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Who cares if you overfitted? See, the model has 100% success rate vs the training set! Who cares if it denies bail to minorities or hits a few pedestrians fro
by efitz 5y ago
Who cares if you overfitted? See, the model has 100% success rate vs the training set!
Who cares if it denies bail to minorities or hits a few pedestrians from time to time?
The problem isn’t that ML is too hard, it’s that it’s too easy. Crazy people keep connecting ML to systems that matter- that have real, irreversible impact to humans- and they don’t understand it.
I wish ML were 1000x harder/more expensive to integrate so the economics would drive away frivolity.
- counters 5y agoThis is exactly right. I've seen it time and time again: Team has a black box ML/AI solution to a "problem." Team wants to eke out better P/R or deal with some complex edge cases. But team's problem is fundamentally ill-posed and no amount of hacking or kludges will actually produce the success criterion that they need. The problem is the accessibility to these tools, which in many times has led folks to neglect the subject matter expertise required to effectively apply them in the first place. At least as these tools catch on in popularity in myriad problem domains, there will be a new generation of subject matter / domain experts who are familiar with them, and we'll probably jump over this hurdle.