RT Journal Article
T1 The future: reflections on emerging machine-learning methods for digital heritage
A1 Asa Calow
YR 2023
VO Congruence Engine
IS Autumn 2022
K1 Artificial Intelligence
K1 Deep Learning
K1 few-shot learning
K1 fine-tuning
K1 large language models
K1 Machine Learning
K1 observability
AB 
NO https://hai.stanford.edu/research/ai-index-2022
NO https://jack-clark.net
NO https://lastweekin.ai
NO https://paperswithcode.com/
NO https://huggingface.co
NO For example, see https://layout-parser.github.io
NO https://le.ac.uk/library/special-collections/explore/historical-directories
NO For example, see https://github.com/microsoft/unilm/tree/master/dit
NO 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
NO http://www.incompleteideas.net/IncIdeas/BitterLesson.html
NO https://www.deepmind.com/blog/predicting-the-past-with-ithaca
NO https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
NO https://stability.ai/blog/stable-diffusion-public-release
NO ‘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
PB The Science Museum Group
SN 2054-5770
LA eng
DO 10.15180/221818
UL https://journal.sciencemuseum.ac.uk/article/the-future-reflections-on-emerging-machine-learning-methods-for-digital-heritage/
WT Science Museum Group Journal
OL 30