Is embodiment necessary for consciousness?
Nathaniel Wright’s chapter, with Neil Lawrence and Nicky Clayton, appears in Perspectives on Machine Consciousness.
Machine learning systems for real-world challenges
About
ML@CL is creating safe and reliable machine learning systems that can be deployed to tackle real-world challenges.
Amidst growing excitement about AI, a gap has emerged between our aspirations for AI and our ability to deploy these technologies to tackle real-world challenges. ML@CL aims to bridge this gap through innovations in modelling, systems and software engineering for machine learning deployment; the application of AI for scientific discovery; and the development of policy frameworks for trustworthy and beneficial AI.
Read more
Research
Our work addresses the full pipeline of AI system development, from data acquisition, through model development to system deployment.
We work closely with practitioners, domain experts and policymakers to understand and respond to the real-world challenges associated with AI deployment. Our research spans the fundamentals of machine learning methods, statistical emulation and uncertainty quantification, real-time inference and decision-making, systems design, the application of AI in science and industry, data stewardship, and AI policymaking. Find out more about current projects on our Research pages.
Our Research
23 September 2026
10 September 2026
MHRA Commission publishes its recommendations
The National Commission into the Regulation of AI in Healthcare has published its report. It argues for regulation that is proportionate, lifecycle-based and system-wide.
3 September 2026
Reframing AI’s role in scientific discovery
A paper in RSS Data Science and Artificial Intelligence opens a call for papers on how AI changes scientific practice, and where the limits of that change lie.