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A major point that's underappreciated in academia is quality of execution. The more complicated a model is, the less well you can execute it. Academics are rew
by soVeryTired 9y ago
A major point that's underappreciated in academia is quality of execution. The more complicated a model is, the less well you can execute it.
Academics are rewarded for building complicated things, but it often takes graduate PhDs entering industry a long time to learn about quality of execution. I think that's the fundamental tension between business and academia.
- ska 9y agoI wanted to say that I disagreed with you, but then I realized I don't know what you mean by "execution" here, though. This statement "The more complicated a model is, the less well you can execute it" is fishy though. The typical problem in practice with complicated models is the data quality and quantity to support it, not the implementation or interpretation. I suppose here we also need to define "complexity" - I am using this to mean roughly number of parameters.
- jackcosgrove 9y agoI took "execution" here to mean "scaling up". In a trivial case, a model may fail to scale up if it's computationally too slow or too sensitive to noisy data. Sometimes these shortcomings cannot be tested in a laboratory environment with limited data sets.