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by IanSanders 2332 days ago
Didn't read the book yet, but from your description absolutely agree on the trickiness of the problem - it almost requires solving the "proper artificial intelligence" problem first, like you say.

However, I imagine it could be applied to refine an existing question? There certainly exist "obviously poor" questions on SO, and it's a good first step to make otherwise poor question "look" like a good question - trivial things like formatting and misuse of the language. It won't get other, high-level attributes of a genuinely good question however, but some poor questions are poor in just that - formatting and language, the "requires editing" queue.

Regarding "intrinsically poor" questions, on the other hand, if everyone used the described model, readers would now have an increased cognitive load to distinguish between good and poor questions. Over time, the described model would drop in performance, as the "typical good question attributes" are used in poor questions which wouldn't have those otherwise.

(Forgive me for trivialising the concept of the quality of a question)

It's still a very interesting problem for a book. It's just as suitable for demonstrating the model development process, and it's likely very relevant to the vast majority of the readers (I imagine).
1 comments
e_ameisen 2331 days ago
Yes, this is the approach in the book. The concept of question quality is nuanced, and does not have a clear definition. It can be easy to feel like you've solved the problem by just throwing in ML and calling it a day, but producing something useful is a real challenge.

The book covers multiple aspect of that process, from choosing an ML approach that isn't too simple or ambitious, to iterating on a model within the context of its final use case (i.e rather than only optimizing for a metric, testing how the model helps with its end goal).

In my experience, I've found that it is often those challenges that make or break the quality of an ML product, so the book focuses on tools to make complex problems more tractable, and less risky.

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