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[–] 9 points 2 days ago (2 children)

Give it few weeks, and they have collected meteics that the AI did the job just as good as the human within that timeframe. A few weeks down the line, the tables have turned, the humans are in charge of notifying the AI if it makes errors. A few weeks later, metrics will show that humans hasn't really notified anything, and humans are removed from their position, leaving the AI fully in charge.

Within a year, a critical halucination or a job which AI lacks experience to handle will happen and a very preventable accident will occur. The owners will shrug it off as something that would definitely happen with humans in charge, and that the reduced costs outweigh the additional risk.

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  • [–] 4 points 2 days ago (1 child)

    "Hallucinations" are produced by LLMs, which wouldn't be appropriate for air traffic control. Automations of this type are done with Symbolic AI (aka "classical" or "logical" AI). Symbolic AI is built from sets of rules and conditions used by human beings to make the same decisions. This type of AI has been widely used in all kinds of control systems since the 1970s, and is completely unrelated to ChatGPT and other LLMs except by being called "artificial intelligence".

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  • It'll probably be a deep learning model - some time series model that can factorise multiple input features like an RNN or LSTM. Those things work well within certain tolerances of their training data and are significantly more appropriate for this sort of thing than an LLM but as soon as something unusual happens they can react very weirdly - hopefully there are deterministic programmed guardrails that kick in and summon a human in edge cases.

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