%0 Journal Article
%T The future: reflections on emerging machine-learning methods for digital heritage
%A Asa Calow
%D 2023
%V Congruence Engine
%N Autumn 2022
%K Artificial Intelligence
%K Deep Learning
%K few-shot learning
%K fine-tuning
%K large language models
%K Machine Learning
%K observability
%X 
%Z https://hai.stanford.edu/research/ai-index-2022
%Z https://jack-clark.net
%Z https://lastweekin.ai
%Z https://paperswithcode.com/
%Z https://huggingface.co
%Z For example, see https://layout-parser.github.io
%Z https://le.ac.uk/library/special-collections/explore/historical-directories
%Z For example, see https://github.com/microsoft/unilm/tree/master/dit
%Z An example of this can be found in ‘The Shape of Art History in the Eyes of the Machine’ https://arxiv.org/abs/1801.07729
%Z http://www.incompleteideas.net/IncIdeas/BitterLesson.html
%Z https://www.deepmind.com/blog/predicting-the-past-with-ithaca
%Z https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
%Z https://stability.ai/blog/stable-diffusion-public-release
%Z ‘No one asked for a Ruth Bader Ginsburg chatbot, but now we have one’ https://www.inputmag.com/tech/ask-ruth-bader-ginsberg-chatbot-ai21
%I The Science Museum Group
%@ 2054-5770
%B eng
%U https://journal.sciencemuseum.ac.uk/article/the-future-reflections-on-emerging-machine-learning-methods-for-digital-heritage/
%J Science Museum Group Journal