4 ms·
Point is there is an explosion of ML usage in soft sciences and applied industries who treat the tools as a black box. On the other hand, extreme reliability is
by DrNuke 10y ago
Point is there is an explosion of ML usage in soft sciences and applied industries who treat the tools as a black box. On the other hand, extreme reliability is really not needed there, it's just a lot of non-math or basic-math trained people messing around with stats, R packages and novel jargon.
- DrNuke 10y agoAnother point is the overreaction among the cognoscenti: so many words about open source leveraging the tools and allowing the masses to focus on real-life problems, then the rage against the end-users mocked as data-monkeys? Most industries do not need data science at all and, if the case, a very simple 80/20 approach solves all their problems. It happens the 80/20 approach is within the reach of every data-monkey able to clean and normalize datasets, set up Anaconda with Scikit, theano, xgboost, do some ensembling and deploy to AWS for semi-intensive tasks. You all as an industry wanted that for years, so what now?