@lxo one other question. Why are you taking a position when you clearly have absolutely no idea how any of this works? This whole exchange left me feeling gross like after talking to an elected official that is still always campaigning. Just entirely disengenuis.
Study and understand: A model's behavior heavily relies on its training data. Without it, fully grasping why the model makes specific predictions is difficult (nearly impossible for sophisticated models).
Improve and adapt: Effective improvements often require retraining with modified or additional data. Without the original training data, replicating or enhancing the model's capabilities is challenging.
@lxo Let's say I have created a cool BigNumber library that performs math functions really well.
So I take my library and generate a lot of small object code blobs. Labels are the function and parameter calls associated with the object code blobs (fft, cosine, lambda, etc).
I train a model with a feed forward neural network so that the function call labels can be used to predict blobs.
My free software programs replace GNU's big number library with this AI model.
If people are on the fence on whether a given AI model use data or object code, then lets just use real example AI-programs and put it through the rigor of real GPLv3 style compliance on real programs.
I did this with my own projects (which now feel ancient being over 20 years old) and this convinced me beyond a doubt that training data needs to be provided to comply with the letter of the GPL. It is robot controller based on an AI model. Corresponding source includes training data.
@kirschner My daughter, Ada (7y/o), read the first half of the hardcover edition tonight and she is absolutely loving it! She is excited to write a review of the book when she is done. :)
I like helping open source communities and get to do it for a living. Open Source at IEEE SA (but thoughts my own) and ex: FSF, CK-12, Tor. Father of 4, living in Boulder, COI joined mastodon.social in April 2017, and migrated here in Nov. 2022.