Old brain cells perform new tricks to allow life-long learning. In the brain, nerve cells transmit electrical signals more quickly and reliably when they are insulated. The insulating cells undergo small adaptive changes that speed up information transfer during learning, and the faster the electrical signal, the better the learning outcomes. This project aims to understand the signals that direct insulating cells to adapt and support life-long learning. In the longer term, this knowledge may be ....Old brain cells perform new tricks to allow life-long learning. In the brain, nerve cells transmit electrical signals more quickly and reliably when they are insulated. The insulating cells undergo small adaptive changes that speed up information transfer during learning, and the faster the electrical signal, the better the learning outcomes. This project aims to understand the signals that direct insulating cells to adapt and support life-long learning. In the longer term, this knowledge may be used to: develop interventions that improve learning and educational outcomes; counteract age-related memory decline and enable longer work force participation; develop strategies to circumvent the memory loss caused by brain diseases, or improve the design of computer hardware.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultu ....Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultural issues. This project is expected to benefit industries with widely distributed multi-cultural workforces such as mining, defence and medicine.Read moreRead less