TY  -  JOUR
TI  -  The future: reflections on emerging machine-learning methods for digital heritage
  AU -Asa Calow
PY  -  2023
VL  - Congruence Engine
IS  - Autumn 2022
KW - Artificial Intelligence
KW - Deep Learning
KW - few-shot learning
KW - fine-tuning
KW - large language models
KW - Machine Learning
KW - observability

AB - 
N1 - https://hai.stanford.edu/research/ai-index-2022
N1 - https://jack-clark.net
N1 - https://lastweekin.ai
N1 - https://paperswithcode.com/
N1 - https://huggingface.co
N1 - For example, see https://layout-parser.github.io
N1 - https://le.ac.uk/library/special-collections/explore/historical-directories
N1 - For example, see https://github.com/microsoft/unilm/tree/master/dit
N1 - 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
N1 - http://www.incompleteideas.net/IncIdeas/BitterLesson.html
N1 - https://www.deepmind.com/blog/predicting-the-past-with-ithaca
N1 - https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model
N1 - https://stability.ai/blog/stable-diffusion-public-release
N1 - ‘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
UR -  https://journal.sciencemuseum.ac.uk/article/the-future-reflections-on-emerging-machine-learning-methods-for-digital-heritage/
T2 -  Science Museum Group Journal