Deep Neural Networks – part of the broader family of machine learning – have become increasingly powerful in everyday real-world applications such as automated face recognition systems and self-driving cars.
While DNNs have become an increasingly popular tool to model the computations that the brain does, particularly to visually recognise real-world “things”, the ways in which DNNs do this can be very different.and led by the University of Glasgow’s School of Psychology and Neuroscience, presents a new approach to understand whether the human brain and its DNN models recognise things in the same way, using similar steps of computations.
As a current challenge of accurate AI development is understanding whether the process of machine learning matches how humans process information, it is hoped this new work is another step forward in the creation of more accurate and reliable AI technology that will process information more like our brains do.
“If we have a greater understanding of the mechanisms of recognition in human brains, we can then transfer that knowledge to DNNs, which in turn will help improve the way DNNs are used in applications such as facial recognition, where there are currently not always accurate.