| EPSRC Reference: |
EP/S02266X/1 |
| Title: |
UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents (SOCIAL) |
| Principal Investigator: |
Vinciarelli, Professor A |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
School of Computing Science |
| Organisation: |
University of Glasgow |
| Scheme: |
Centre for Doctoral Training |
| Starts: |
01 July 2019 |
Ends: |
31 December 2027 |
Value (£): |
5,141,401
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| EPSRC Research Topic Classifications: |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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07 Nov 2018
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UKRI Centres for Doctoral Training AI Interview Panel T – November 2018
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Announced
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Summary on Grant Application Form |
Context
Social intelligence is an important aspect of human cognition making us capable of dealing with others' attitudes, intentions, feelings, personality, and expectations. Correspondingly, Artificial Social Intelligence is the area of Artificial Intelligence (AI) that aims at endowing machines with such social intelligence, i.e., with the ability to interact with their users in the same way as people interact with one another. While being driven by technological needs, Artificial Social Intelligence is an inherently interdisciplinary field that revolves around humans as much as it revolves around the building of machines.
As a result, the proposed Centre for Doctoral Training (CD) is based on the collaboration between different experts in human behaviour - students will be trained by specialists ranging in expertise from neural, physiological, cognitive and psychological processes to verbal/nonverbal societal communication - and experts in AI methodologies. They will gain expertise and skills that will range from the synthesis of human/societal interactive behaviour to the distillation of knowledge from sensors and data.
- Aims and objectives
The goal of the CDT is to train the next generation of experts in Artificial Social Intelligence, young researchers and practitioners well versed not only in AI, but also in a range of fields spanning from Psychology/Social Science and Neuroscience to Human-Computer Interaction and Data Science. These different disciplines will come together to train the cohort in:
a. Identifying principles and laws underlying social interactions between users and agents;
b. Developing technological approaches that allow artificial agents to act as believable partners in social interactions involving human users;
c. Integrating artificial agents into the wider technological infrastructures;
d. Investigating human responses to artificial agents in a naturalistic, real-world social settings.
Academic involvement will be in the form of the provision of courses across the topics listed above, advanced workshops and direct supervision in cutting edge research that is not necessarily (yet) part of the industrial workflow.
- Applications and Benefits
The proposed training approach will be in tight collaboration and co-creation with our industrial partners as the aim is to provide the students with the best of both the academic and industry worlds. Industrial involvement will be in the form of co-design and co-supervision of the PhD project as well as placements, usually over a period of 3 months. This will allow the students to co-create innovation through the PhD proposal and the development of specific, real-world industry problems. Such a tight interaction with industry will also be of advantage to the UK economy that will benefit from a pool of talent trained not only from a scientific and technological point of view, but also in terms of professional skills and experience necessary to operate in highly technological companies.
Students will further benefit from wider social sciences training through the proposed partnership with the Scottish Graduate School for Social Sciences (SGSSS), a ESRC funded Doctoral Training Partnership (DTP) with a track record for excellence in Teaching, Cohort training and Knowledge Exchange and Impact. The outlined training model will inform AI approaches with the findings on human behaviour and, vice versa, AI technologies will be used to better understand and model human behaviour.
Last, but not least, the emphasis on ethics and social issues is of great societal importance as AI-driven technologies play an increasingly important role in sensitive settings such as healthcare, assistance, education, law enforcement, etc.
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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.gla.ac.uk |