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Artificial intelligence and the library of the future, revisited

Artificial intelligence and the library of the future, revisited. Catherine Nicole Coleman Digital Research Architect Research Director, Humanities + Design, Stanford Libraries. November 3, 2017.

“There are two breakthrough technologies catching fire on campus these days. One of them, CRISPR-Cas9, is changing our relationship to the physical world through gene editing. The other, Artificial Intelligence (AI), is changing how we generate, process and analyze information. AI already touches many of our daily computing activities, from searching the web to managing spam in email applications. It underlies the speech recognition that makes Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, and the Google Assistant able to process and respond — with some success — to our queries. It is the computer vision that helps self-driving cars and food delivery robots navigate our streets and sidewalks. The fundamental activity driving these varied applications of AI is search within a large space of possibilities. It is not deep cognition but perceptual recognition. The power lies in the fact that machines can recognize patterns efficiently and routinely, at a scale and speed that humans cannot approach. Though the underlying AI technologies that make all these applications possible have existed since the 1970s and 80s, AI has really taken off in the last decade, applied to search within images, sound, and text. Natural Language Processing (NLP) in Linguistics is a system for understanding language that has opened entirely new avenues of research across the university, making it possible to mine large corpora, identify topics, recognize named entities (people, places, and things), and perform sentiment analysis. Computer Vision is an interdisciplinary branch of AI that incorporates knowledge from several domains including physics, signal processing, and neurobiology to understand images and video. Machine learning  dramatically accelerates statistical pattern recognition by learning from examples. Research to predict crop yield from remote sensing data, diagnose heart arrhythmias, and read 2,000-year-old papyri carbonized by the eruption of Vesuvius use machine learning in combination with other AI technologies…”

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