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Recurring expenses (i.e. the kind that will generate enough data to adequately train a ML system) are already handled pretty well by most businesses through tra
by variaga 7y ago
Recurring expenses (i.e. the kind that will generate enough data to adequately train a ML system) are already handled pretty well by most businesses through traditional (non-ML) methods. Operations Research has been a thing for 60+ years. Businesses that don't handle their recurring expenses well now are likely to have organizational issues (e.g. senior management that ignores the advice of their reports on how to do things) that will prevent them from doing so even if ML is added to the mix.
The place where businesses get in real trouble (and hence would see significant value from better decision making) is when doing things that they have _not_ done 10K+ times before. Things like "Should we expand into $NEW_MARKET?". ML isn't going to help them with that, because there will not be any useful historical data to train them with.
- solidasparagus 7y agoI completely disagree with the assumption that because something has been done for 60+ years it won't be improved by new, extremely relevant technologies. Amazon is very effective at both OR and organizational dynamics, but they found massive savings by using ML to predict demand and thus inventory/costs (Research is here - https://arxiv.org/pdf/1711.11053.pdf https://arxiv.org/pdf/1711.11053.pdf).
- variaga 7y agoUnless I missed it, the referenced paper doesn't quantify what savings Amazon achieved, or even if Amazon actually used the NN described. It does not support the statement "found massive savings".
- solidasparagus 7y agoNo, that was from hearing them present their work. But I do think the GEFCom2014 Electricity Forecasting benchmark is pretty clear proof that ML solutions such as these can improve expenditure decision making compared to established techniques.