I am increasingly worried with people applying ML in everything without any rigour.
Statical inference generally only works well in very specific conditions:
1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong)
2 - Using (1), you calculate how much data you need so you get an estimation error below x%
Even though most ML models are essentially statistics and have all the same limitations (issues with convergence, fat tailed distributions, etc...) it seems the industry standard is to pretend none of that exists and hope for the best.
IMO the best moneymaking opportunities in the decade will involve exploiting unsecured IOT devices and naive ML models, we will have plenty of those.
Statical inference generally only works well in very specific conditions:
1 - You know the distribution of the phenomenon under study (or make an explicit assumption and assume the risk of being wrong)
2 - Using (1), you calculate how much data you need so you get an estimation error below x%
Even though most ML models are essentially statistics and have all the same limitations (issues with convergence, fat tailed distributions, etc...) it seems the industry standard is to pretend none of that exists and hope for the best.
IMO the best moneymaking opportunities in the decade will involve exploiting unsecured IOT devices and naive ML models, we will have plenty of those.