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That was my thinking - anything of value is product-specific and behind closed doors. It's not my field, but something I see come up from time to time that seem
by lmc 5y ago
That was my thinking - anything of value is product-specific and behind closed doors. It's not my field, but something I see come up from time to time that seems weirdly over-represented in ML articles.
- samhw 5y agoI work on these systems, and if anything my only complaint about the field is the propensity to solve every optimisation problem with ML. I have seen people solve textbook-grade linear, and even differentiable, optimisation problems. And the reason it happens despite the 'invisible hand' etc is because it still works, it just happens to be horrendously inefficient. I think that's the main area of inefficiency in the industry: not in getting the job done, nor even arguably in accuracy - at least not severely - but in overcomplicating the solution[0] because we've formed a cargo cult around one particular method of optimisation, beyond all nuance. [0] I mean 'overcomplicating' in absolute terms. Of course the very crux of my point is that, from the data scientist's perspective, it's not overcomplicated - it's less complicated than using e.g. ILP precisely because we have made libraries like TensorFlow so incredibly easy and tempting to use.
- whimsicalism 5y agoFwiw, my org heavily relies on LP solvers in conjunction with ML to solve these problems
- monkeybutton 5y agoAcademia thrives on open benchmark that researchers compete against each other to get the highest score on. How would you replicate that with a recommendation system where in industry you test recommendations by variants A/B/... and observe the winner? Your benchmark is no longer static and you rely on users' feedback for the recommendations each algorithm made.