this post was submitted on 31 Aug 2023
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A trained AI model is a set of weights that is applied to the given neural network, the difference between two models, one trained without key data and one trained with key data, can be computed and a tool can be created to generate a transformation from model A to model B, or even a good approximation of model B trained with another AI.
It's not THAT hard actually.
I don't doubt that mathematically, but practically that sounds like it would be functionally equivalent to just retraining the model. Like if it were more efficient to just calculate the model weights based on input data, that's what we would do, there would be no need to go through the training process. We could just start with a completely untrained model and calculate the difference between that model and one that was trained with all the data. The more I think about it the more I doubt that mathematically. The feasibility of this would depend heavily on the details of the model and how it was trained. Lots of times the order in which the data was presented during training has an impact on the final result, so there's no guarantee your subtraction would achieve the same or even similar result as retraining without the specified data. Maybe you can reference some papers on the topic.
You are correct. It would be heinously expensive to "remove" training data. Even training a very rudimentary model can take hours on a high-end tensor processor.
You don't work in AI, do you?
I have a bachelors in computer science specialised in data engineering and data science, with a masters in data science, and I have worked for some years in computer vision, training and tweaking models.
Currently specialised in data engineering, but I'd wager I do know about what I'm talking about.
People who "work with AI" most of the time don't know shit about how it internally works, so I don't know if that's a label I'd even use to give an informed opinion about the matter.