This brief article is an attempt to provide some reasonably sober and concrete sense of what actual and relevant changes might occur within the next decade or so, without going into technical details, and what these changes might imply for the practices of archives and special collections, or cultural memory organizations more broadly.
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One of the great, largely unexplored challenges for cultural memory organizations is the extent to which it is advantageous to “customize” or specifically train machine learning on individual collections – an individual’s handwriting, as opposed to Victorian copperplate script broadly; or the set of family members that might likely appear in a collection of photographs. Creating these training sets will be expensive, and the cost and workflow trade-offs will be critical.
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