UKRI Centre for Doctoral Training in Interactive Artificial Intelligence

Lead Research Organisation: University of Bristol
Department Name: Computer Science

Abstract

Our mission is to train the next generations of innovators in responsible, data-driven and knowledge-intensive human-in-the-loop AI systems. Our innovative, cohort-based training programme will deliver cohorts of highly trained PhD graduates with the skills to design and implement complex interactive AI pipelines solving societally important problems in responsible ways.

While fully autonomous artificial intelligence dominates today's headlines in the form of self-driving cars and human-level game play, the key AI challenges of tomorrow are posed by the need for interactive knowledge-intensive systems in which the human plays an essential role, be it as an end-user providing relevant case-specific knowledge or interrogating the system, an operator requiring crucial information to be presented in an intelligible form, a supervisor requiring confirmation that the system's performance remains within acceptable limits, or a regulator assessing to what extent the system operates according to exacting standards concerning transparency, accountability and fairness.
Each of these examples demonstrates a need for specific and meaningful interaction between the AI system and human(s). The examples also demonstrate the importance of knowledge for achieving human-level interaction, in addition to the data driving the machine learning aspect of the system.

In close conversation with our industry partners we thus identified Interactive Artificial Intelligence (IAI) as a core sub-discipline of AI where the need for and deficit in advanced AI skills is abundantly evident while being homogeneous enough to have intellectual integrity and be taught and researched within the context of a single CDT. The most important aspects of the training programme are:
- Knowledge-Driven AI and Data-Driven AI are core components treated in a close symbiotic relationship: the former uses knowledge in processes such as reasoning, argumentation and dialogue, but in such a way that data is treated as a first-class citizen; the latter starts from data but emphasises knowledge-intensive forms of machine learning such as relational learning which take knowledge as an additional input.
- Human-AI Interaction is another core component addressing all human-in-the-loop aspects, overseen by a co-investigator from the human-computer interaction field.
- Responsible AI is underpinning not just the taught first year but the students' doctoral training throughout all four years, overseen by two dedicated co-investigators with backgrounds in IT law and industrial codes of practice.

Other skill requirements from stakeholders include: the ability to design and implement complete end-to-end systems; acquiring depth in some AI-related subjects without sacrificing breadth; the ability to work in teams of people with diverse skill sets; and being able to take on a role as "AI ambassadors" who are able to inspire but also to manage expectations through their in-depth understanding of the strengths and weaknesses of different AI techniques.

The IAI training programme is designed to achieve this by strongly emphasising cohort-based training. Students will develop their projects and coursework within an innovative software environment which means easy integration of their work with that of others. This virtual hub is complemented by a physical hub where all cohorts are colocated -- together both hubs will strongly promote interaction both within and between cohorts: e.g., projects can aim at improving or extending software produced by the previous cohort, so that senior students can be involved in mentoring their juniors.

In summary, the IAI training programme pulls together Bristol's unique and comprehensive strengths in doctoral training and AI to deliver highly trained AI innovators, equipping them with essential skills to deliver the interactive AI technology society requires to deal with current and future challenges.

Planned Impact

The IAI CDT training programme has been co-designed for maximum impact with our co-creation partners (more than 30, of which 14 attended one or both co-creation workshops and 23 provided letters of support with in-kind and cash contributions totalling nearly 3.5M pounds) -- all invested stakeholders who will have a direct interest in co-creating impact and delivering value within the CDT. The IAI training programme will deliver significant impact for students, employers, AI solution providers, the research community and society at large.

Students will benefit from this CDT, as it provides them with the advanced skills needed to be innovation leaders in a rapidly expanding and societally important field.

Employers and the UK economy will benefit hugely from a supply of highly-skilled yet broadly educated early-career researchers. These are in very short supply, making it difficult to build the capacity needed to meet demand in this rapidly growing sector. Potential employers in the private sector range from multinationals to SMEs in the technology domain (including our current partners, with many more being added during the lifetime of the Centre).

The broad and multidisciplinary training of the students will particularly benefit the product, system and service development cycle for those and similar companies. For example, the human-in-the-loop design ethos permeating the training programme will facilitate user acceptance and trust, and the students' knowledge regarding the ethical and legal context will mitigate risks in the regulatory pathway.

Knowledge transfer will be encouraged, and students will receive training in generation of intellectual property, product licensing and entrepreneurial skills. We also emphasise the value of public engagement activities to increase public understanding of AI technology and the impact it may have on people's life and wellbeing. Here we build on our well-established working relationship with outreach partners such as Knowle West Media Centre and We The Curious.

Bristol has significant infrastructure to support new companies, including partnerships with the award-winning SETsquared business acceleration centre, and the UnitDX incubator, which currently houses 12 University of Bristol spin-outs and 17 companies started by University alumni. Bristol's Computer Science Department has a long history of successful spin-out and start-up companies, including our CDT partner XMOS and Ultrahaptics, which has attracted more than 30M pounds in funding since spinning out of the Bristol Interaction Group in 2013, so informal enterprise networks and social capital are available to students.

Doctoral training involves a significant research project leading to a PhD. The AI research community will benefit from this research through publications in top-tier conferences and journals. Other research outputs such as open source software, data sets, online notebooks etc. will also be of benefit to the community. Industrial partners who suggested a research topic and/or co-supervised a PhD student will benefit more directly from the research carried out by the students and its outputs.

The CDT will be an exemplar for technology-supported cohort-based training. The IAI-Hub, an innovative integrated software environment supporting the development and deployment of Interactive AI pipelines and workflows, will be developed in open-source and adhere to common standards and efforts will be spent on sharing best practice with the community.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022937/1 01/04/2019 30/09/2027
2276774 Studentship EP/S022937/1 23/09/2019 22/09/2023 Tayfun Karaderi
2276905 Studentship EP/S022937/1 23/09/2019 22/09/2023 Stefan Radic Webster
2276915 Studentship EP/S022937/1 23/09/2019 22/09/2023 Grant Stevens
2276298 Studentship EP/S022937/1 23/09/2019 22/09/2023 Stoil Ganev
2276896 Studentship EP/S022937/1 23/09/2019 22/09/2023 Kipp McAdam Freud
2276920 Studentship EP/S022937/1 23/09/2019 22/09/2023 Daniel Whettam
2276812 Studentship EP/S022937/1 23/09/2019 22/09/2023 Gavin Leech
2258687 Studentship EP/S022937/1 23/09/2019 22/09/2023 David Ireland
2276910 Studentship EP/S022937/1 23/09/2019 22/09/2023 Philipp Schmitz
2276918 Studentship EP/S022937/1 23/09/2019 22/09/2023 Emily Vosper