| EPSRC Reference: |
EP/W002965/1 |
| Title: |
Turing AI Fellowship: Advancing Modern Data-Driven Robust AI |
| Principal Investigator: |
Ghahramani, Professor Z |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Engineering |
| Organisation: |
University of Cambridge |
| Scheme: |
EPSRC Fellowship - NHFP |
| Starts: |
01 October 2021 |
Ends: |
30 September 2026 |
Value (£): |
2,623,132
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| Information Technologies |
Transport Systems and Vehicles |
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| Related Grants: |
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| Panel History: |
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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.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.cam.ac.uk |