This research area focuses on the reproduction or surpassing of abilities (in computational systems) that would require intelligence if humans were to perform them.
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
- search and optimisation
The applications of AI systems, including but not limited to machine learning, are diverse, ranging from understanding healthcare data to autonomous and adaptive robotic systems, to smart supply chains, video game design and content creation.
This research area primarily covers fundamental advances in AI technologies, while applications of such technologies are captured within other subject domains.
We aim to maximise opportunities arising from the current increased global interest in AI and its widespread applications, as well as the government’s Industrial Strategy, namely via the National Productivity Investment Fund (NPIF).
This strategy recognises that AI and the data economy was named as one of four Grand Challenges in the Industrial Strategy, as well as AI research’s importance to data science in general and robotics and autonomous systems (RAS) specifically. It aims to show where activity can be focused to allow the UK to grow in international expertise in both fundamental theory and more applied research.
A strong AI community
We aim to have a community which actively engages in:
- the challenges of responsible research and innovation
- public acceptability of AI
- ensuring that research outcomes are socially beneficial, ethical, trusted and deployable in real world situations.
We expect a supply of people with skills across the breadth of AI technologies, reflecting growing demand, and who can contribute expertise across a wide range of domains (for example, the future of healthcare delivery).
A combination of new methodology and applications
Researchers will aim to combine development of new methodology and applications – for example, by working alongside research enablers such as research engineers, translational researchers and industry collaborators with application expertise.
A strong portfolio
The portfolio aims to contain AI-enabled robotics and autonomous systems technologies co-created with other disciplines, such as:
- human-computer interaction
- computer vision
- 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 human-computer interaction.
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 – for example, 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, particularly those leading to beneficial societal impacts.
AI is likely to become an increasingly dominant feature of our world and understanding the future of AI and its impact on future society are critically important research areas.
Explainability of AI decisions is key, as is the use of AI to simplify data in order to facilitate human understanding and decision making.
The recent growth in this research area, namely on the data intensive sub-symbolic side of the AI technologies portfolio has implications for information and communications technologies (ICT) hardware related research areas. We recognise that research in ICT hardware should keep up with the advances made in AI technologies and that better links between these communities need to be encouraged.