For the last 20 years or so， AI has been focused on the problems surrounding the construction of intelligent agents， systems that perceive and act in some environment。 In this context， intelligence is related to statistical and economic notions of rationality。 Colloquially， the ability to make good decisions， plans， or inferences。 As a result of this recent work， there has been a large degree of integration and cross-fertilisation among AI， machine learning， statistics， control theory， neuro science， and other fields。 The establishment of shared theoretical frameworks， combined with the availability of data and processing power， has yielded remarkable successes invarious component tasks， such as speech recognition， image classification， autonomous vehicles， machine translation， legged locomotion， and question-answering systems。
在过去的20年或更长时间里，人工智能一直专注于建设智能代理所产生的问题，即在特定环境下可以感知并行动的各种系统。在这种情况下，智能是一个与统计学和经济学相关的理性概念。通俗地讲，这是一种做出好的决定、计划和推论的能力。基于这些工作，大量的整合和交叉孕育被应用在人工智能、机器学习、统计学、控制论、神经科学、以及其它领域。共享理论框架的建立，结合数据的供应和处理能力，在各种细分的领域取得了显著的成功。例如语音识别、图像分类、自动驾驶、机器翻译、步态运动和问答系统。
As development in these areas and others， moves from laboratory research to economically valuable technologies， a virtuous cycle evolves， whereby even small improvements in performance， are worth large sums of money， prompting further and greater investments in research。 There is now a broad consensus that AI research is progressing steadily， and that its impact on society is likely to increase。 The potential benefits are huge， since everything that civilization has to offer， is a product of human intelligence； we can not predict what we might achieve， when this intelligence is magnified by the tools AI may provide。 But， and as I have said， the eradication of diseaseand poverty is not unfathomable。 Because of the great potential of AI， it is important to research how to reap its benefits， while avoiding potential pitfalls。
