Tim O’Reilly presents a Web 2.0 ‘MemeMap’, the result of some brainstorming at a recent event. This places some familiar concepts (services, not packaged software; remixable data source and data transformations; data as the ‘Intel inside’) in useful frame.
My eye was drawn to ‘harnessing collective intelligence’, as it echoed a phrase that I have been using here of late. How, in the current environment do we better mobilize the collective capacity of libraries to deliver value.
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Generative AI, scholarly and cultural language models, and the return of content
AI momentum continues to grow, and we are seeing more applications in the scholarly and cultural spaces. Several organizations have been creating specialist large language models based on reservoirs of curated scientific and cultural data.
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Generative AI and large language models: background and contexts
The promise and challenge of Generative AI is now central. This is a summary overview of some of the major directions and issues, acknowledging that things are moving very quickly. I intend it as background to later posts about library implications and developments.
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The technology career ladder
Library leaders should be drawn from across the organization. Any idea that technology leaders are overly specialised or too distant from general library work is outmoded and counter-productive.
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