A challenge for AI research is to operate autonomously in natural environments. Many organisms process the world in milliseconds and react accordingly. Achieving such a rapid and complex response with AI is an ongoing challenge. One solution is to pursue research on Brain-Like Intelligence: biologically-inspired solutions that replicate natural processes, which address the computational demands of multiple sensory inputs and potential motor outputs.
Brain-Like AI research has typically focused on human intelligence as the end-goal. What if the focus was broadened to look at other evolved intelligences from the natural world? The pluralistic view that all species have evolved adaptive sensory and motor capabilities for different environments might better equip autonomous robots with the flexibility to use different computational approaches attuned to their environment.
My project aims to examine the evolution of sensory cognition by the phylogenetic modelling of behavioural data to provide a basis for biologically-inspired Brain-Like AI. The results will stimulate innovations in the diversity of artificial intelligences and increase the input and output modalities for AI agents and robotics.
Modelling the visual system of humans and other species.
Computer vision, audition, and ethical implications.
Biologically-inspired robotics and autonomous machines.
The use and regulation of AI and Robotics in the areas of health care and education.
BSc Biology and Psychology (Joint Honours) at the University of Stirling
MSc Psychological Research Methods, specialising in neuroscience, at Birkbeck, University of London
I worked as an RA at the University of Cambridge for almost two years: in the Department of Psychology, Faculty of Education, and then at the Cambridge Institute of Public Health. I also worked as an analyst in the areas of AI ethics, cybersecurity, and healthcare. Most recently, I was a project assistant for the software team at CMR Surgical.
Dr Michael Proulx
Prof Eamonn O’Neill
Dr Alexandra de Sousa