Meet the algorithm that can learn “everything about anything”

Okay, let the thing 'learn' about the Kuhn-Tucker conditions by searching on Google and reading, say, Wikipedia or some books at Google or Amazon. Then have the thing show that for problems in functional form the Zangwill and Kuhn-Tucker constraint qualifications are independent. Do that and I will start to believe that the terminology 'deep learning' is appropriate. I'm not holding my breath.

Yes, it may be that in some rough sense the kind of 'learning' it is doing is roughly like some of the learning of a child of, say, 2 as it is starting to learn about language and things. Yes, it may be that such 'learning' is a significant part of the intelligence of, say, a child of 3-5. Maybe. Big, huge maybe.

When I was working in AI, I noticed the terminology had been cooked up to imply much more than was being accomplished. Now, as I understand it, there is a specific definition for the current AI term 'deep learning' and has to do with the 'depth' of where adjust parameters in a neural network, not how 'deep' the 'learning' is about the subject in question. Cute terminology.

So basically there are two categories of "learning" involved in this sort of research, supervised and unsupervised. In supervised learning, someone gives the computer a long list of concepts and their attributes ("frog", "green frog", "jumping frog") and a set of pictures to go with each item, and feeds them into a visual-recognition algorithm. In unsupervised learning, the computer is given a concept like "frog" but then has to discover all the variations itself and get its own visual data to match.

The claim in this paper is that they have made the unsupervised learning as strong as the supervised learning. That is, they give the computer a concept ("frog"), it goes and searches through Google Books for common variations ("green frog", "jumping frog") and then uses Google image search to fetch images for each of those queries. They can then remove the obvious false positives (they test to see which images seem to screw up their learning algorithm and leave those out), and the result they get is on par with the supervised learning methods.

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In my opinion, this is only mildly interesting because Google Image Search functions based on human input anyway -- Google knows the difference between a "frog" and a "jumping frog" or even a "camel" simply because people on the internet caption such images and Google can make associations between images and their captions. Essentially, what the researchers have managed to do is outsource the work of some grad student to millions of people around the world through Google.

Of course, it could be argued that there is some sort of parallel with what humans actually do (we know what things are called because we hear other people call them that), but even if I didn't know the name of an animal I could still tell you when the same animal is in different pictures, and I can also tell you when it's jumping and what colour it is. I don't need to have someone caption the image for me to understand the broad range of situations to which the caption "jump" applies.