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Dylan_TMB t1_j1jew6k wrote

The easiest steps are this:

  1. Have domain knowledge in a very specific area that is niche.

  2. Find where AI hasn't been applied yet in that niche.

  3. Apply AI in that specific domain of the niche.

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[deleted] OP t1_j1ksica wrote

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Dylan_TMB t1_j1l7g1o wrote

That's kind of the secret sauce you don't know what you don't know. If you could just know what that niche is then people would just be doing it. You often have to already have domain knowledge in some areas.

An example may be GIS stuff (spatial imaging and mapping data). If you know a lot about environmental sciences and geology then maybe there is some interesting problem to be solved in that field and you can be the first to do it. But it requires you to know the problem.

There is no such thing as an easy to find problem that will also give quick results. If it exists it will be done already. If you don't have domain knowledge then you're out of luck and will have to put the work into getting more cross discipline knowledge or innovating on the archetecure side of things.

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[deleted] OP t1_j1l7z2n wrote

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Dylan_TMB t1_j1l8o93 wrote

Maybe but I think these problems fall into a unique category. Those that know of the problems but don't know ML won't know how to ask or how to articulate them as ML problems. Those with ML knowledge but don't know if the problems obviously don't know they exist. Those that know the problem and ML are solving the problem themselves most of the time.

I think a list of problems is a nice idea but again I don't think those with knowledge of problems know what is a good ML problem and what isn't.

I will say I think a good blueprint for looking for these problems is to find a problem where the data for the problem is similar to a well understood problem.

For example if a problem in a niche can be framed as a seq2seq problem you can use translation models to try and solve it.

Another good one is trying to find problems that can be framed as a game. Reframing problems as games to use reinforcement learning is a good project.

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111llI0__-__0Ill111 t1_j1zbyu6 wrote

Would you say lack of domain knowledge is what makes stats and CS not always the best majors for ML/AI? It seems like problem formulation is critical for applied modeling and this stuff is not easy to learn if all you learn is tools

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