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.
Corresponding Author(s):
Yue-ting ZHUANG,Fei WU,Chun CHEN,Yun-he PAN
引用本文:
庄越挺,吴飞,陈纯,潘云鹤. 挑战与希望：AI2.0时代从大数据到知识[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14.
Yue-ting ZHUANG,Fei WU,Chun CHEN,Yun-he PAN. Challenges and opportunities: from big data to knowledge inAI 2.0. Front. Inform. Technol. Electron. Eng, 2017, 18(1): 3-14.
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