Jia Liu Ph.D.
Tsinghua University Chair Professor of Sciences, Chief Scientist of Beijing Academy of Artificial Intelligence
In recent years, fascinating progresses have been made in utilizing artificial intelligence to solve a broad range of problems. AI systems today can match and even outperform human performance in certain challenging tasks. Many recent AI advances have been largely inspired by neuroscience research into biological brain, guided by architectural and algorithmic constrains from biological neural networks. However, artificial neural networks remain to be “black boxes,” where the internal representations and computations of network components are poorly understood.
In my lab, we advocate the potential for cognitive neuroscience to further benefit AI. Specifically, research techniques and approaches available in cognitive neuroscience, including single-unit recording, neuroimaging, cognitive, and lesion techniques, can serve as a repertoire of tools for unveiling the black boxes of AI, illuminating the computations and representations inside AI networks. Further, research findings from cognitive neuroscience can provide inspirations to develop next generation of AI, by build-in a priori architectures, algorithms, and knowledge. The purpose of our research is to bring together research efforts from AI and cognitive neuroscience, seeking to integrate AI and cognitive neuroscience toward a new field: AI of Brain and Cognition (ABC).
Degrees
Department of Brain and Cognitive Sciences, MIT, PhD, 2003
Department of Psychology, Peking University, B.S. & M.S., 1995/1997
Appointments
Department of Psychology & Tsinghua Laboratory of Brain and Intelligence,Tsinghua University,Professor,2020- Now
National Key Laboratory of Cognitive Neuroscience and Learning/School of Psychology/Faculty of Psychology,Beijing Normal University,Professor,2006–2020
Department of Brain & Cognitive Sciences,Massachusetts Institute of Technology,Fulbright Visiting Scholar,2009–2010
National Key Laboratory of Brain & Cognitive Sciences,Institute of Biophysics,Chinese Academy of Sciences,Associate Professor/Professor,2003–2006
Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology,Postdoc,2002–2003
Recent publications & preprints (selected)
l Neuroscience
1. H Ma, L Jiang, T Liu, & J Liu (2023) From Sensory to Perceptual Manifolds: The Twist of Neural Geometry. bioRxiv, 2023.10.02.559721, https://doi.org/10.1101/2023.10.02.559721
2. B Zhang, L Ma, & J Liu (2023) Experience replay facilitates the formation of the hexagonal pattern of grid cells. bioRxiv, 2023.02.19.529130. https://doi.org/10.1101/2023.02.19.529130
3. X Liu, X Fei, & J Liu (2023) The Cognitive Critical Brain: Modulation of Criticality in Task-Engaged Regions. bioRxiv, 2023.06.29.547080. https://doi.org/10.1101/2023.06.29.547080
4. T Huang, Y Song, & J Liu (2022) Real-world size of objects serves as an axis of object space. Communications biology 5 (1), 749. https://doi.org/10.1038/s42003-022-03711-3
5. B Zhang & J Liu (2022) Spatial periodicity of hippocampal place cells in navigation. bioRxiv, 2022.01.29.478346. https://doi.org/10.1101/2022.01.29.478346
6. Y Zhang, K Zhou, P Bao, & J Liu (2021) Principles governing the topological organization of object selectivities in ventral temporal cortex. bioRxiv, 2021.09.15.460220. https://doi.org/10.1101/2021.09.15.460220
l Cognitive Science
1. X Feng, S Xu, Y Li, & J Liu (2023) Body size as a metric for the affordable world. eLife, https://doi.org/10.1101/2023.03.20.533336
2. S Xu, Y Song, & J Liu (2023) The development of spatial cognition and its malleability assessed in mass population via a mobile game. Psychological science 34 (3), 345-357. https://doi.org/10.1177/09567976221137313
3. T Huang & J Liu (2023) A stochastic world model on gravity for stability inference. eLife. https://doi.org/10.1101/2022.12.30.522364
l AI
1. Z Yin, W Ding, & J Liu (2023) Alignment is not sufficient to prevent large language models from generating harmful information: A psychoanalytic perspective. arXiv preprint arXiv:2311.08487, https://arxiv.org/abs/2311.08487
2. X Wang, X Li, Z Yin, Y Wu, & J Liu (2023) Emotional intelligence of large language models. Journal of Pacific Rim Psychology 17, https://doi.org/10.1177/18344909231213958
3. S Xu, Y Zhang, Z Zhen, & J Liu (2021) The face module emerged in a deep convolutional neural network selectively deprived of face experience. Frontiers in computational neuroscience 15, 626259. https://doi.org/10.3389/fncom.2021.626259
4. J Tian, H Xie, S Hu, & J Liu (2021) Multidimensional face representation in a deep convolutional neural network reveals the mechanism underlying AI racism. Frontiers in computational neuroscience 15, 620281. https://doi.org/10.3389/fncom.2021.620281
5. T Huang, Z Zhen, & J Liu (2021) Semantic relatedness emerges in deep convolutional neural networks designed for object recognition. Frontiers in computational neuroscience 15, 625804
6. Y Song, Y Qu, S Xu, & J Liu (2021) Implementation-independent representation for deep convolutional neural networks and humans in processing faces. Frontiers in computational neuroscience 14, 601314. https://doi.org/10.3389/fncom.2020.601314
7. X Liu, Z Zhen, & J Liu (2020) Hierarchical sparse coding of objects in deep convolutional neural networks. Frontiers in computational neuroscience 14, 578158. https://doi.org/10.3389/fncom.2020.578158
Contact Information
Email: liujiaTHU@tsinghua.edu.cn
Office: RM 909, Tsinghua Laboratory of Brain and Intelligence, Tsinghua University
