this post was submitted on 11 Jan 2026
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Fuck AI

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AI, in this case, refers to LLMs, GPT technology, and anything listed as "AI" meant to increase market valuations.

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recently my friend's comics professor told her that it's acceptable to use gen Al for script- writing but not for art, since a machine can't generate meaningful artistic work. meanwhile, my sister's screenwriting professor said that they can use gen Al for concept art and visualization, but that it won't be able to generate a script that's any good. and at my job, it seems like each department says that Al can be useful in every field except the one that they know best.

It's only ever the jobs we're unfamiliar with that we assume can be replaced with automation. The more attuned we are with certain processes, crafts, and occupations, the more we realize that gen Al will never be able to provide a suitable replacement. The case for its existence relies on our ignorance of the work and skill required to do everything we don't.

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[–] peanuts4life@lemmy.blahaj.zone 1 points 14 hours ago (1 children)

Clunky wording on my part. I mean results can be tested objectively. In creative fields, there are no objective means of testing outputs. In programming, one model can, for example, build a user input field to match requirements, and another model can test it. The success and failure of those test can be measured objectively (do stored inputs fall within the desired domain, does a hash of the memory (sans known changing variables) change?)

[–] HetareKing@piefed.social 1 points 14 hours ago (1 children)

Sorry, that still doesn't really make sense to me. If you can't trust the generative model to produce code that does what it's supposed to do, then you also can't trust the adversarial model to perform the tests needed to determine that the code does what it's supposed to do. So if the results have no meaning, then the fact that you can objectively measure them also has no meaning.

[–] peanuts4life@lemmy.blahaj.zone 1 points 12 hours ago (1 children)

But you can trust the first model to produce the code you want it to. Or, at least get a baseline of whether it works as expected. To roll back to the simple example of secure (sanitized) user input via a form, the human sets up the testing environment. All the human needs to do is write a script that reads the entered database entry, and hashes the rest of the database / application in memory.

It should be simple for the first model to use different languages and approaches from strongly typed languages like ada to yolo implementations in Python.

The adversarial model's job is to modify the state of the application or database outside of that entry. This should be possible with some of the first models implementations, unless they are already perfect.

The idea is with enough permutations of implementations at different temperatures and with different input context, an almost infinite number of blue team and red team examples can be iterated on and produced on this one specific problem.

This approach is already being generalized to produce more high-quality software training data for LLMs than exist in the lexicon of human output.

This is very hard to do with art or writing. Art is subject, you can not validate the variable automatically or detect subtle variations without context and opinion so easily.

This is tangental to why Machine Learning works so well for weather data. We can objectively validate the output with historic data, but we can also create synthetic weather data using physically based models. It's different, but similar in principal.

[–] HetareKing@piefed.social 1 points 6 hours ago

I'm not sure that the comparison with the weather data works. Tweaking curves to more closely match the test data, and moving around a model's probability space in the hope that it sufficiently increases the probability of outputting tokens that fixes the code's problems, seem different enough to me that I don't know whether the former working well says anything about how well the latter works.

If I understand what you're describing correctly, the two models aren't improving each other, like in adversarial learning, but the adversarial model is trying to get the generative model to zone in on output that produces the user's desired behaviour based on the given test data. But that can only work as well as how much the adversarial model can be relied upon to actually perform the tasks needed to make this happen. So I think my point still stands that the objectivity of your measurements of the test results is only meaningful if the test results themselves are meaningful, which is not guaranteed given what's doing the testing.

How complex is the adversarial model? If it's anywhere near the generative model, I don't think you can have actual meaningful numbers about its reliability that allow you to reason about how meaningful the test results it produces are.