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→与人工智能 Artificial intelligence的关系Relation to artificial intelligence
然而，日益强调的'''基于知识的逻辑方法 Knowledge-based Approach'''导致了人工智能和机器学习之间的裂痕。概率系统一直被数据获取和表示的理论和实际问题所困扰。在人工智能内部，符号/知识学习的工作确实在继续，导致了归纳逻辑编程，但更多的统计研究现在已经超出了人工智能本身的领域，即模式识别和信息检索。神经网络的研究几乎在同一时间被人工智能和计算机科学领域所抛弃，但这种思路却在人工智能/计算机之外的领域被延续了下来，被其他学科的研究人员称为“连接主义”。（编者注：连接主义又称为仿生学派或生理学派，其主要原理为神经网络及神经网络间的连接机制与学习算法。）包括霍普菲尔德 Hopfield、鲁梅尔哈特 Rumelhart和辛顿 Hinton。他们的主要成就集中在20世纪80年代中期，在这一阶段神经网络的方法随着反向传播算法的出现而重新被世人所重视。
然而，日益强调的'''基于知识的逻辑方法 Knowledge-based Approach'''导致了人工智能和机器学习之间的裂痕。概率系统一直被数据获取和表示的理论和实际问题所困扰。在人工智能内部，符号/知识学习的工作确实在继续，导致了归纳逻辑编程，但更多的统计研究现在已经超出了人工智能本身的领域，即模式识别和信息检索。神经网络的研究几乎在同一时间被人工智能和计算机科学领域所抛弃，但这种思路却在人工智能/计算机之外的领域被延续了下来，被其他学科的研究人员称为“连接主义”。（编者注：连接主义又称为仿生学派或生理学派，其主要原理为神经网络及神经网络间的连接机制与学习算法。）包括霍普菲尔德 Hopfield、鲁梅尔哈特 Rumelhart和辛顿 Hinton。他们的主要成就集中在20世纪80年代中期，在这一阶段神经网络的方法随着反向传播算法的出现而重新被世人所重视。
Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory. As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr Daniel Hulme, who both teaches AI and runs a company operating in the field, argues that machine learning and AI are separate.
机器学习，在重组为一个独立的领域之后，与20世纪90年代开始蓬勃发展。该领域的目标从实现人工智能转变为解决实际中的可解决问题。它将焦点从它从人工智能继承的符号方法转移到从统计学和概率论中借鉴的方法和模型。截止至2019年，许多资料都继续断言机器学习仍然是人工智能的一个子领域。然而，一些该领域的从业者（例如丹尼尔 · 休姆 Daniel Hulme博士，他既教授人工智能，又经营着一家在该领域运营的公司），则认为机器学习和人工智能是分开的。
Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the [[symbolic artificial intelligence|symbolic approaches]] it had inherited from AI, and toward methods and models borrowed from statistics and [[probability theory]].<ref name="changing" /> As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr [[Daniel J. Hulme|Daniel Hulme]], who both teaches AI and runs a company operating in the field, argues that machine learning and AI are separate. <ref name="elements">
Machine learning, reorganized as a separate field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the [[symbolic artificial intelligence|symbolic approaches]] it had inherited from AI, and toward methods and models borrowed from statistics and [[probability theory]].<ref name="changing" /> As of 2019, many sources continue to assert that machine learning remains a sub field of AI. Yet some practitioners, for example Dr [[Daniel J. Hulme|Daniel Hulme]], who both teaches AI and runs a company operating in the field, argues that machine learning and AI are separate. <ref name="elements">