Inference from associative statistical models is a kind of remembering. Think of a model as a heated metal sheet placed over some surface (ie., the data); trai

Inference from associative statistical models is a kind of remembering. Think of a model as a heated metal sheet placed over some surface (ie., the data); training is heating this sheet just enough that it takes an impression, but not so much that the impression is exact. Cooling and heating the metal to smooth-over impressions is called "regularization". This is very different from, e.g., explanatory modelling, whereby some (e.g., scientist) builds a device to imitate a mechanism in the world, rather than a dataset. Consider a building a model of the solar systems with cogs (etc.). A mechanistic model of the solar system isn't a remembering of any data taken about the solar system, rather its a machine which can generate such data. In the latter case, "inference" is running the model which has been crafted so its mechanisms track properties the world has. In the former case, "inference" is throwing a dart on the metal sheet, and hoping that whatever impression has been taken at that point will be good-enough. So the technical question here isnt open. All associative modelling is a kind of impression-taking of a particular historical dataset. The moral question is whether one ought be paid (etc.) for providing the data. Soon enough it will be easy to invert a large LLM and show you the sources of data it draws from in its replies. At the moment, this is being attempted, but it isnt presented that well and computationally difficult for larger models. Nevertheless, when it happens, I think people will be less impressed. Perhaps, optimistically, they might be more impressed at the people who wrote the original works being sampled. However, of course, I doubt it. The one thing which often accompanies the adoration of LLMs is a dehumanising impulse to deprive people of any capacities at all.