This technology is a brain-computer interface that monitors and utilizes users’ neurophysiological responses to improve training of artificial intelligence (AI) systems.
Advances in AI are enabling applications where autonomous systems have extended interactions with human users. However, these systems, such as those implemented in driverless cars, can behave in ways that cause discomfort to users. There remains an unmet need for ways to adapt AI behavior to human expectations and reactions in order to improve task performance.
This technology is a system that analyzes and uses the neurophysiological responses of human users as an input for improved deep reinforcement-based learning of AI systems. As users interact with the autonomous system, their physiological signals are continuously and non-invasively tracked using methods such as EEG, pupillometry, and heart rate monitoring. These inputs can be used to determine what types of automated behaviors are stressful or uncomfortable for users, subsequently informing the AI agent to respond in appropriate ways to improve the user’s experience. Furthermore, correlation with neurophysiological signals may help the AI solve problems of higher complexity by leveraging the input of human expertise.
This technology has been validated in a simulated autonomous car environment.
IR CU16192
Licensing Contact: Dovina Qu