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This paper's conclusion (I only read the abstract) jives with my experience. Whenever I've tried to make something "intelligent" it always ends up in headaches. Now-a-days when building code, I try to stay away from having the computer make decisions. I've found its much easier to build stuff in a way that put a real human in charge of making all decisions.

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Unfortunately, thats not scalable for a whole bunch of problems. I agree its a headache, but the solution is not to stay away from machine intelligence.

I understand what you mean. Consider though, not everyone here is writing code that needs to scale.

What I meant was for projects that need to scale, i.e. you can't have humans making decisions, there is no way to avoid this. If your project does not need to scale in this manner, my statement doesn't apply - in fact, I quite agree with you - I'm not sure why one would use machine learning!

It is fine (good even!) to design things in a way to let humans make the intelligence-required decisions, as that becomes a nice modular interface for intelligent optimization systems that can be introduced later. It also provides a nice heuristic: If your human interface doesn't expose the data used to make a computationally optimal intelligent decision, then it isn't a good human interface. And it works quite well with Agile principals. If you iterate on that interface until the humans start to like the interface, then that is a good signal that it is time to start introducing intelligent optimization. Some of the most groundbreaking intelligent systems at Amazon have followed the same principles.

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