网络时代人工智能研究与发展

50多年来，人工智能在模式识别、知识工程、机器人等领域已经取得重大成就，但是离真正的人类智能还相差甚远.当今网络时代，人工智能科学要在学科交叉研究中实现人工智能的发展与创新，会更加关注认知科学、脑科学、生物智能、物理学、网络科学、计算机科学与人工智能之间的交叉渗透，重视认知物理学的研究；自然语言是人工智能研究知识表示无法回避的直接对象，要对语言中的概念建立起能够定量表示的不确定性转换模型，发展不确定性人工智能；要利用现实生活中复杂网络的小世界模型和无标度特性，把网络拓扑作为知识表示的一种新方法，研究网络拓扑的演化与网络动力学行为，研究网络智能.对这3个重要方向进行了阐述，并提出了具体建议.

AI research and development in the network age

Pattern recognition, knowledge engineering, and robotics have made significant progress in the 50 year history of artificial intelligence, yet AI displays far from human intelligence. In the current network era, if researchers in artificial intelligence want to maximize developments and innovations in interdisciplinary studies, they must pay more attention to the intersections and infiltrations of cognitive science, brain science, physics, network science, computer science, and artificial intelligence. Research in cognitive physics will be an especially important direction in AI. Natural language is an important objective in AI research; we need to establish an uncertainty transformation model that can quantitatively represent its concepts. This dictates that an AI science with uncertainty will be developed. Considering the small world model and scale free features of complex networks in real life, we need to use network topology as a new way for knowledge representation. This will aid study of the progress of network topology, network dynamics and intelligence. This paper discusses these three directions in detail. Some concrete suggestions for further research are also provided.