Researchers from the University of Glasgow's School of Psychology and Neuroscience have developed a novel approach to understanding whether the human brain and its DNN models recognize things in the same way Deep Neural Networks have become very significant in everyday real-world applications such as automated face recognition systems and self-driving cars. Deep Neural Network is used by researchers to model the processing of information and examine how this processing is equivalent to that of humans. While how DNNs perform computations can be very different from the human brain. Hence, researchers have invented a unique approach to understanding whether the human brain and its DNN models recognize things in the same way, using similar steps of computations. Prof Philippe Schyns, Dean of Research Technology at the University of Glasgow, said: "Having a better understanding of whether the human brain and its DNN models recognize things the same way would allow for more accurate real-world applications using DNNs. This article defines a new approach to better this understanding of how the process works: first, researchers must show that both the brain and the DNNs recognize the same things – such as a face – using the same face features; and, secondly, that the brain and the DNN must process these features in the same way, with the same steps of computations. This research would overcome the main hurdle in AI development i.e. understanding the process of machine learning, which matches how humans process information. "Creating human-like AI is about more than mimicking human behavior – technology must also be able to process information, or'think', like or better than humans if it is to be fully relied upon.

Researchers use deep neural networks, or DNNs, to model the processing of information, and to investigate how this information processing matches that of humans. While DNNs have become an increasingly popular tool to model the computations that the brain does, particularly to visually recognize real-world "things," the ways in which DNNs do this can be very different. New research, published in the journal Trends in Cognitive Sciences and led by the University of Glasgow's School of Psychology and Neuroscience, presents a new approach to understanding whether the human brain and its DNN models recognize things in the same way, using similar steps of computations. Currently, deep neural network technology is used in applications such as face recognition, and while it is successful in these areas, scientists still do not fully understand how these networks process information. This opinion article outlines a new approach to better this understanding of how the process works: first, that researchers must show that both the brain and the DNNs recognize the same things--such as a face--using the same face features; and, secondly, that the brain and the DNN must process these features in the same way, with the same steps of computations.

From the technology that helps self-driving cars recognize stop signs, to medical advancements that help produce COVID-19 vaccines, to studying the unconscious bias found in algorithms, the Luddy School of Informatics, Computing and Engineering is involved in all parts of AI development. As artificial intelligence continues to infiltrate everyday life, IU's researchers are focused on developing these technologies, while working to ensure their research is safe and ethical. The Luddy Center for Artificial Intelligence is set to open this month, providing researchers a place to focus on the intersection of robotics, complex networks, health and social media. Kay Connelly, Luddy School's associate dean of research, studies proactive health and AI technologies that can help the terminally ill and older people as they age, specifically wearable devices. She said proactive health is like "Fitbit before Fitbit."