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"> |