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Front. Inform. Technol. Electron. Eng.  2017, Vol. 18 Issue (1): 3-14    DOI: 10.1631/FITEE.1601883
Review Articles     
Challenges and opportunities: from big data to knowledge in AI 2.0
Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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Abstract  In this paper, we review recent emerging theoretical and technological advances of artificial intelligence (AI) in the big data settings. We conclude that integrating data-driven machine learning with human knowledge (common priors or implicit intuitions) can effectively lead to explainable, robust, and general AI, as follows: from shallow computation to deep neural reasoning; from merely data-driven model to data-driven with structured logic rules models; from task-oriented (domain-specific) intelligence (adherence to explicit instructions) to artificial general intelligence in a general context (the capability to learn from experience). Motivated by such endeavors, the next generation of AI, namely AI 2.0, is positioned to reinvent computing itself, to transform big data into structured knowledge, and to enable better decision-making for our society.

Key wordsDeep reasoning      Knowledge base population      Artificial general intelligence      Big data      Cross media     
Received: 31 December 2016      Published: 20 January 2017
CLC:  TP391.4  
Cite this article:

Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. Challenges and opportunities: from big data to knowledge in AI 2.0. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 3-14.

URL:

http://www.zjujournals.com/xueshu/fitee/10.1631/FITEE.1601883     OR     http://www.zjujournals.com/xueshu/fitee/Y2017/V18/I1/3

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