Bio-Inspired Human-Level Machine Learning
Final rept. 27 Sep 2012-26 Sep 2015
SEOUL NATIONAL UNIV (REPUBLIC OF KOREA)
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How can brain computation be so fast, flexible, and robust What kinds of representational and organizational principles facilitate the biological brain to learn so efficiently and flexibly on the sub-second time scale and so reliably on the continuous lifetime scale To understand these principles, we aimed to develop human-level machine learning technology that is fast, flexible, and reliable to adapt to a continuously changing, dynamic environment. Based on dynamic neural populations neural assemblies, we constructed a human-like machine learning model and implement this model in molecular populations molecular assemblies using in vitro DNA computing. In the first year, we developed the dynamic hypernetwork models of neural populations in the sequential Bayesian framework for lifelong learning. In the second year, we extended it to the molecular dynamic hypernetwork model, and designed in vitro experimental protocols to implement online language learning from a stream of text corpus. In the third year, we demonstrated the use of molecular dynamic hypernetworks for multimodal visuo-linguistic concept learning from a long stream of video data and their extensions to high-level cognitive functions such as anagram solving problem.
- Operations Research