Post #3166136
2025-08-12 06:21 UTC
@kevlin@mastodon.social That’s true. However, there is value in stochastic results in some cases. To give an example from a different industry: Antigen tests have a false negative and false positive rate > 0. Still they are very useful. When you need an exact result you do a PCR test that takes longer to complete and is much more expensive.
Similarly, LLMs could be useful (and I’m ignoring the moral implications here - obviously this is non-trivial) for roughly evaluating if e.g. an insurance claim is valid or not. Only on edge cases or when a customer complains, a humans looks at it. This is non-deterministic, could however save money in the long run, and also give faster responses to the customers.
Replies (1)
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@DJGummikuh@mastodon.social 2025-08-12 06:42
@sigmasternchen@comfy.social @kevlin@mastodon.social the problem with your insurance example however is again that the insurers would require at least statistical consistency in their AI's prognosis of eligibility as they need to revolve their risk management around this. If, unpredictably, the AI suddendly fucks up and approves every claim due to training data hiccup, the damage might've already been done before they can correct. In the end, the overall reliability in society takes a hit everywhere AI is employed.