We build robotic surrogates by "cloning" the embodied and situated
mind using wearable sensors, such as smart glasses, watches, brain scanners, body sensors, and mobile devices. We collect human activity data by long-term life-tracking in the real world. Based on these
observational data, we reverse-engineer the brain architecture by exploring the plausible neurocognitive models that
best explain the mind in working. We also use videos and cartoons to study how the cognitive brain constructs episodic memory and mental imagery from a long sequence of context-rich events while watching dramas and movies.
How does the biological mind work? How does the brain build a model of the world to instantly and robustly act on it? What kinds of processing and organizational mechanisms allow the brain to learn so rapidly, flexibly, and continuously in a noisy, dynamic, and changing environment? How can we build smart machines that perceive, think, act, and learn like the human brain and mind? In the Biointelligence Lab and the Cognitive Robotics and ArtificialIntelligence
Center (CRAIC) we pursue these questions using diverse methods of cognitive modeling, computational neuroscience, robotics, and computer science.
Our current efforts center around the deep, recurrent, and sparse hypernetwork architectures and learning algorithms that self-organize their structures instantly, incrementally, and continuously in a self-supervised way by perception-action cycle. The ultimate goal is to discover a large-scale, neurocognitive computational model of the brain that autonomously develops or evolves
into human-level machine intelligence in lifelong interactions with the environment. For the ongoing and completed projects, visit BI research. For general information, visit the Biointelligence Laboratory home.
Hypernetwork Models of Learning and Memory
Data Mining in Life Sciences and Life Blogs
Molecular Evolutionary Computation in Silico and in Vitro
Talks and Tutorials
Position Papers & Monographs [Korean]
- Humans and machines in the evolution of AI in Korea, AI Magazine, 37(2):108-112, 2016. [PDF]
- Deep hypernetwork models (in Korean), Communications of the Korean Institute of Information Scientists and Engineers, 33(8):11-24, 2015. [PDF]
- Team THOR's entry in the DARPA robotics challenge trials 2013, Journal of Field Robotics, 32(3):315-335, 2015. [PDF]
- Consensus analysis and modeling of visual aesthetic perception, IEEE Transactions on Affective Computing, 6(3):272-285. [PDF]
- Automated construction of visual-linguistic knowledge via concept learning from cartoon videos, J.-W. Ha, K.-M. Kim, and B.-T. Zhang, In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015), 2015.
- Ontogenesis of agency in machines: A multidisciplinary review, B.-T. Zhang, AAAI 2014 Fall Symposium on The Nature of Humans and Machines: A Multidisciplinary Discourse, Arlington, VA, 2014.
- Communication as moving target tracking: Dynamic Bayesian inference with an action-perception-learning cycle, B.-T. Zhang, Alignment in Communication. Towards a New Theory of Communication, Chapter 7, pp. 133-147, 2013.
- Information-theoretic objective functions for lifelong learning, B.-T. Zhang, AAAI 2013 Spring Symposium on Lifelong Machine Learning, Stanford University, March 25-27, 2013. [PDF]
- Sparse population code models of word learningin concept drift, B.-T. Zhang, J.-W. Ha, and M. Kang, In Proceedings of the 34th Annual Meeting of the Cognitive Science
Society (CogSci 2012), 2012. [PDF]
- A DNA assembly model of sentence generation, J.-H. Lee, S. H. Lee, W.-H. Chung, E. S. Lee, T. H. Park, R. Deaton, and B.-T. Zhang, BioSystems, 106:51-55, 2011. [PDF]
A molecular evolutionary architecture for cognitive learning and
memory, B.-T. Zhang, IEEE Computational Intelligence Magazine,
3(3):49-63, August 2008.[PDF][LINK]
- Molecular basis for the
recognition of primary microRNAs by the Drosha-DGCR8 complex, J. Han,
Lee Y, K.H. Yeom, J.-W. Nam, I. Heo, J.-K. Rhee, S. Y. Sohn, Y.
Cho, B.-T. Zhang and V.N. Kim, Cell,
125:887-901, 2006. [PDF]
evolutionary optimization of DNA sequences for reliable DNA computing,
S.-Y. Shin, I.-H. Lee, D. Kim, and B.-T. Zhang, IEEE Transactions on Evolutionary Computation,
9(2):143-158, 2005. [PDF]
model averaging of Bayesian network classifiers over multiple
node-orders: application to sparse datasets, K.-B. Hwang and B.-T.
Zhang, IEEE Transactions on
Systems, Man, and Cybernetics Part B: Cybernetics,
35(6):1302-1310, 2005. [PDF]
- Self-organizing latent
lattice models for temporal gene expression profiling, Zhang, B.-T, Yang, J. and Chi, S. W., Machine Learning,
- System identification using
evolutionary Markov chain monte carlo,
Zhang, B.-T. and Cho, D.-Y., Journal of Systems Architecture, 47(7):587-599,
- Bayesian methods for
efficient genetic programming, Zhang,
Programming and Evolvable Machines, 1(3):217-242,
- Evolutionary induction of
sparse neural trees, Zhang, B.-T., Ohm,
P., and Mühlenbein, H., Evolutionary Computation, 5(2):213-236, 1997. [PDF]
- Balancing accuracy and
parsimony in genetic programming, Zhang,
B.-T. and Mühlenbein, H., Evolutionary Computation, 3(1):17-38, 1995.
- Accelerated learning by
active example selection, Zhang, B.-T., International Journal of
Neural Systems, 5(1):67-75, 1994. [PDF]
- Evolving optimal neural
networks using genetic algorithms with Occam's razor, Zhang, B.-T. and Mühlenbein, H., Complex Systems, 7(3):199-220,
1993. [PDF] [LINK]