6 ms·

I would argue that we might be in a Research bubble(who knows.. I don't have a clue) but we are definitely not in "using the ML for practical problems bubble".

by tlear 8y ago

I would argue that we might be in a Research bubble(who knows.. I don't have a clue) but we are definitely not in "using the ML for practical problems bubble". Amount of industries and businesses that can benefit not from the state of the art but from stuff that been known for years now and just works because it was developed for harder problems is huge. The big return/promise companies are vacuuming up all the talent while niches all over the place can benefit from someone spending few month cleaning up data and using some transfer learning to save them a ton of $$. For these application you don't need PhD you need an engineer who knows how to ship stuff but on other hand also knows how to work with current DL frameworks.

ackbar03 8y ago
This sounds good on paper but I don't feel we see all that many real examples of this sort of model (engineer + niche problem) or at least haven't heard of them. But then again if we did I guess that opportunities already gone or saturated.
- tlear 8y ago
  Different areas of manufacturing for example. All that computer vision, now apply it to X-rays of metal parts used to try and detect failures(not it is a guy looking at them all day using a dozen primitive filters, 8 hours a day every day). These are very domain specific, like liners of internal combustion engines, turbine blades etc On app side.. Cookpad added this neat feature last year, where it scans your pictures and adds to you cfood/cook log if it is food. The had a PhD do it, but this is because they want to do a lot more with it later I guess so building expertise and team. Food in general I think has a lot of neat computer vision apps that will happen eventually.
Jedi72 8y ago
There is a lot of old tech out there that could be implemented and would probably provide ROI, but isnt. So many businesses are still running on paper! My point is that industry isnt just sitting around waiting for new tech so they can get higher efficiency, there are reasons why not everyone is doing the latest and greatest (including ML) and they're usually valid.
eksemplar 8y ago
I’m less skeptical about this than I was about the blockchain bubble, but I’ve yet to see AI or ML actually work on a smaller scale. It’s a big hype in the public sector in these years, but it’s really all talk. We did a project where we used ML and 1000 server instances in Azure to go through millions of employee cases, to flag cases that didn’t have a certain document. Because the the is new, and this wasn’t something that could be delayed, we also had 10 employs do the same task to make sure it got completed. The ML project took 2 really expensive employs and 3 months to train the algorithm, then 5 hours to go through all the documents + we had to spend 1 week of 5 employs to clean up the stuff our algorithm had marked as “unreadable” due to terrible scans. The human employs did it in the same amount of time, but made a few more errors. Over all the ML was more expensive and our politicians won’t favor it again. On other things where ML might work, our datasets are turning out to be too small, unless we work together with other municipalities and then we’re facing GDPR violations that may be hard to pass through our legal team. Legal is a big issue on a lot of things, it’s not currently legal to automate any sort of process that require any form of validation (as long as it has to do with case working). That being said, I’m sure stuff like facial recognition will change the world.
- asfdsfggtfd 8y ago
  As a single batch job that won't be repeated this doesn't sound like a good candidate for ML. ML is more suited to on-going processes. Why would you use server instances in Azure to do ML? Something like Google CloudML (I'm sure that the other major cloud providers do managed Tensorflow as well I've just never tried it on their platforms) would be a better fit to a project with only two technical staff. Your two staff probably spent a combined total of one-person-month working on infrastructure. Your issue with small data is very real. People need to stop trying to do ML on small datasets. The results will be sub-optimal.
  - a008t 8y ago
    When you say ML, you must mean deep learning applied to unstructured data (vision, audio). ML in general can absolutely be used with small datasets. ML is all about finding the right model complexity to fit to the data to maximize out-of-sample performance. If your dataset is small, all that means is that your model will have to be more crude. A simple cross-validated regularized linear regression or a shallow decision tree are ML models too, and you can usefully apply them to a dataset of just 100 samples.

by tlear 8y ago

ackbar03 8y ago

This sounds good on paper but I don't feel we see all that many real examples of this sort of model (engineer + niche problem) or at least haven't heard of them. But then again if we did I guess that opportunities already gone or saturated.

tlear 8y ago
Different areas of manufacturing for example. All that computer vision, now apply it to X-rays of metal parts used to try and detect failures(not it is a guy looking at them all day using a dozen primitive filters, 8 hours a day every day). These are very domain specific, like liners of internal combustion engines, turbine blades etc On app side.. Cookpad added this neat feature last year, where it scans your pictures and adds to you cfood/cook log if it is food. The had a PhD do it, but this is because they want to do a lot more with it later I guess so building expertise and team. Food in general I think has a lot of neat computer vision apps that will happen eventually.

Jedi72 8y ago

There is a lot of old tech out there that could be implemented and would probably provide ROI, but isnt. So many businesses are still running on paper! My point is that industry isnt just sitting around waiting for new tech so they can get higher efficiency, there are reasons why not everyone is doing the latest and greatest (including ML) and they're usually valid.

eksemplar 8y ago

I’m less skeptical about this than I was about the blockchain bubble, but I’ve yet to see AI or ML actually work on a smaller scale. It’s a big hype in the public sector in these years, but it’s really all talk. We did a project where we used ML and 1000 server instances in Azure to go through millions of employee cases, to flag cases that didn’t have a certain document. Because the the is new, and this wasn’t something that could be delayed, we also had 10 employs do the same task to make sure it got completed. The ML project took 2 really expensive employs and 3 months to train the algorithm, then 5 hours to go through all the documents + we had to spend 1 week of 5 employs to clean up the stuff our algorithm had marked as “unreadable” due to terrible scans. The human employs did it in the same amount of time, but made a few more errors. Over all the ML was more expensive and our politicians won’t favor it again. On other things where ML might work, our datasets are turning out to be too small, unless we work together with other municipalities and then we’re facing GDPR violations that may be hard to pass through our legal team. Legal is a big issue on a lot of things, it’s not currently legal to automate any sort of process that require any form of validation (as long as it has to do with case working). That being said, I’m sure stuff like facial recognition will change the world.

asfdsfggtfd 8y ago
As a single batch job that won't be repeated this doesn't sound like a good candidate for ML. ML is more suited to on-going processes. Why would you use server instances in Azure to do ML? Something like Google CloudML (I'm sure that the other major cloud providers do managed Tensorflow as well I've just never tried it on their platforms) would be a better fit to a project with only two technical staff. Your two staff probably spent a combined total of one-person-month working on infrastructure. Your issue with small data is very real. People need to stop trying to do ML on small datasets. The results will be sub-optimal.
- a008t 8y ago
  When you say ML, you must mean deep learning applied to unstructured data (vision, audio). ML in general can absolutely be used with small datasets. ML is all about finding the right model complexity to fit to the data to maximize out-of-sample performance. If your dataset is small, all that means is that your model will have to be more crude. A simple cross-validated regularized linear regression or a shallow decision tree are ML models too, and you can usefully apply them to a dataset of just 100 samples.