> As opposed to having to figure it out later from the outputs of a black box?
Not all problems can be formulated as a set of explicit equalities, constraints and variables (e.g. machine vision). If explicit modeling is an option, of course you should do it. I am seeing efforts to try reinforcement learning on systems that we know how to describe with equations, and of course the results are laughable compared to the traditional methods.
> I can't imagine CV could be an actual replacement for actual SPC in many industries. There's a reason we need to take samples and stress test, analyze composition, etc.
In one big manufacturing company they were using Machine vision and a cheap web camera to control flaring. Could they do it with fancy sensors instead? Of course, but it would be more expensive, and they never did in the past.
Another manufacturing company is using machine vision to raise an alarm if the door of a cargo car of a train is not closed after loading. Could they install sensors in all of the doors of the train instead? Sure, but it would be cost prohibitive.
>NPL could be big everywhere... if it provides actual value, which is not a given. ML has a lot of tangential applications (you could also say, better forecasting), but how will directly improve manufacturing processes?
In manufacturing we have multiple people opening pdfs from emails to copy contract numbers to excel spreadsheets. Others are getting orders in emails and then type them in SAP manually. I think that these tasks can be automated specially with the recent versions of NLP networks.
>I apologize for being abrasive, but I'm so tired of cs people descending upon all industries, plugging shit data into pytorch and doing shitty ML like it will automatically add value. Even more so in industrial engineering, which in my experience is full of people way better at math than computer scientists and requires a deep understanding of the product and the manufacturing process.
All is good :) There has been a lot of unsubstantiated hype in ML, made even worse by big consulting companies and cloud providers who just sell the hype.
