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This seems to kind of be the opposite opinion of The Bitter Lesson[0]. [0] http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas

• jpace121 3y ago
  I don’t think it’s really the opposite opinion. To pick on the game of Go as an example, the insight from the Bitter Lesson is the best method to use for Go is probably going to be the most broad method that abuses the magic of brute force the most, compared to a method that really encodes the best existing strategies of Go. I think what OP is referring to and what I’ve observed is that in some fields you have to have a certain amount of expertise to just understand the rules of the game, and there are a lot of applications where someone approaching a problem with the goal of applying ML can’t quite get over the hump of understanding all the rules.
• sidibe 3y ago
  Someone still needs to have enough domain knowledge to figure out what needs solving and how to validate that it was solved. Being able to frame problems, and being able to cobble together the data and infra for the AI to solve them are the most valuable things in the medium term.
  • bigger_cheese 3y ago
    Yes I think having domain knowledge (such as Chemistry, Physics etc) is invaluable for real world problem solving. I am a Materials Engineer I see it in my field with ML as well. I think people are starting to understand now that being able to validate the inputs and outputs requires some understanding of the process you are trying to model. i.e. To ensure you aren't proposing things that fundamentally contradict basic Thermodynamics etc. Even for optimization type modelling you need domain knowledge to design sensible constraints to frame the problem.