Digital libraries: the systems analysis perspective machine erudition

摘要

Purpose - The purpose of this paper is to explore the concept of machine learning. Current trends in the field are explored, along with the potential impact on information science. Machine learning is both an old and new field. It has been theoretically explored since the 1940s, but advances in technology, statistics and mathematics have recently created conditions, wherein it can be put into practice. Design/methodology/approach - This is a conceptual column exploring the notion of machine learning and the applications for information science. Findings - Some of the objections to machine intelligence are common philosophical problems dealing with the nature of thinking, self-awareness, understanding and other human traits that allow us to relate to people, develop intuitions and have situational awareness. Originality/value - While machine learning is being taken advantage of in the commercial sector, it has not been effectively exploited in the academic sphere. Libraries have traditionally focused on structured analysis and strictly controlled vocabularies to enable information discovery. Machine learning opens up possibilities for unstructured data to be analyzed intelligently. Over 80 per cent of regularly consumed information on the Internet is unstructured, so this field has huge implications for discovery from a library perspective.