4 ms·

Converging everything towards the median (google results) is not machine learning, and is making search results and the internet far worse. Speech recognition

by guineamax2000 8y ago

Converging everything towards the median (google results) is not machine learning, and is making search results and the internet far worse. Speech recognition on my phone hasn't improved much since Dragon in the 90s. I'd argue the tech today is worse because it never works without a network connection, so it basically never works unless you're in a big city. Google translate is a running joke. I don't think that I'm allowed to comment further on Google's use of machine learning without violating NDAs, but manually fiddling with weights until you get the magic number you want is not machine learning. Again, not really seeing any benefit. Lots of hype though.

whymauri 8y ago
You're just pulling a No True Scotsman. I'm surprised someone replied to you in good faith.
- scottlocklin 8y ago
  He's very much not engaging in any rhetorical fallacy: it's an uncommonly clear observation of the actual reality of the current year in machine learning. Which probably means he labors in the field. The main businesses which seems to truly depend on ML (other than maybe FICO) is that of tech journalist and PR dweeb.
  - jononor 8y ago
    Sure Facebook, Google and Amazon (for example) would probably operate fine without machine learning, ie their businesses don't "truly depend on it". But that is far from that there has not been "any practical application yet".
  - wenc 8y ago
    Are we talking about ML or DL in particular? Also, does ML have to be sophisticated in order for it to "count"? Just off the top of my head, 3D/HD mapping companies like HERE rely on DL-based image recognition to recognize objects at reasonable levels of accuracy -- totally infeasible to do manually at scale. Also, one of the triggers of the renaissance of NNs is that it was shown to outperform traditional computer vision techniques around the early 2010s. USPS does handwriting recognition every single day -- maybe not with DL -- but definitely with some ML algorithm (I was at a talk given by one of the originators of said algorithm). ML is more than just DL. If take the definition of ML encompass to statistical learning -- which it traditionally does -- production ML deployments is extremely pervasive, from industries as varied as finance to chemical manufacturing. The exact FICO algorithm is proprietary, but algorithms of its ilk are pervasive. As you know, at least two other companies (Transunion and Experian) also have their own algorithms. And credit scoring algorithms are among the least sophisticated ML deployments. I'm not able to talk about the ML models I work on, but they're in production and the business relies on them. I guess I'm not seeing the finer points of the argument -- as it stands without further qualification or refinement, it does not seem to be a valid conclusion.
    - scottlocklin 8y ago
      I haven't spoken to the Here guys lately, but while they did hire a bunch of DL weenies, it looked like they mostly put them to work doing more useful things. I'll say it again: no company depends on machine learning, other than, maybe Fair Isaac. Many use such things. They don't depend on ML. IMO the trend is in the other direction; many companies will begin to realize what they've spent on models isn't worth the returns.
openasocket 8y ago
I mean, isn't pretty much all image recognition going to involve some form of machine learning at some level? I'm not even sure how you'd be able to do that without some sort of ML system. And that's used plenty in production.
- scottlocklin 8y ago
  Lots of stuff which can be done here; cigarette companies used to recognize tax stamps using optics hardware.
obastani 8y ago
Considering that Dragon is based on machine learning (hidden Markov models, or HMMs, to be exact) [1], I'm not sure what point you're trying to make. In any case, I used Dragon in the late 90's, and it was terrible, whereas Google's voice recognition on Pixel devices works great for me. One benefit of using deep learning is that they are much less brittle, e.g., working much better for people with accents, in noisy environments, etc. [1] https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking

whymauri 8y ago

You're just pulling a No True Scotsman. I'm surprised someone replied to you in good faith.

scottlocklin 8y ago
He's very much not engaging in any rhetorical fallacy: it's an uncommonly clear observation of the actual reality of the current year in machine learning. Which probably means he labors in the field. The main businesses which seems to truly depend on ML (other than maybe FICO) is that of tech journalist and PR dweeb.
- jononor 8y ago
  Sure Facebook, Google and Amazon (for example) would probably operate fine without machine learning, ie their businesses don't "truly depend on it". But that is far from that there has not been "any practical application yet".
- wenc 8y ago
  Are we talking about ML or DL in particular? Also, does ML have to be sophisticated in order for it to "count"? Just off the top of my head, 3D/HD mapping companies like HERE rely on DL-based image recognition to recognize objects at reasonable levels of accuracy -- totally infeasible to do manually at scale. Also, one of the triggers of the renaissance of NNs is that it was shown to outperform traditional computer vision techniques around the early 2010s. USPS does handwriting recognition every single day -- maybe not with DL -- but definitely with some ML algorithm (I was at a talk given by one of the originators of said algorithm). ML is more than just DL. If take the definition of ML encompass to statistical learning -- which it traditionally does -- production ML deployments is extremely pervasive, from industries as varied as finance to chemical manufacturing. The exact FICO algorithm is proprietary, but algorithms of its ilk are pervasive. As you know, at least two other companies (Transunion and Experian) also have their own algorithms. And credit scoring algorithms are among the least sophisticated ML deployments. I'm not able to talk about the ML models I work on, but they're in production and the business relies on them. I guess I'm not seeing the finer points of the argument -- as it stands without further qualification or refinement, it does not seem to be a valid conclusion.
  - scottlocklin 8y ago
    I haven't spoken to the Here guys lately, but while they did hire a bunch of DL weenies, it looked like they mostly put them to work doing more useful things. I'll say it again: no company depends on machine learning, other than, maybe Fair Isaac. Many use such things. They don't depend on ML. IMO the trend is in the other direction; many companies will begin to realize what they've spent on models isn't worth the returns.

openasocket 8y ago

I mean, isn't pretty much all image recognition going to involve some form of machine learning at some level? I'm not even sure how you'd be able to do that without some sort of ML system. And that's used plenty in production.

scottlocklin 8y ago
Lots of stuff which can be done here; cigarette companies used to recognize tax stamps using optics hardware.

obastani 8y ago

Considering that Dragon is based on machine learning (hidden Markov models, or HMMs, to be exact) [1], I'm not sure what point you're trying to make. In any case, I used Dragon in the late 90's, and it was terrible, whereas Google's voice recognition on Pixel devices works great for me. One benefit of using deep learning is that they are much less brittle, e.g., working much better for people with accents, in noisy environments, etc. [1] https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking https://en.wikipedia.org/wiki/Dragon_NaturallySpeaking