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How does this work? My understanding was that the only "learning" that took place is when the model is trained on the dataset (which is done only once, requiring a huge amount of computational resources), and any subsequent usage of the model has no effect on the training.
I'm far from an expert here.
If they want to make the AI 'smarter' at the cost of longer/more expensive training, they can add parameters (i.e. variables that the AI considers when interpreting an input and translating it into an output), and more data to train on to better refine said parameters. Very roughly speaking, this is the difference between training the AI to recognize colors in terms of 'only' the seven colors of the rainbow vs. the full palette of Crayola crayons vs. at the extreme end the exact electromagnetic frequency of every single shade and brightness of visible light.
My vague understanding is that the current models are closer to the crayola crayons than to the full electromagnetic frequency.
Tweaking an existing model can also achieve improvements, think in terms of GANs.
If the AI produces an output and receives feedback from a human or another AI as to how well the output satisfices the input, and is allowed to update its own internals based on this feedback, it will become better able to produce outputs that match the inputs.
This is how a model can get refined without needing to completely retrain it from scratch.
Although with diffusion models like DallE, outputs can also be improved by letting the model take more 'steps' (i.e. run it through the model again and again) to refine the output as far as it can.
As far as I know there's very little benefit to manually tweaking the models once they're trained, other than to e.g. implement a NSFW filter or something.
And as we produce and concentrate more computational power, it becomes more and more feasible to use larger and larger models for more tasks.
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