
The British Library has been working with AI and related approaches –big data, deep neural networks, data science, machine learning, etc – for many years. This presentation shares examples of experiments, pilots and collaborations that have helped the Library understand the potential and challenges of AI with library collections for research and discovery. I discuss tools and methods that the BL developed internally or in collaboration, as well as external tools that we tried with BL collections. I focus on AI work with collections – there's a whole other world of potential for libraries and GLAMs as venues and visitor attractions.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
