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.
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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
29 September 2026
29 September 2026
Liverpool City Region AI Task Force
Jessica Montgomery has joined the new Liverpool City Region AI Task Force, an expert advisory group established to help shape the development and adoption of AI across the region.
23 September 2026
Is embodiment necessary for consciousness?
Nathaniel Wright’s chapter, with Neil Lawrence and Nicky Clayton, appears in Perspectives on Machine Consciousness.
29 September 2026
Fourth UK AI Conference in Nottingham
The UK AI Conference returns on 29–30 September 2026 at the Hilton Nottingham, with support from the Somabotics Turing AI Fellowship. Neil chairs; Christian Cabrera serves on the programme committee.
6 October 2026
AI for Local Government Show and Tell (Autumn 2026 Edition)
The AI for Local Government Show and Tell brings together local government officials working with AI to share experience from live projects, pilots, and implementation. The workshop focuses on what is being learned through practice: where AI is producing value, where projects encounter organisational or technical barriers, how professional roles and human judgement are affected, and what good evaluation and governance look like as adoption matures.