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auai·Actually Useful AIbybabelspace

Neural Networks Need Data to Learn. Even If It’s Fake.

I’ve been following the development of the next Stable Diffusion model, and I’ve seen this approach mentioned.

Seems like this is a way in which AI training is analogous to human learning - we learn quite a lot from fiction, games, simulations and apply this to the real world. I’m sure the same pitfalls apply as well.

Neural Networks Need Data to Learn. Even If It’s Fake.https://www.quantamagazine.org/neural-networks-need-data-to-learn-even-if-its-fake-20230616/Open linkView original on kbin.social
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auai·Actually Useful AIbybabelspace

Why artificial intelligence needs to understand consequences

This is the potential development in AI I'm most interested in. So naturally, I tested this when I first used ChatGPT. In classic ChatGPT fashion, when asked to make a directed acyclic graph representing cause and effect, it could interpret that well enough to make a simple graph...but got the cause and effect flow for something as simple as lighting a fire. Haven't tried it again with ChatGPT-4 though.

Why artificial intelligence needs to understand consequenceshttps://www.nature.com/articles/d41586-023-00577-1Open linkView original on kbin.social
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