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

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• 作者：Yue-ting Zhuang ; Fei Wu ; Chun Chen…
• 关键词：Key wordsDeep reasoning ; Knowledge base population ; Artificial general intelligence ; Big data ; Cross media
• 刊名：Frontiers of Information Technology & Electronic Engineering
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：18
• 期：1
• 页码：3-14
• 全文大小：
• 刊物类别：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization
• 刊物主题：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization and Communication Networks; Electronics and Microelectronics, Instrumentation; Communications Engine
• 出版者：Zhejiang University Press
• ISSN：2095-9230
• 卷排序：18

文摘

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.