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| Towards human-like and transhuman perception in AI 2.0: a review |
| Yong-hong Tian, Xi-lin Chen, Hong-kai Xiong, Hong-liang Li, Li-rong Dai, Jing Chen, Jun-liang Xing, Jing Chen, Xi-hong Wu, Wei-min Hu, Yu Hu, Tie-jun Huang, Wen Gao |
| School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611730, China; Department of Electronic Engineering and Information Sciences, University of Science and Technology of China, Hefei 230027, China; Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China |
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Abstract Perception is the interaction interface between an intelligent system and the real world. Without sophisticated and flexible perceptual capabilities, it is impossible to create advanced artificial intelligence (AI) systems. For the next-generation AI, called ‘AI 2.0’, one of the most significant features will be that AI is empowered with intelligent perceptual capabilities, which can simulate human brain’s mechanisms and are likely to surpass human brain in terms of performance. In this paper, we briefly review the state-of-the-art advances across different areas of perception, including visual perception, auditory perception, speech perception, and perceptual information processing and learning engines. On this basis, we envision several R&D trends in intelligent perception for the forthcoming era of AI 2.0, including: (1) human-like and transhuman active vision; (2) auditory perception and computation in an actual auditory setting; (3) speech perception and computation in a natural interaction setting; (4) autonomous learning of perceptual information; (5) large-scale perceptual information processing and learning platforms; and (6) urban omnidirectional intelligent perception and reasoning engines. We believe these research directions should be highlighted in the future plans for AI 2.0.
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Received: 12 December 2016
Published: 20 January 2017
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Cite this article:
Yong-hong Tian, Xi-lin Chen, Hong-kai Xiong, Hong-liang Li, Li-rong Dai, Jing Chen, Jun-liang Xing, Jing Chen, Xi-hong Wu, Wei-min Hu, Yu Hu, Tie-jun Huang, Wen Gao. Towards human-like and transhuman perception in AI 2.0: a review. Front. Inform. Technol. Electron. Eng., 2017, 18(1): 58-67.
URL:
http://www.zjujournals.com/xueshu/fitee/10.1631/FITEE.1601804 OR http://www.zjujournals.com/xueshu/fitee/Y2017/V18/I1/58
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