Guest Editorial
What Machine Learning Means for Libraries The COVID-19 pandemic threw the role of technology in our lives into sharp relief. While videoconferencing programs, delivery apps, and the availability (or not) of broadband internet were some of the most visible examples, the pervasive use of artificial intelligence, particularly in the form of machine learning, has received comparatively less attention. Machine learning allows “smart assistants” like Alexa, Cortana, and Siri to comprehend our commands, cloud photo services to organize and tag friends in our photos, and our preferred streaming app to recommend what to watch or listen to next. Although we encounter these technologies every day, many people still do not know what machine learning is or how it works. Machine learning is a subset of artificial intelligence. Rather than operating in general ways like human intelligence often does, machine learning is intended to accomplish a specific task, such as making a decision in a narrow domain. For example, a machine learning tool could be built to determine whether a given picture contains a cat. While this may seem like a trivial task, the advantage of the technology becomes clear when the goal is to identify the presence of a cat in thousands, or even millions, of pictures. Machine learning is indispensable in the era of big data. The basis of all machine learning is pattern recognition. As a machine learning algorithm operates, it builds mathematical models of the subject of interest. Importantly, these mathematical models are not static, and that is where the “learning” aspect of the technology comes in. To return to the cat example, instead of a programmer trying to input all the possible properties of a cat picture ahead of time, the algorithm draws on
data from a collection of examples that do and do not contain cats to create mathematical models representing the degree of “catness” in a picture on its own terms. Then, as it is exposed to more data over time, it updates and refines these models. This not only improves the accuracy of the models, but also allows them to adapt. The flexibility inherent in the bottom-up identification of properties is a key advantage of machine learning. Medical researchers made extensive use of machine learning in order to develop prognoses and treatments for COVID-19. In the early days of the pandemic, when the disease was not well understood, scientists
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