Comment by jacquesm
5 months ago
No, that depends on whether or not the AI work product rests on key contributions to its training set without which it would not be able to the the work, see other comment. In that case it looks like 'a new work doing the same' but it still a derived work.
Ted Nelson was years ahead of the future where we really needed his Xanadu to keep track of fractional copyright. Likely if we had such a mechanism, and AI authors respected it then we would be able to say that your work is derived from 3000 other original works and that you added 6 lines of new code.
No, training and inference are two separate processes. Training data is never redistributed, only obtained and analyzed. What matters is what data is put into context during inference. This is controlled by the user.
AI/ML is complex, so as a simpler analogy: If I watch The Simpsons, and I create an amusing infographic of how often Homer says "D'oh!" over time, my infographic would be an original work. AI training follows the same principle.
> my infographic would be an original work.
> AI training follows the same principle.
If you really believe that then we can't have a meaningful conversation about this, that's not even ELIF territory, that's just disconnected. You should be asking questions, not telling people how it works.
How exactly is it different? All the model itself is is a probability distribution for next token given input, fitted to a giant corpus. i.e. a description of statistical properties. On its own it doesn't even "do" anything, but even if you wrap that in a text generator and feed it literal gcc source code fragments as input context, it will quickly diverge. Because it's not a copy of gcc. It doesn't contain a copy of gcc. It's a description of what language is common in code in general.
In fact we could make this concrete: use the model as the prediction stage in a compressor, and compress gcc with it. The residual is the extent to which it doesn't contain gcc.
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