My PhD project is on the explainability of human-machine dialogue through visualisation. Recently, research on dialogue systems has been shifting focus from a traditional task-oriented rule-based paradigm to the more scalable, but less explainable, data-driven, deep-learning paradigm. As deep learning becomes more and more disruptive, it becomes more and more important to require explainability from approaches to implementing dialogue systems.
AI is arguably the most exciting topic in computer science, with endless interesting questions. Doing a PhD in STAI was therefore a natural choice for me. My industry experience at Bank of America showed me that I loved research and the great people there (who themselves held PhD titles) also encouraged me to do a PhD. I chose the STAI CDT programme because it offered great opportunities, such as the opportunity to engage with industry, with other students in the various cohorts, and in transferable training.
Most importantly, STAI offered me the opportunity to take on a fascinating project. I chose my current project because natural language and especially human-machine dialogue pose some of the biggest challenges for AI and for us humans who develop and use AI systems. Visualisation is an approach that empowers humans and gives us the explanation and control that we require. It is therefore my way forward on our long path ahead towards talking machines that we can trust.
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Undergraduate Qualification: BSc (Hons) in Computer Science with a Year in Industry (specialising in Artificial Intelligence), King’s College London
Award: Alan Fairbourn Memorial Prize for Most Meritorious Final Year Project by King’s College London
