The Open Source Initiative, who maintain the Open Source Definition, have been running a whole series over the past year to collect input from all sorts of stakeholders about what it means for an AI to be open source. I was lucky enough to participate in an afternoon long session with about a hundred other people last year at All Things Open.
Can you summarize? I'm reading https://deepdive.opensource.org/wp-content/uploads/2023/02/D... but it seems to tackle too many questions when I'm really only interested on what criteria to use when deciding whether (for example) Stable Diffusion is open source or not.
Anyway, to go on a tangent, some day maybe with zero knowledge proofs we will be able to prove that a given pretrained model was indeed the result of training using a given dataset, in a way that can be verified vastly cheaper than training the model itself from scratch. (This same technique could also be applied to other things like verifying if a binary was compiled from a given source with a given compiler, hopefully verified in a cheaper way than compiling and applying all optimizations from scratch).
If this ever materialize, then we can just demand proofs.
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The Open Source Initiative, who maintain the Open Source Definition, have been running a whole series over the past year to collect input from all sorts of stakeholders about what it means for an AI to be open source. I was lucky enough to participate in an afternoon long session with about a hundred other people last year at All Things Open.
https://deepdive.opensource.org/
I encourage you to go check out what's already being done here. I promise it's way more nuanced than anything than is going to fit on a tweet.
Can you summarize? I'm reading https://deepdive.opensource.org/wp-content/uploads/2023/02/D... but it seems to tackle too many questions when I'm really only interested on what criteria to use when deciding whether (for example) Stable Diffusion is open source or not.
Anyway, to go on a tangent, some day maybe with zero knowledge proofs we will be able to prove that a given pretrained model was indeed the result of training using a given dataset, in a way that can be verified vastly cheaper than training the model itself from scratch. (This same technique could also be applied to other things like verifying if a binary was compiled from a given source with a given compiler, hopefully verified in a cheaper way than compiling and applying all optimizations from scratch).
If this ever materialize, then we can just demand proofs.
Here's a study on that
https://montrealethics.ai/experimenting-with-zero-knowledge-...
https://dl.acm.org/doi/10.1145/3576915.3623202
And here is another
https://eprint.iacr.org/2023/1174