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Dive into Deep Learning

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Re: Dive into Deep Learning

#61

Post by jrumbut »

fareesh wrote: post #30
newfeatureok wrote: post #28

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…

Sorry but it was a scenario I imagined and not something that happened in reality. I can't talk about some of the real-world scenarios that I am asked to consult on, so I made up a rather poorly thought-out one.

Something to look at is the classic image processing algorithms that can be effective and more importantly behave predictably.

In your example, take a film of the factory floor when it is empty, then once work begins use a approximately human sized/shaped rectangular sliding window and look for areas that exceed a threshold of difference to the image of the empty floor.

You can then use that window as input to a classifier which will be easier due to the considerable dimension reduction or perhaps you can get sufficient performance using further deterministic techniques.

Re: Dive into Deep Learning

#62

Post by lowdose »

fareesh wrote: post #8

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…

It's about agile development. I would really like a write up how Google for example reduced the energy consumption in their data centers. I have a hunch 30% energy reduction was based on an insight on the specific causal relationship between demand and supply flows. This kicked-off a development sprint and eventually lead to a major energy reduction. A traditional waterfall project planning starting with a requirement to reduce 30% energy collapses before it starts.

Re: Dive into Deep Learning

#63

Post by [deleted] »

nolite wrote: post #59
mrspeaker wrote: post #52

Earlier quoted context omitted.

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.

Honestly, despite your facetiousness, this is the best starting point. And then from here work up to more complex solutions if there are reasons why rhis simple one isn’t suitable

[deleted]

Re: Dive into Deep Learning

#64

Post by mrspeaker »

newfeatureok wrote: post #53
mrspeaker wrote: post #46

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…

This solution would change the work place culture - and I 100% bet would lead to a lot of good (for MY definition of good!) supervisors leaving.

Imagine where you worked suddenly introduced this: "Yes, previously everyone could wear whatever they wanted - but from today, just the senior programmers must code while wearing a high-vis jacket around the office so we can track when they at their desks".

The supervisors have now changed their relationship with coworkers - signaling their superiority, while simulataneously feeling stalked by their bosses, and looking "unfashionable"/un-cool - all because someone couldn't figure out how to do deep learning properly... which was the OP was actually asking about!

Re: Dive into Deep Learning

#65

Post by Gatsky »

newfeatureok wrote: post #28
fareesh wrote: post #8

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…

Reminds me of this:

https://userweb.cs.txstate.edu/~br02/cs1428/ShortStoryForEng...

Re: Dive into Deep Learning

#66

Post by amelius »

1MoreThing wrote: post #49
mrspeaker wrote: post #46

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."

The problem with supervisors was just an example. The person asking the question isn't served by simplifying the problem, because clearly they are after a more general solution.

Re: Dive into Deep Learning

#68

Post by newfeatureok »

mrspeaker wrote: post #64
newfeatureok wrote: post #53

Earlier quoted context omitted.

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…

This solution would change the work place culture - and I 100% bet would lead to a lot of good (for MY definition of good!) supervisors leaving. Imagine where you worked suddenly introduced this: "Yes, previously everyone could wear whatever they wanted - but from today, just the senior programmers must code while wearing a high-vis jacket around the office so we can track when they at their desks". The supervisors h…

I really don't understand your comment.

1. Supervisors are already by definition "superior" than their subordinates.

2. Supervisors on factories already wear distinctive clothing - especially in fully automated factories.

Finally, you have yet to propose a solution to the problem yourself that would be highly accurate and easy to train. You vastly underestimate the difficulty to create a bespoke solution from scratch and no data.

In any case since the supervisor thing was just an example - the original poster's only real choice is to manually label everything, but AI is really problem centric so it's hard to recommend anything without knowing the actual problem. Assuming it really is just [someone in an area for a period of time] kind of problem, and the difficulty is picking apart the 'someone' and you cannot influence their behavior, you just need massive amounts of data. Even then there's no guarantee you'll have high accuracy.

If high accuracy is required the problem itself needs to be examined on a higher level.

Re: Dive into Deep Learning

#69

Post by newfeatureok »

SubiculumCode wrote: post #55
newfeatureok wrote: post #28

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…

This is not bad, but once in this territory, why not just add some tracking beacon to a badge?

That is also a good idea. It really depends on what the rest of the requirements are.

Re: Dive into Deep Learning

#70

Post by newfeatureok »

mrspeaker wrote: post #52
1MoreThing wrote: post #49

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.

This wouldn't work as there is a time requirement of 20 minutes. A solution to this would have to be real-time and not require one to manually log their presence, which would defeat the whole point.
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