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Details of Grant 

EPSRC Reference: EP/W002965/1
Title: Turing AI Fellowship: Advancing Modern Data-Driven Robust AI
Principal Investigator: Ghahramani, Professor Z
Other Investigators:
Huszar, Dr F
Researcher Co-Investigators:
Project Partners:
Graphcore Invenia Labs Tractable Ltd
Wayve Technologies Ltd.
Department: Engineering
Organisation: University of Cambridge
Scheme: EPSRC Fellowship - NHFP
Starts: 01 October 2021 Ends: 30 September 2026 Value (£): 2,623,132
EPSRC Research Topic Classifications:
Artificial Intelligence Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Transport Systems and Vehicles Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 May 2021 Turing AI World-Leading Researcher Fellowship Interview Panel Announced
Summary on Grant Application Form
Modern Artificial Intelligence is dominated by methods that learn from large amounts of data. These machine learning methods underpin many current technologies such as voice recognition, face recognition, product recommendation, social media news feeds, online advertising, and autonomous vehicles. They are also the basis of recent breakthroughs in AI like the game-playing systems that can beat humans at chess, Go, and poker. Machine learning also underlies many practical advances in science, engineering and medicine, such as automated tools for analysing genomic data and medical images.

These advances in machine learning have come about through the use of large complex deep learning models, open-source software, very large data sets, new computer hardware, and distributed computation. Despite the spectacular successes, industry investment and media attention, many limitations and therefore opportunities for research remain.

The limitations of current AI systems include a poor handling of noise, uncertainty and changing circumstances, gaps in the ability to combine symbolic and statistical reasoning, and the lack of automation of many of the stages of learning.

This project will advance modern data-driven AI methods by developing a number of new algorithms and applications to address these limitations. The work will bring together symbolic and statistical methods through new scalable deep probabilistic approaches. These approaches will generalise better to novel data, and "know when they don't know". The project will also develop better tools for automating the process of building and maintaining a machine learning system. We will also bring approaches from data-driven machine learning to the use of simulators, which are widely used to model and understand complex systems in science and engineering. Finally, we will apply the algorithms and software tools developed in this proposal to challenging problems in modelling and optimising complex systems with many interdependent components, in particular in the areas of electrical grid efficiency and transportation systems.

Key Findings
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Potential use in non-academic contexts
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Summary
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Organisation Website: http://www.cam.ac.uk