On the transparency of large AI models
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Date
Authors
Wang, Wanying
Wang, Ge
Marivate, Vukosi
Hufton, Andrew L.
Journal Title
Journal ISSN
Volume Title
Publisher
Cell Press
Abstract
As large AI models demonstrate increasingly human-like performance
on complex tasks, many scientists are developing
or adapting these models to empower their research and
applications. Because of the substantial costs involved in
building, training, and running large AI models, closedsource
models can often offer performance that cannot be
matched by open-source counterparts, making them tempting
tools for researchers even if they are not transparent or
accessible according to conventional academic standards.
Moreover, even researchers who are developing their own AI
models may face special challenges when trying to publish
their work in an open and reproducible manner. In particular,
the very large datasets required to train AI models often
come with special challenges that make them inherently
hard to share—ranging from sheer size to tricky copyright
and privacy issues. In this editorial, we share some insights
and tips that we hope will help researchers in this field understand
our journal’s policies and prepare submissions for the
journal.
Description
Keywords
AI models, Transparency, Scientists, Tools, Editorial, Artificial intelligence (AI), SDG-09: Industry, innovation and infrastructure
Sustainable Development Goals
SDG-09: Industry, innovation and infrastructure
Citation
Wang, W., Wang, G., Marivate, V. et al. 2023, 'On the transparency of large AI models', Patterns, vol. 4, pp. 1-2. https://DOI.org/10.1016/j.patter.2023.100797.