Understanding and developing continuous learning power of AI in natural conversation generation

This PhD research project forms an integral part of the Intelligent Virtual Personal Assistant (Intel-PA) project, which is being undertaken at the National Centre for Computer Animation at Bournemouth University. The Intel-PA consists of three key functional components with the ability to: detect the motions and emotions of the human user; perform sensible conversations; and enact appropriate body gestures and facial expressions. By performing such human - like functions, the Intel-PA will be able to play a huge role in many applications, such as healthcare, training, education, marketing and decision making.
The aims of this PhD research project are to set a corner stone to bring in-depth understanding about the mechanism of continuous learning in AI and to investigate natural language generation with additional visual cues. Rich multimodal inputs of audio signals and visual cues are combined to provide complementary information for context understanding. Virtual agents will be simulated to mimic real humans. This project will therefore pursue two mutually supportive paths of research and development: (1) advancing the theoretic frontiers of continuous learning in cognitive and audio/visual processing terms; and (2) developing and producing practical applications.

Applications are made via Bournemouth University’s website by clicking ’institution website’ button. If you have an enquiry about this project please contact us via the ’Email institution’ button, however your application will only be processed once you have submitted an application form as opposed to emailing your CV to us.

Candidates for a PhD Studentship should demonstrate outstanding qualities and be motivated to complete a PhD in 4 years and must demonstrate:

• Outstanding academic potential as measured normally by either a 1st class honours degree (or equivalent Grade Point Average (GPA) or a Master’s degree with distinction or equivalent
• An IELTS (Academic) score of 6.5 minimum (with a minimum 6.0 in each component, or equivalent) for candidates for whom English is not their first language and this must be evidenced at point of application.

An ideal candidate should have a background in computer science, mathematics, engineering or a similar subject. Strong mathematical and computer programming skills are essential.