Brain-Inspired AI
Today on the show, we are joined by Lin Zhao and Lu Zhang. Lin is a Senior Research Scientist at United Imaging Intelligence, while Lu is a Ph.D. candidate at the Department of Computer Science and Engineering at the University of Texas. They both shared findings from their work When Brain-inspired AI Meets AGI.
Lin and Lu began by discussing the connections between the brain and neural networks. They mentioned the similarities as well as the differences. They also shared whether there is a possibility for solid advancements in neural networks to the point of AGI. They shared how understanding the brain more can help drive robust artificial intelligence systems.
Lin and Lu shared how the brain inspired popular machine learning algorithms like transformers. They also shared how AI models can learn alignment from the human brain. They juxtaposed the low energy usage of the brain compared to high-end computers and whether computers can become more energy efficient.
You can learn more about Lin and his work on LinkedIn. Check Lu’s website to learn more about her research.
Lin Zhao
Lin Zhao is currently a senior research scientist in United Imaging Intelligence. He received his Ph.D. degree in Computer Science from University of Georgia in 2023 under the supervision of Prof. Tianming Liu. He received his B.E. degree from Northwestern Polytechnical University in 2017. His current research interests include brain-inspired AI and medical image analysis.
Lu Zhang
Lu Zhang is a fifth-year PhD student at the University of Texas at Arlington in the Computer Science and Engineering department, under the supervision of Dr. Dajiang Zhu. She received her bachelor’s and master's degrees in computer science and technology from the Northwestern Polytechnical University in China. Her research topic mainly focuses on two major problems: using machine learning/deep learning methods to integrate multi-scale and multi-modal brain imaging data to better understand brain fundamental organization principles and brain disorders, such as Alzheimer’s disease and Autism; and applying neuroscience knowledge to design more effective and efficient deep neural networks (Brain Inspired AI). She has published more than fifteen papers on top-tier conferences and Journals in the brain imaging area and received the Best Paper Award at MMMI 2019 and the Prestigious Young Scientist Award at MICCAI 2020.
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