51–60 of 94 posts
Earlier quoted context omitted.
"Easily solved - just have them wear special clothes." Everything is easy if you can arbitrarily change the requirements!
This is good problem-solving. Why spend tens (if not hundreds) of thousands of dollars building technology to do a complicated task if you can cut that effort in half or more by having somebody where a funny vest? Remember, the problem is "I need to know when I don't have two managers on the floor," not "how do I use machine learning to know when I don't have two managers on the floor."
If we can make up arbitrary rules and assumptions then just have them jot down on a piece of paper when they come and go, and if they are the last to leave then they have to send an email.
Earlier quoted context omitted.
I'm somewhat surprised at the responses for this. I believe your issue can be easily solved - have supervisors wear a distinctive color from a non-supervisor. For example let's say it's yellow. OK so now you have yellow wearing supervisors and everyone else. To resolve the issue you have described acquire a month or so of footage, with labels per minute describing how many yellow wearing supervisors and how many peop…
"Easily solved - just have them wear special clothes." Everything is easy if you can arbitrarily change the requirements!
The general point is to capitalize on preexisting information than to do the "true" solution which is error prone and even a human might not have 100% accuracy at, due to the fact that in certain settings (such as this hypothetical) the perfected solution cannot be accomplished without constraints.
As an engineer I find myself in this type of situation quite often - if anyone can point me to some good resources or has any advice, I'd be quite grateful: - Some non-technical stakeholder comes to me and says "can we solve this problem with Machine Learning?" usually it's something like "there need to be two supervisors on the factory floor at all times, and I want an email alert everytime there are less than 2 sup…
I'm somewhat surprised at the responses for this. I believe your issue can be easily solved - have supervisors wear a distinctive color from a non-supervisor. For example let's say it's yellow. OK so now you have yellow wearing supervisors and everyone else. To resolve the issue you have described acquire a month or so of footage, with labels per minute describing how many yellow wearing supervisors and how many peop…
Earlier quoted context omitted.
This is good problem-solving. Why spend tens (if not hundreds) of thousands of dollars building technology to do a complicated task if you can cut that effort in half or more by having somebody where a funny vest? Remember, the problem is "I need to know when I don't have two managers on the floor," not "how do I use machine learning to know when I don't have two managers on the floor."
This particular problem is "I need to know when I don't have two managers on the floor, and they aren't always wearing funny vests just because the computer guys are bad at deep learning". If we can make up arbitrary rules and assumptions then just have them jot down on a piece of paper when they come and go, and if they are the last to leave then they have to send an email.
Earlier quoted context omitted.
"Easily solved - just have them wear special clothes." Everything is easy if you can arbitrarily change the requirements!
The requirements were not changed. Supervisors of almost every working class position already wear different clothes to begin with. Heck, even doctors wear different clothing than nurses, teachers than students, coaches from athletes, etc. The general point is to capitalize on preexisting information than to do the "true" solution which is error prone and even a human might not have 100% accuracy at, due to the fact…
As an engineer I find myself in this type of situation quite often - if anyone can point me to some good resources or has any advice, I'd be quite grateful: - Some non-technical stakeholder comes to me and says "can we solve this problem with Machine Learning?" usually it's something like "there need to be two supervisors on the factory floor at all times, and I want an email alert everytime there are less than 2 sup…
Earlier quoted context omitted.
This is good problem-solving. Why spend tens (if not hundreds) of thousands of dollars building technology to do a complicated task if you can cut that effort in half or more by having somebody where a funny vest? Remember, the problem is "I need to know when I don't have two managers on the floor," not "how do I use machine learning to know when I don't have two managers on the floor."
This particular problem is "I need to know when I don't have two managers on the floor, and they aren't always wearing funny vests just because the computer guys are bad at deep learning". If we can make up arbitrary rules and assumptions then just have them jot down on a piece of paper when they come and go, and if they are the last to leave then they have to send an email.
As an engineer I find myself in this type of situation quite often - if anyone can point me to some good resources or has any advice, I'd be quite grateful: - Some non-technical stakeholder comes to me and says "can we solve this problem with Machine Learning?" usually it's something like "there need to be two supervisors on the factory floor at all times, and I want an email alert everytime there are less than 2 sup…
In general, it's much better to not use machine learning at all if at all possible.