| EPSRC Reference: |
EP/Y030796/1 |
| Title: |
UKRI AI Centre for Doctoral Training in Practice-Oriented Artificial Intelligence (PrO-AI) |
| Principal Investigator: |
Flach, Professor P |
| Other Investigators: |
| Houghton, Dr C |
Ray, Dr O |
Charlesworth, Professor A |
| Santos-Rodriguez, Dr R |
Simpson, Dr E E |
Abdallah, Dr ZS |
| O'Hara, Professor K |
Marshall, Dr P |
Lewis, Dr MAF |
| Cussens, Dr J |
Ajmeri, Dr N |
Silva Filho, Dr T |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computer Science |
| Organisation: |
University of Bristol |
| Scheme: |
Centre for Doctoral Training |
| Starts: |
01 April 2024 |
Ends: |
30 September 2032 |
Value (£): |
9,631,125
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
| Aerospace, Defence and Marine |
Creative Industries |
| Pharmaceuticals and Biotechnology |
Energy |
| Information Technologies |
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Summary on Grant Application Form |
Our mission is to train the next generations of AI innovators, equipping them with the expertise and skills to co-design and build practice-oriented AI systems for science and research domains of key national importance. Our innovative, cohort-based training programme will deliver highly-trained PhD graduates with the transferable skills to deliver impact while solving societally important problems in responsible ways.
While the current, media-driven narrative focuses on the disruptive nature of selected AI technologies, research organisations working towards incorporating AI methods strongly prefer to do so in a way that maximally respects their well-established practices. The key AI challenges of tomorrow are posed by the need for a deeper cooperation and alignment between AI systems, AI practitioners, and domain experts, the latter being the source of both relevant case-specific knowledge and requirements with regards to functionality, robustness, transparency, accountability and fairness for a given application. In other words, we need AI experts with the ability to act as a bridge between domain experts and AI technology. But rather than delivering 'AI for x' experts for a fixed domain x -- which might quickly lose its importance or utility -- the PrO-AI doctoral training programme will deliver experts with the flexibility to adapt to new domains during the course of their career.
In close conversation with our industry partners, we thus identified Practice-Oriented Artificial Intelligence (PrO-AI) as a core sub-discipline of AI where the need for and deficit in skills is abundantly evident, while being homogeneous enough to have intellectual integrity and be explored and researched within the context of a single CDT. The most important aspects of the bespoke training programme are:
- Practice projects will be run in close collaboration with CDT partners (academic and industry) and across CDT cohorts to offer 'deep dives' into a research domain. Students will liaise with domain experts to get an understanding of their expertise, expectations and success criteria. Exploring different domains will allow the students to acquire the transferable skills that will be key when moving between domains in their future careers.
- The Foundations of Practice-Oriented AI module is run in a student-led, 'flipped classroom' manner and covers the main topics in data-driven AI, knowledge-intensive AI and human-AI interaction; human-centred and participatory methodologies to bring those AI techniques into practice; and the legal and ethical context that will help students to do so responsibly and in a manner beneficial to society.
- Academic mentors attend the sessions to gauge their mentee's knowledge and identify areas where further study will be beneficial as part of the Research Orientation module, which completes a student's foundational training in an individual-centred manner and guides them on the way to choosing a research topic for the Summer Project and subsequent PhD research.
- Throughout their programme the students participate in and build an online portfolio of transferable skills training inspired by the Vitae Researcher Development Framework. This includes training in responsible innovation, entrepreneurship, hosting visitors, organising events, public engagement, citizen science, etc. A 'serious game' approach will encourage students to build their Researcher Development portfolio, unlocking additional research and training support resources when they reach certain milestones.
In summary, the PrO-AI CDT pulls together the University of Bristol's proven track record in doctoral AI training and its unique and comprehensive strengths in AI applied to a range of science and research areas including health, environment and energy, and creative industries, to deliver highly-trained AI innovators with the transferable skills to deliver societal impact across domains.
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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.bris.ac.uk |