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Most sane people consider it a fundamental goal in life to make the world a better place. It's true that this isn't a rational choice, but then again, it's not a rational choice to act selfishly, because that, too, is based on your emotional response to the stimuli your body receives. In our society, people who make the selfish choice are generally called sociopaths. The only possible explanation for your post is that you are one of them.
If that doesn't work, use a Kinect.
Data visualizations use 3D all the time; it's built into most scientific plotting softwares.
Building 3D models of arbitrary scenes from just images is rapidly leaving the research world, as demonstrated by recent 3D reconstruction projects like Building Rome In a Day (A research page, which, by the way could greatly benefit from 3D web). I wouldn't be surprised if artists start uploading their sculptures, or parents start uploading models of their kids' sandcastles.
And these are just the applications I can think of with dumb 3d models, no physics.
they're patenting a specific method of doing so.
There is nothing specific about the methods they're patenting. I just worked on a very similar project, and after reading the patent, I see very little separating what they patented from what we did. Indeed we don't use dimensionality reduction the way they suggest (although we did use it for a while), and we don't provide specific names for the objects we discover (though we have talked about doing so via crowdsourcing). Indeed our work is more recent than the patent filing, but people have been attempting similar things for ages (e.g. , ...they are very easy to find). Worse, the two papers I cite provide enough detail to actually produce a working system, whereas the patent provides little detail beyond a few references to well-known machine learning and computer vision techniques. And even when they suggest methodology, it's always "maybe we'll use this, maybe not", and further they tend to list several potential methods without any indication that they've researched which ones work.
In fact, I'm quite sure that nothing like this exists. I'm not sure about the actual search engine part of all this, but I did see a talk last fall by one of the researchers who worked on ShadowDraw, which I'm reasonably sure is going to be a component of the final system. The real problem that *they* had to solve was the simple fact that the average person is a horrible, HORRIBLE artist. Ask them to draw a rabbit and for 90% of people, it will come out as a blob that might be an animal, but that's about all you can tell. The algorithms they talked about that actually make the system work as well as it does were actually quite impressive--extremely fast contour indexing, contour combination, converting real photos into convincing sketches--it all sounds easy, but I dare you to actually try implementing it.
Now--and let's see what happens to my karma for saying this--I actually kinda think they deserve a patent for this. Not for coming up with the idea of drawing-based search; that idea is obvious. However, making a system that works as well as ShadowDraw is quite an achievement, and more importantly, Microsoft Research would never have released the algorithm to the public unless it could be patent-protected. Patents in this case aren't about protecting Microsoft's innovation; it's about motivating Microsoft to publish for the sake of other innovators.
And there's some background on Poon's goals here:
The goals seem to me to be about studying specific theories about information propagation across synapses as well as studying brain-computer interfaces. They never mention building a model of the entire visual system or any serious artificial intelligence. We have only the vaguest theories about how the visual system works beyond V1, and essentially no idea what properties of the synapse are important to make it happen.
About two years ago, while I was still doing my undergraduate research in neural modeling, I recall that the particular theory they're talking about--spike-timing dependent plasticity--was quite controversial. It might have been simply an artifact of the way the NMDA receptor worked. Nobody seemed to have any cohesive theory for why it would lead to intelligence or learning, other than vague references to the well-established Hebb rule.
Nor is it anything new. Remember this story from ages ago? Remember how well that returned on its promises of creating a real brain? That was spike-timing dependent plasticity as well, and unsurprisingly it never did anything resembling thought.
Slashdot, can we please stop posting stories about people trying to make brains on chips and post stories about real AI research?
Krugman's error stems from his conflation of the two definitions of information. By one definition, the physical number of bits that the human race has managed to store on hard drives, the amount of information the human race has produced has been increasing exponentially. However, this is not useful information, and not the kind of information that requires any serious education to produce. The other definition is from information theory, where information is defined in terms of randomness: here, information is the total number of bits that you need in order to convey a signal in its most compressed form (i.e. the 'random' component of the signal that can't be derived from other parts of the signal). By this definition, the fact that I copy the 100mb file 'a.mp4' from my desktop to my home folder does not mean that I have produced 100mb of information; I have produced at most 64 bytes of information, since that's the number of bytes it took for me to describe the new state of the world.
As for the rest of the article, Krugman argues (correctly, I believe) that any job which requires the production of information will remain strictly in the domain of human beings. However, he seems to forget that most physical goods are just copies of other physical goods, and therefore contain very little information. The production of those goods can generally be replaced by machines.
However, there is still some insight in what Krugman says, though you have to think a bit to realize it. Krugman is actually arguing that educations are only valuable if they teach you how to produce information, and that an education which only teaches you to parrot facts makes you very much like a computer, and very much replaceable by computers. Hence why he needed to use lawyers in his example. I don't think us computer scientists have much to worry about from this argument.
The rest of gradeinflation.com gives much more information you may find interesting.
The reason for this is that the more students they fail, the better they look.
This is also incorrect. Far more important in the school's rankings are (a) the percent of their admitted class to accept the admissions offer, and (b) a higher number of students who get job offers after graduating. This incentivizes schools to lower failure rates (US News and World Report reports graduation rates and rolls them into rankings because they know it turns off most prospective students), and also to increase grades to make their students' resumes look better.
The surprising thing in TFA is that Intel is claiming to have done almost as well on a problem that NVIDIA used to tout their GPUs. It really makes me wonder what problem it was. The claim that "performance on both CPUs and GPUs is limited by memory bandwidth" seems particularly suspect, since on a good GPU the memory access should be parallelized.
It's clear that Intel wants a piece of the growing CUDA userbase, but I think it will be a while before any x86 processor can compete with a GPU on the problems that a GPU's architecture was specifically designed to address.