[overkalix]

> OR is perfect when you can describe explicitly what the decision space is and what the restrictions are.

As opposed to having to figure it out later from the outputs of a black box?

> Quality control with machine vision is a good application for ML.

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.

> NLP for PDF documents is a huge field for manufacturing as well.

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?

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.

[whatever1]

> 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.

[nightski]

There is a significant amount of research from the field of computer vision before ML even existed. It was quite robust as well within certain constraints. Those techniques simply did not generalize anywhere even close to as well as deep learning.

However, that said, in a tightly controlled environment such as a manufacturing line trying to spot defects I would imagine they would have a good chance at performing a lot better than deep learning.

A lot of the advancements in deep learning have also come out of ideas from that research. While they didn't use the techniques directly, there is a lot of knowledge that we'd be lost without.

This is one thing that scares me about ML. We are losing research into the fundamental physics/science to deeply understand these things and instead just throwing models at them.

[noiwillnot]

I work in this space: most "cool" ML is useless, and stakeholder are very skeptical of new modelling techniques. It is a long slog of EDA and finding actionable causality. Deep learning, modern reinforcement learning... are not the best fit here.

However I have seen CV and NLP useful here and there... but it is not the bread and butter.