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Are the non-AI models any more “explainable?” Models built on multivariate statistics, processing terabytes of data a day, spitting out numbers might be “unders
by chatmasta 8y ago
Are the non-AI models any more “explainable?” Models built on multivariate statistics, processing terabytes of data a day, spitting out numbers might be “understandable” in the sense that there is some discrete representation of how their inputs map to outputs. But can anyone really look at those algorithms and explain why they work? What’s really the difference between NN and advanced statistical regression, beyond differing levels of familiarity/comfort?
- bitL 8y agoBuzzword bingo is more difficult to play with NNs than throwing some (misunderstood) p-values around for people that understand a bit about stats/optimization/etc. but aren't at the bleeding edge (i.e. a typical MBA with an outdated tech degree).
- raxxorrax 8y agoCame here for fluid mechanics. Don't know if my tech degree is outdated already, but I certainly skipped getting an MBA. At least I wondered a bit as to why BlackRock is into fluidity simulations too.
- bitL 8y agoBlack-Scholes computed on NVidia Teslas, right? If your tech degree is not in computer science, it's likely not outdated. If it is in CS, then it most likely is.
- gbrown 8y agoI didn't read the whole article since I didn't want to sign up, but in general: maybe. Machine learning models are really good at things like prediction, but if it's valuable to do inference about the phenomenon (e.g., is there evidence that X is positively associated with the odds of Y, given Z,Q,R), careful study design and appropriate statistical models are a better choice. These come with theoretical underpinnings - whether that's the coverage guarantees of frequentist methods or the decision-theoretic foundations of Bayesian inference. I'm not sure whether or not that means this choice was good on the part of BlackRock, however.