Developments in artificial intelligence (AI) present a host of exciting opportunities for GLAMs (galleries, libraries, archives and museums) in the digital world. These range from the development of models or algorithms perfected through data processing, to mining, analysing and enriching datasets with new metadata. CC fully supports GLAMs in using the massive amounts of data in their digital collections for AI-training purposes (including machine-learning) in order to fulfil their public interest missions. This uncertainty is likely to have a chilling effect on GLAMs wishing to take advantage of AI technologies. A flowchart helps visualise whether the licenses are triggered and if so, what conditions may apply.

AI terminology can be complex, so let's clear up some definitions. While reading our posts you might see terms like'machine learning', 'deep learning', 'models' or'training'. Machine learning vs deep learning is a common area of confusion for those not familiar with AI techniques. Machine learning consists of a set of algorithms which automatically learn from data. Deep learning is a type of machine learning that excels in solving problems with high dimensionality (where the number of features is much greater than the number of observations). Deep learning uses a family of models inspired by the structure and functioning of the brain (artificial neural networks) that effectively learn to extract relevant features from the data.