Epigenetic Development: Generating Internal Representations through Interactions with the Real-World Environments

August 20, 2007
Juyang (John) Weng | Michigan State University

Human manually written programs do many (clean) tasks well, such as word processing or dexterous dancing. However, machines have done poorly for (muddy) tasks that the brain is good at, such as perceiving and behaving properly in open ended, complex human environments. Human hand-designed task-specific representations face great challenges in such environments. Inspired by neuroscienece, this talk presents general purpose architectures that constrain the types of representation to be generated. Further, inspired by the biological laminar cortical structure, the Multilayer In-place Learning Networks (MILN) is presented to show what the internal representation is, how it is automatically generated while the system interacts with the environments. MILN has several major advantages over existing networks, such as FFN, RBF, SOM, CCLA, SVM and IHDR. This new technology indicates a commercial potential for generating a new kind of programs — epigenetically programmed through human-machine interactions — to lead to seeing, hearing, thinking and behaving machines and robots. (A more technical talk can be arranged in another setting.)

Speaker Details

Juyang (John) Weng is a professor at the Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, USA. He received his MS and PhD degrees from University of Illinois at Urbana-Champaign, 1985 and 1989, respectively, all in Computer Science. His recent research interests include perceptual and cognitive development, vision, audition, touch, human-machine multimodal interface, and intelligent robots. He is the author or coauthor of over two hundred research articles and book chapters published in books, journals, conferences and workshops. He is an editor-in-chief of International Journal of Humanoid Robotics and the Chairman of the Governing Board of the multidisciplinary International Conferences on Development and Learning (ICDL) (http://cogsci.ucsd.edu/~triesch/icdl/). He was the chairman of the Autonomous Mental Development Technical Committee of the IEEE Computational Intelligence Society (2004-2005), an associate editor of IEEE Trans. on Pattern Recognition and Machine Intelligence, an associate editor of IEEE Trans. on Image Processing. He initiated and supervised the SAIL (Self-organizing Autonomous Incremental Learner) and Dav projects, in which he and his coworkers have designed and custom built their SAIL and Dav robots for research on robotic computational realization of autonomous cognitive development. He is the author of an up-coming book titled “Computational Autonomous Mental Development” MIT Press, 2008.