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Comment by petra | Hacker News Reader

>> Generative adversarial networks (so computers can get human like abilities at generating content)

Does this only apply to artistic content, or also to engineering content ? Say PCB layouts, architectural plans, mechanical designs, etc ?

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Artistic only. You'd need an exact verifier in case of engineering designs, and something built with neural networks is not one.

To get a better understanding (other than reading a paper) read this excellent blog post https://openai.com/blog/generative-models/

GANs are a general tool -- they just happen to get a lot of attention for generating images of stuff. Here's an example for generating sequences [1]. The example is language oriented, but ultimately GANs are interesting because you can use them to build a generator for an arbitrary data distribution. This can have many applications in engineering (to take a random example -- generating plausible looking chemical structures under a certain set of constraints). As with any ML application, you need to quantify your tolerance for "inaccuracy" (in a generative setting, how well the generated distribution matches the true data distribution). This is simply an engineering trade-off and will vary based on the application.

[1] https://arxiv.org/abs/1609.05473

The approach was applied without any real knowledge of art, even though it has been applied to the domains you mentioned I don't see why not.

[edit]: it is a lot harder to build a NN when there are very constraint rules. But it is also a lot easier to verify and penalize it and generate synthetic data.

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