Keywords
Artificial Intelligence, Machine Learning, Libraries, Digital Collections, Research Datasets
Description
The presentation will explore Northwestern University Libraries (NUL) new strategic vision with artificial intelligence / machine learning (AI/ML) as a central pillar of its strategic pursuing. NUL identifies AI/ML as a logical core of the library’s vision for two reasons. First, AI/ML methods offer analytical power at massive data scales that can be immediately applied to library digital collections and research datasets, enabling scholars to investigate research questions by creating computational models from any library collection. Second, AI/ML represents a genuinely transdisciplinary set of techniques that can be adapted to create models based on any data type, from any disciplinary context, whether qualitative or quantitative, structured or unstructured, and across all media types. The presentation will share NUL strategic planning and priority realignment experiences as we set AI/ML as the core element of their organizations’ strategic visions. The presentation will explore: (1) the opportunities and limitations of an AI-driven library vision in the context of CNI/ARL AI Scenarios Planning. (2) the culture building, organizational structure, workforce development and resource alignment through internal and external funding mechanisms, required within library organizations to accomplish it. (3) The presentation will conclude with a reflection on potential new ways to be more directly aligned with emerging university-level data science and AI initiatives, and with the faculty research and teaching driving these initiatives, in the coming decade.
Accelerated Libraries Digital Transformation –An AI Centric Planning
The presentation will explore Northwestern University Libraries (NUL) new strategic vision with artificial intelligence / machine learning (AI/ML) as a central pillar of its strategic pursuing. NUL identifies AI/ML as a logical core of the library’s vision for two reasons. First, AI/ML methods offer analytical power at massive data scales that can be immediately applied to library digital collections and research datasets, enabling scholars to investigate research questions by creating computational models from any library collection. Second, AI/ML represents a genuinely transdisciplinary set of techniques that can be adapted to create models based on any data type, from any disciplinary context, whether qualitative or quantitative, structured or unstructured, and across all media types. The presentation will share NUL strategic planning and priority realignment experiences as we set AI/ML as the core element of their organizations’ strategic visions. The presentation will explore: (1) the opportunities and limitations of an AI-driven library vision in the context of CNI/ARL AI Scenarios Planning. (2) the culture building, organizational structure, workforce development and resource alignment through internal and external funding mechanisms, required within library organizations to accomplish it. (3) The presentation will conclude with a reflection on potential new ways to be more directly aligned with emerging university-level data science and AI initiatives, and with the faculty research and teaching driving these initiatives, in the coming decade.
