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by StuffedParrot
2359 days ago
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> So uhhh, isn't this like not science? Very little of technology has anything to do with validating hypotheses. > meaning that humans, even those who design them, cannot understand how variables are being combined to make predictions. The intention is to not rely on the explanation to evaluate the effectiveness of the model. This does not preclude any of the infinite narratives that might explain the model. This is fundamentally a cost saving mechanism to avoid hiring engineers to code heuristics useful to business. There is nothing related to science here at all. A "black box" model is fashionable to those who prefer to observe and not create meaning, even if the observed meaning is deeply flawed from a human perspective. After all, people spend money based on less all the time. |
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1. It is hard than it should be to explain the concept to people (particularly VCs) 2 . people struggle to understand that a mechanistic model could have more utlity than a machine learning black box 3. people think you are doing something wrong if you are not using a neural network 4. The less people understand about neural networks, the more they seem to believe they are appropriate for all predictive / modelling problems 5. There is generally quite a low understanding of scientific method in the startup / VC space (speaking as someone who has worked in and around academia for years) vs how "scientific" people believe they are because it sounds good to be data driven and scientific about running startups and funding them.