It variates, and it's in the earl stages. One example we've done is sort through 500.000 personal cases, containing multiple documents, some documents containing up to 50 scanned pages to find cases missing a specific law required form.
Once machine learning has OKed a case we used a software robot to archive it automatically.
Because it was an experiment and also a job that had to be done. We did it simultaneously with as a competition of man vs machine.
The man power was a team of expienced case workers working on it 37 hours a week. The machine was two new teams, one in RPA and one in machine learning.
Both proceses ended up taking roughly 3 months, but the "tech" team spent a lot of the time learning how to use the technology and quite a bit training the algorithm.
Once the tech team and algorithms were ready, the actual processing time took 5 hours in the Azure cloud.
Now we have a process for finding specific documents, however, and I'm told it'll take 1-2 weeks to retrain it, meaning it'll be 1-2 weeks + 5 hours vs 3 months next time.
> One example we've done is sort through 500.000 personal cases, containing multiple documents, some documents containing up to 50 scanned pages to find cases missing a specific law required form.
Forgive my ignorance, but how is this not a case for OCR + indexing? I assume that the form would have some specific texts that can be found once it's processed with OCR, what is there to machine learn?
You're correct, it was mainly OCR, but we categorize this as machine learning. Partly because we use public machine learning algorithms to do it, but also because we're planning to do a lot with OCR coupled with other data sets and a range of IOT initiatives. Things like predicting where we'll need to water trees, prioritize areas for snow shoveling or schools for renovation, how to control traffic flows and a range of other things.
We are doing some more traditional stuff in BI, but a lot of it is relatively secret, in the very early stages or even getting shut down because legislation in the area is changing rapidly.
We tracked citizen movement in our inner city based on wifi from smartphones for instance. Then we compared the data with various attempts at directing crowd flows. With a decent success rate, much higher than before we started using machine learning to score the results. We've killed the project though, because the new EU privacy protection acts makes that sort of wifi tracking illegal.
Once machine learning has OKed a case we used a software robot to archive it automatically.
Because it was an experiment and also a job that had to be done. We did it simultaneously with as a competition of man vs machine.
The man power was a team of expienced case workers working on it 37 hours a week. The machine was two new teams, one in RPA and one in machine learning.
Both proceses ended up taking roughly 3 months, but the "tech" team spent a lot of the time learning how to use the technology and quite a bit training the algorithm.
Once the tech team and algorithms were ready, the actual processing time took 5 hours in the Azure cloud.
Now we have a process for finding specific documents, however, and I'm told it'll take 1-2 weeks to retrain it, meaning it'll be 1-2 weeks + 5 hours vs 3 months next time.