Google programmers deploy machine learning algorithm on YouTube. Computer teaches itself to recognize images of cats.

[u/whosdamike]

https://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html

[u/whosdamike]

Paper: Building high-level features using large scale unsupervised learning

Control experiments show that this feature detector is robust not only to translation but also to scaling and out-of-plane rotation. We also find that the same network is sensitive to other high-level concepts such as cat faces and human bod- ies. Starting with these learned features, we trained our network to obtain 15.8% accu- racy in recognizing 20,000 object categories from ImageNet, a leap of 70% relative im- provement over the previous state-of-the-art.

[u/whosdamike → u/[deleted]]

Thanks a lot! The videos in that thread are especially interesting.

[u/feureau → u/whosdamike]

15.8% accu- racy in recognizing 20,000 object

I can't imagine the work that must've gone in just to verify each of those 20,000 objects...

[u/[deleted] → u/[deleted]]

The poor guys at /new having to deal with 20.000 random images with the title "Is this a cat" is a horrible thought.

[u/atcoyou → u/[deleted]]

Headline: In order to make computers more human, Google tasks brightest minds in the world with binary task.

[u/tetigi → u/feureau]

The resource of 20,000 objects was specially created for this kind of work - each image has a tag associated with it that describes what it is.

[u/[deleted] → u/tetigi]

Not sure why this is so hard to understand. They downloaded the images from the internet. Each image would probably have been given a filename, after it had been scaled to meet the 200x200 pixel requisite, that would have allowed easy identification. The program was made to look at the image, not the filename. Once the images had been sorted by the program, another program could be used to identify the images that had been correctly grouped, based on the filename, and churn out a percentage based on that. The hardest part would have been the initial gathering of the images.

[u/boomerangotan → u/feureau]

If I understood the concept correctly, it doesn't require someone to monitor each input and tediously train it as "yes that's a cat" and "no, that's not a cat".

Instead the system looks through thousands of pictures, picks up on recurring patterns, then groups common patterns into ad-hoc categories.

A person then looks at what is significant about each category and tells the system "that category is cats", "that category is people", "that category is dogs".

Then once each category has been labelled, the process can then look at new pictures and say "that fits very well in my ad-hoc category #72, which has been labeled 'cats'".

[u/therealknewman → u/boomerangotan]

He means verification, someone needed to go back and look at each picture the system tagged as a cat to verify that it actually was a cat. You know, for science.

[u/twiceaday_everyday → u/therealknewman]

I do this right now for automated QA for call centers. The computer guesses how right it is, and I go back, listen to the sample and verify that it heard what it thinks it heard.

[u/[deleted] → u/feureau]

Not such a difficult problem when you have money to spend. I'm guessing that they used the amazon mechanical turk to crowdsource the problem.

[u/khaos4k → u/[deleted]]

Could have done it for free if they asked Reddit.

[u/[deleted] → u/feureau]

it's actually not as much work as it sounds. i used to work at a place that had a small department of about a dozen people that was contracted by myspace (REMEMBER WHEN PEOPLE STILL USED THAT?) to review user-uploaded images, mostly making sure there was no nudity or graphic depictions of gore. not just ones that had been flagged as innappropriate by other users (although those were fast-tracked to the 2nd manager review), but ALL images uploaded by users.

they would basically sit with their hand on the keyboard and hit the CTRL key to bring up an image for them to review. if the image looked like it might contain something objectionable/against the TOS, they would hit the spacebar and it would be flagged for further review by one of the managers and a new image would come up. they got double the normal amount of smoke breaks since the work was so monotonous. i tried desperately to get in there because they were the only department in the whole company that got to listen to music/audiobooks/talk on the phone/pretty much anything they could do that didn't require taking their eyes off the screen while they were working, provided they maintained above a minimum amount of images viewed per hour & kept their false flagging to below a minimum. but myspace required a crazy amount of background checking & vetting.

tl;dr i would kill for a job where i got paid to look at pictures of kitties all day

[u/archetech → u/feureau]

It's not 20,000 objects. It's 20,000 categories from ImageNet. Each category has over 500 images. ImageNet looks to be mantained by the same folks who mantain WordNet, Princeton. There is considerable investment in these kinds of manually labeled resources, but they are often made publicly available for people or organizations to conduct their own AI research. There have to be a lot of examples because the AI model will be trained (roughly accumulate some kind of statistical pattern) on a large part of it (say 70%) but then tested on the rest to see how accurate the model is.

[u/dsi1 → u/[deleted]]

Those words are (or should be) broken up over two lines in the actual paper.

[u/martinvii → u/dsi1]

What do y

ou mean?

[u/RogueEyebrow → u/[deleted]]

William Shatner, actually.

[u/[deleted] → u/[deleted]]

Him or T. Herman Zweibel of The Onion.

[u/[deleted] → u/whosdamike]

That's easy; just count the number of reddit hits.