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CLC number: TP391.4

On-line Access: 2017-01-20

Received: 2016-12-31

Revision Accepted: 2017-01-09

Crosschecked: 2017-01-11

Cited: 4

Clicked: 5965

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fei Wu

http://orcid.org/0000-0003-2139-8807

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.1 P.3-14

http://doi.org/10.1631/FITEE.1601883


Challenges and opportunities: from big data to knowledge in AI 2.0


Author(s):  Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   yzhuang@zju.edu.cn, wufei@zju.edu.cn, chenc@zju.edu.cn, panyh@cae.cn

Key Words:  Deep reasoning, Knowledge base population, Artificial general intelligence, Big data, Cross media


Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. Challenges and opportunities: from big data to knowledge in AI 2.0[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14.

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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.

挑战与希望:AI2.0时代从大数据到知识

概要:本文对大数据时代人工智能领域近期出现的若干理论和技术进展进行了综述。我们认为,将数据驱动机器学习方法与人类的常识先验与隐式直觉有效结合起来,可实现可解释、更鲁棒和更通用的人工智能。AI 2.0时代大数据人工智能具体表现为:从浅层计算到深度神经推理;从单纯依赖于数据驱动的模型到数据驱动与知识引导相结合学习;从领域任务驱动智能到更为通用条件下的强人工智能(从经验中学习)。下一代人工智能(AI 2.0)将改变计算本身,将大数据转变为知识以支持人类社会作出更好决策。

关键词:深度推理;知识库扩充;强人工智能;大数据;跨媒体

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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