[rp1]

Your experience rings true to me. It's one of my biggest frustrations with ML at the moment. There are so many ideas I'd like to try, but I know only a small fraction of them will work, and discovering which of the ideas fail and which succeed is a herculean task. Your conclusion that empathy helps may also be true, but I have a different take.

It currently takes way too much time to explore ML-based ideas. I compare this to the early days of computer programming where programmers needed to manually fill out punch cards, and doing anything took days of full time work. There is lots of room for improvement along every step of the ML pipeline, from data wrangling, model choice, training, and evaluation. Good ML tooling will likely bring huge gains in the field.

[iroh2727]

Yep I think you hit another nail on the head (likely the bigger nail).

There is a whole 'nother category of ideas I wasn't thinking about which are the ideas that seem promising but just like way too much effort to even test.

Those ideas just get completely lost when it's hard to test ideas.

[chroem-]

It gets better with practice as you develop more accurate intuition. Eventually, most new ideas that you try will work, but it takes a lot of failures to get to that level.

[rp1]

Maybe… but the article had an anecdote of asking Ian Goodfellow for ideas and none of those ideas working. I would assume Ian Goodfellow would have the requisite amount of experience for sufficient intuition.

[webmaven]

> I would assume Ian Goodfellow would have the requisite amount of experience for sufficient intuition.

Well, maybe. It isn't entirely clear just when that anecdote happened (he was at Google first as an intern, then as a research scientist, then he was at OpenAI before going back to Google).

In any case, as important as a developed intuition is, it is no guarantee that it will provide the necessary insight after hearing a description of the problem rather than experiencing and digging into it himself.