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They take the frames from YouTube. It is weird to me that YouTube, (derided as a way of sharing funny cat videos) is able to contribute something actually useful to the world.
I'm seriously considering quitting my job and studying ML for a few months in a desperate attempt to get work in projects like this. I feel like I'm missing out but too dumb for traditional grad school.
"and studying ML for a few months" Could anyone with expertise say if this would be enough to build a foundation? How much math background do you need?
Earlier quoted context omitted.
I replied by adding to my comment above as it wouldn't allow me to reply earlier. Reference that. tldr; High-level features generated by this unsupervised network are so high-level that one of them aligns with "has a face in the image", another to "has cat in image" (see the section "Cat and human body detectors") and so on. Note however that they select the "best neuron" for face classification -- the only way they…
But the difference is that you can show it 1 billion unclassified images, then show it 1000 images you know to be faces, analyzing how its neurons respond to the known inputs to use it to classify the rest of the images. Strictly speaking, you do need to have some labeled data at the end in order to determine how the neural net views faces, but I think that obscures what's notable about this system. The amount of hum…
Overhyping when it comes to machine learning and AI seems to be the norm and has already hurt AI/ML severely in the past[1].
More specifically: I didn't disagree with anything you've stated, simply pointed out that labeled training data is necessary in response to the statement that it wasn't.The high-level feature extraction the paper discusses is unsupervised but the classifiers it produces are semi-supervised. It's an important distinction.
> the dataset has 10 million 200x200 pixel images downloaded from the Internet They take the frames from YouTube. It is weird to me that YouTube, (derided as a way of sharing funny cat videos) is able to contribute something actually useful to the world.
See: http://www.nvidia.com/object/tesla-servers.html (4.5 teraflops in one card)
Reminder: GPUs will destroy the world.
"Our training dataset is constructed by sampling frames from 10 million YouTube videos."
People are missing the point here. Yes, 15% accuracy doesn't seem great. BUT the detector built its own categories(!). It managed to find 20,000 different categories of objects in Youtube videos, and one of these categories corresponded to human faces, and another to cats. Once the experimenters found the "face detection neuron" and used it to test faces THAT neuron managed 81.7% detection rate(!). Forget the singula…
> BUT the detector built its own categories(!). It's not revolutionary. Clustering algorithms and neural nets are plenty. Really, what differentiates this network is its scale.
Also, it was (again, from the article) plausible but not a given that high level concepts could be found from unlabeled data.
That "cat" is one of the high level concept you get from using random Youtube videos as raw data is both impressive, and slightly amusing.
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This paper is actually more interesting: it automatically learns some "neuron" which its firing represents a detected face, without any supervise technique. It shows the possibility to extract complex information solely from data.
Is there any concept of "reward" for this thing? Wouldn't that make training it much quicker and make it much more accurate? Or are we trying to avoid any human interaction at all with the earning loop? See, for example this company (one of many) that trains bees to smell certain odours. ( http://www.inscentinel.com/ )