this post was submitted on 04 Apr 2024
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Meta’s AI image generator is coming under fire for its apparent struggles to create images of couples or friends from different racial backgrounds.

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[–] jedibob5@lemmy.world 45 points 7 months ago* (last edited 7 months ago) (11 children)

I hate that the focus of AI/ML development has become so fixated on generative AI - images, video, sound, text, and whatnot. It's kind of crazy to me that AI can generate output with the degree of accuracy that it does, but honestly, I think that generative AI is, in a sense, barking up the wrong tree in terms of where AI's true strengths lie.

AI can actually turn out to be really good at certain kinds of problem-solving, particularly when it comes to optimization problems. AI essentially "learns" by extremely rapid and complex trial-and-error, so when presented with a problem with many complex, interdependent variables in which an optimal solution needs to be found, a properly-trained AI model can achieve remarkably effective solutions far quicker than any human could, and could consider avenues of success that humans otherwise would miss. This is particularly applicable to a lot of engineering problems.

Honestly, I'd be very intrigued to see an AI model trained on average traffic data for a section of a city's street grid, taken by observations from a series of cameras set up to observe various traffic patterns over the course of a few months, taking measurements on average number of cars passing through across various times of day, their average speed, and other such patterns, and then set on the task of optimizing stoplight timings to maximize traffic flow and minimize the amount of time cars spend waiting at red lights. If the model is set up carefully enough (including a data-collection plan that's meticulous enough to properly model average traffic patterns, outlier disincentives to keep cars at little-used cross streets from having to wait 10 minutes for a green light, etc.), I feel that this sort of thing would be the perfect kind of problem for an AI model to solve.

AI should be used on complex, data-intensive problems that humans can't solve on their own, or at least not without a huge amount of time and effort. Generative AI doesn't actually solve any new problems. Why should we care if an AI can generate an image of an interracial couple or not? There are countless human artists who would happily take a commission to draw an interracial couple (or whatever else your heart desires) for you, without dealing with investing billions of dollars into developing increasingly complex models built on dubiously-sourced (at best) datasets that still don't produce results as good as the real thing. Humans are already good at unscripted creativity, and computers are already good at massive volumes of complex calculations, so why force a square peg into a round hole?

[–] gbzm@lemmy.world 1 points 7 months ago* (last edited 7 months ago)

That's absolutely true, generative AI is mostly a parlor trick with very few applications beyond placeholder art and faster replies to emails. But even for your kind of engineering problem, there's still a big issue that's often disregarded.

If we keep your example of an AI for a city grid, an important aspect of this type of engineering problem is guaranteeing that the system has as few catastrophic failures as possible (usually guaranteeing less than 1 for every 10^9^ hours of uptime for systems where catastrophic means a certain quantity of dead bodies or high monetary costs, like a city grid, train signalization, flight control...). AI models may very well end up being discarded in those problems because even if you observe a better accuracy in simulations and experiments, mathematically proving this 10^9^ figure is impossible because we don't know how they work. Proving a threshold experimentally can happen, but a 10^9^ number would require something like centuries of concurrent testing in every city in the world... I've just had a class with this example for trains. They were testing a system that reads signalization with a camera in order to move towards a more autonomous train. Deep learning performed better that classical image processing, but image processing allows you to prove that the train won't misread less than x% of the time with way higher certainty than a black box, so they had to go with that if they ever wanted to pass safety certifications.

So I guess deep learning explainability might be a more significant challenge even that finding a dataset that isn't racially biased...

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