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 knowled...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.展开更多
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.
