by legally required safety drivers. Last year they expanded to San Francisco and have also been testing these cars in Stuttgart, and Japan. The company plans to license its technology to car manufacturers, with Nissan set to integrate Wayve’s AI to support driver assistance into its vehicles by 2027.   

Learning the LINGO   

The engineering team have also built LINGO, the first language-driving model tested on public roads. LINGO opens up communication with the robot and can narrate its driving and answer questions. That means Wayve’s engineers (and eventually passengers) can communicate with  the AI and ask it to explain decisions or drive in a certain way.    

“Ultimately, I think the future of robotics is that we're going to want intelligent machines we can delegate tasks to, and to be able to talk to them, to actually instruct them to behave in a way that is safe and matches our preferences.” says Kendall.   

He sees autonomous driving as a launchpad for a broader revolution in embodied AI, with applications in robotics, manufacturing, and healthcare. “Bringing AI into the physical world in a way that it can interact with us, is real – is tangible,” explains Kendall, “I think it’s going to be the biggest transformation we go through in our lifetimes.”    

 Themis Prodromakis: Brain-inspired sustainable AI   

Professor Prodromakis is internationally recognised for his work on memristors—short for memory resistors. They are brain-mimicking devices that combine memory and processing in a single component. This is radically different to the way conventional computer chips work, which store data on a separate unit.    

“What conventional computer chips typically do is go and fetch the data from memory, bring it into the processing unit, do some number crunching, and then take the outcome of that and go back and store it in memory,” explains Professor Prodromakis. This process consumes more energy and slows computation.    

Memristors process information in a way that’s much closer to how the human brain works—efficiently, in parallel, and with minimal energy. Global electricity demand from data centres is projected to double by 2030 so this system offers a promising solution and could revolutionise the energy efficiency of artificial intelligence.   

Meeting the ‘father of memristors’   

Professor Prodromakis has been working on this technology since meeting Leon Chua on a visiting professorship at Berkeley in 2010. Chua is widely acknowledged as the ‘father of memristors’ and began his work on a theoretical level back in 1971. When Themis returned to the UK, he began making and testing his early memristors in his lab at Imperial College London.    

His work has already had global impact. The testing tools developed by his spinout company, ArC Instruments, are now used in over 300 labs across 26 countries. These tools have accelerated the development of next-generation semiconductor technologies, helping to bring memristors closer to commercial adoption.   

 Professor Prodromakis’ memristors are also compatible with existing CMOS manufacturing processes, making them more viable for integration into current chip production lines. This compatibility has attracted interest from major semiconductor foundries, including TSMC and ST Microelectronics.   

Future applications and ventures   

Beyond data centres, the potential applications of memristors are vast. Their low power consumption and resilience to radiation make them ideal for use in space, while their analogue nature could enable more precise brain-computer interfaces. In a recent breakthrough, Professor Prodromakis’s team successfully connected artificial neurons with living brain cells in rats, paving the way for more advanced neural implants.   

Looking ahead, Professor Prodromakis is launching the Edinburgh Venture Builder in AI hardware (EVA), a new initiative inspired by the legendary Bell Labs, to support the commercialisation of AI hardware innovations. “This has never been done for electronics and specifically AI hardware” he says, “so that’s why I’m quite excited about this new phase.”   

Notes for Editors