You may have heard how artificial intelligence (AI) is being deployed within the information industry to combat fake news, detect plagiarism, and even recommend content to users. Until now however, AI has had minimal impact on the content creation and editorial functions of the publishing ecosystem. For scholarly publishers in particular, AI capabilities have advanced to a degree that they can actually automate significant portions of their workflows, with massive implications for their businesses, their authors and the research community. AI is a method by which humans train machines to identify patterns and learn new patterns. It involves developing algorithms that enable machines to quickly process large swaths of data, recognize the patterns within that data, and make decisions or recommendations based on that analysis.

Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. Three primary uses are predictive analytics, deductive reasoning and natural language understanding. Interfaces for domains such as search and geolocation are increasingly natural-language-like instead of using rigid menu-driven, or programming-language-like interfaces. The task of understanding the user's intention requires complex systems based on machine learning, training data, NLP algorithms modeling theoretical linguistics, or a combination of these techniques. Secondly, machine learning allows us to predict user intention based off of previous user data and tendencies.

Machine Learning, Artificial Intelligence and Natural Language Processing (NLP) are transforming the technological landscape in a wide range of applications. Three primary uses are predictive analytics, deductive reasoning and natural language understanding. Interfaces for domains such as search and geolocation are increasingly natural-language-like instead of using rigid menu-driven, or programming-language-like interfaces. The task of understanding the user's intention requires complex systems based on machine learning, training data, NLP algorithms modeling theoretical linguistics, or a combination of these techniques. Secondly, machine learning allows us to predict user intention based off of previous user data and tendencies.