Our collective understanding of what AI does, what's required to modify language models is limited now. The more we use it, the more we'll understand.
Balancing open source principles with AI complexities can sometimes feel like trying to solve a Rubik's Cube blindfolded.
One of the biggest challenges in creating the Open Source AI Definition is deciding how to treat datasets used during the training phase.
At first, requiring all raw datasets to be made public might seem logical. However, this analogy between datasets and source code is imperfect.
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