Artificial intelligence technologies

Artificial Intelligence (AI) technologies aim to reproduce or surpass abilities (in computational systems) that would require 'intelligence' if humans were to perform them. These include: learning and adaptation; sensory understanding and interaction; reasoning and planning; optimisation of procedures and parameters; autonomy; creativity; and extracting knowledge and predictions from large, diverse digital data. The applications of AI systems are very diverse, ranging from understanding healthcare data to autonomous and adaptive robotic systems, to smart supply chains, video game design and content creation.

We aim to maintain this area as a proportion of the EPSRC portfolio. This strategy recognises AI research's importance to data science and Robotics and Autonomous Systems (RAS), while acknowledging that this area is already very large. It aims to show where activity can be focused to allow the UK to maintain international expertise in fundamental theory and more applied research.

By the end of the current Delivery Plan, we aim to have:

  • A portfolio of AI research and training in data science that complements work undertaken at the Alan Turing Institute, with links to underpinning statistical and theoretical sciences. Academia has an opportunity to make significant advances by addressing gaps in current knowledge, starting to tackle the challenges of making AI more robust, resilient and transferable
  • A supply of people with high-level skills in AI technologies, reflecting growing demand, and who can contribute expertise across a wide range of domains (e.g. the future of healthcare delivery)
  • Researchers combining development of new methodology and applications (e.g. by working alongside research enablers such as research engineers, translational researchers and collaborators with application expertise)
  • A portfolio that contains AI-enabled RAS technologies co-created with other disciplines (e.g robotics, human-computer interaction, computer vision and the social sciences). This should take into account how these intelligent systems interact and collaborate with humans, and consider their validation and verification, especially in application areas where the dependability, safety or security of implementations is a concern

AI researchers will play a key role in furthering EPSRC's Future Intelligent Technologies and Data Enabling Decision Making cross-ICT priorities, and are well-placed to contribute to the other cross-ICT priorities. In order to maximise the impact of these contributions, they should ensure effective communication with researchers in other contributing areas such as natural language processing, visualisation and HCI.

We recognise the need for researchers to work with large-scale data and we encourage them to develop collaborations with users to facilitate this. We also encourage them to explore alternative routes to access sufficient computational resources (e.g. use of commercial clouds). However, UK academia should not try to imitate industry, and should focus on AI opportunities not yet identified by industry or not yet commercially viable.

If the general public are to accept and trust systems based on AI technologies, researchers should acknowledge and demonstrate the importance of both Responsible Innovation and Public Engagement in their proposals. (Evidence source 1) Areas such as RAS and computer games also offer the prospect for AI researchers to inspire the next generation of computer scientists.

Highlights:

The UK is a strong international contributor in this area, evidenced by the existence of world-leading UK research groups. There is a substantial level of high quality AI research in the UK. However, due in part to the volume of activity, there is a slightly wider range of quality in the broader AI landscape than in some other areas of ICT. (Evidence source 2,3)

This is a significant research area for data science and RAS. (Evidence source 1,4) We recently invested £42 million in the Alan Turing Institute as part of a joint data science venture with five university partners. RAS technologies will have an increasing impact on the UK economy, improving business competitiveness, providing effective solutions to societal problems and giving individuals greater freedom and choice. (Evidence source 1,5)

The importance of - and UK international competitiveness in - machine learning (ML) is evidenced by the significant investment being made in UK ML, including Google's acquisition of start-up DeepMind in 2014. Large companies, not normally associated with AI, are increasingly investing in data science; online data is readily available now and these companies are beginning to to tap into this. (Evidence source 3) ML has become an enabler of many technologies (e.g. language technologies) and is expected to play an increasingly important role in this respect. (Evidence source 6,7)

Many UK universities are actively investing in data science/ML and are attracting international experts. Nevertheless, we need to ensure the UK has the trained people to cater for the serious upward trend in demand for ML skills. Recruitment at PhD level is healthy (with keen demand from students and employers), but recruitment/retention in academia beyond this is a problem. There is the threat of key capacity being lost to industry, with universities unable to compete (e.g. in terms of salary, provision of computational resource and access to large scale data). (Evidence source 3,8,9) Academia has the opportunity to complement, rather than try to replicate, industry's research interests. Valence is a particular challenge to collaboration, with expertise in ML techniques in demand across multiple applications. The mechanisms for delivering future investment in computational infrastructure for data science also need careful consideration (e.g. by exploring the possibility of using commercial clouds), as computational resources may quickly become outdated. (Evidence source 3)

The applications of AI systems are very diverse. The UK is in a particularly strong position internationally to develop AI technologies in healthcare, partly due to having National Health Service (NHS) data stakeholders interacting with universities and partly because the UK has one of the world's best-established AI communities, able to offer diversity of research. This makes AI an area of national importance for the future of healthcare delivery. (Evidence source 3)

This area is linked with many research areas and Themes across EPSRC. Areas of highest current relevance are: Biological Informatics, Human-Computer Interaction, ICT Networks, Image and Vision Computing, Information Systems, Natural Language Processing, Operational Research, Pervasive and Ubiquitous Computing, Robotics, Software Engineering, Statistics and Applied Probability, and Verification and Correctness.

AI will most strongly contribute to Connected, Healthy and Resilient Nation Outcomes over a shorter timeframe. Contributions to some of the Ambitions within the Productive Nation are expected to be at a lower level and/or to take place over a longer timeframe. Particularly relevant Ambitions include:

C1: Enable a competitive, data-driven economy

AI is expected to contribute to advances in data science that will deliver the smart tools and analytical techniques required to generate actionable information from large and diverse datasets.

C2: Achieve transformational development and use of the Internet of Things

AI is expected to contribute to the way information can be intelligently assimilated and utilised across a range of business sectors and services.

C3: Deliver intelligent technologies and systems

AI will allow smart tools and intelligent technologies to take the Connected Nation beyond flows of data and turn it into physical action. Multidisciplinary research involving social scientists will enable such tools to be acceptable, usable and ethical.

H3: Optimise diagnosis and treatment

AI can contribute to the development of sophisticated, personalised models that will enable individuals and clinicians to plan treatment holistically, based on a range of possible, and increasingly accurate, predicted outcomes.

P4: Drive business innovation through digital transformation

AI could contribute to the development of autonomous and intelligent technologies that can transform business models and services. 

  1. Commons Select Committee, Robotics and Artificial Intelligence Inquiry - Written and Oral Submissions, (2016).
  2. EPSRC, Analysis of Research Excellence Framework (REF) 2014 data and EPSRC Knowledge Maps, (2014).
  3. Community and user engagement (individual input and group feedback).
  4. EPSRC, Future Intelligent Technologies (FIT) Workshop, (2015).
  5. Robotics and Autonomous Systems Special Interest Group (RAS SIG), Strategy and Landscape Documents, (2016).
  6. The Royal Society, Machine Learning Conference Report (PDF) and ongoing policy project, (2015).
  7. Nesta, Machines that Learn in the Wild (PDF), (2015).
  8. IT Jobs Watch, Tracking the IT Job Market, (2016).
  9. EPSRC, Output from the EPSRC Speech Technologies exceptions process (2015).

Research area connections

This diagram shows the top 10 connections between Research Areas within the EPSRC research portfolio. The depth of the segment relates to value of grants and the width of the segment relates to the number of grants shared by those two Research Areas. Please click to see the related Research Area rationale.

Maintain

We aim to maintain this area as a proportion of the EPSRC portfolio.

Visualising our Portfolio (VoP)
Visualising our portfolio (VoP) is a tool for users to visually interact with the EPSRC portfolio and data relationships.

EPSRC support by research area in Artificial intelligence technologies (GoW)
Search EPSRC's research and training grants.

Contact Details

In the following table, contact information relevant to the page. The first column is for visual reference only. Data is in the right column.

Name: Nelly Wung
Job title: Portfolio Manager
Department: ICT
Organisation: EPSRC
Telephone: 01793 444193