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Google Researchers Created An Amazing Scene-Rendering AI (arstechnica.com)
Researchers from Google's DeepMind subsidiary have developed deep neural networks that "have a remarkable capacity to understand a scene, represent it in a compact format, and then 'imagine' what the same scene would look like from a perspective the network hasn't seen before," writes Timothy B. Lee via Ars Technica. From the report: A DeepMind team led by Ali Eslami and Danilo Rezende has developed software based on deep neural networks with these same capabilities -- at least for simplified geometric scenes. Given a handful of "snapshots" of a virtual scene, the software -- known as a generative query network (GQN) -- uses a neural network to build a compact mathematical representation of that scene. It then uses that representation to render images of the room from new perspectives -- perspectives the network hasn't seen before.
Under the hood, the GQN is really two different deep neural networks connected together. On the left, the representation network takes in a collection of images representing a scene (together with data about the camera location for each image) and condenses these images down to a compact mathematical representation (essentially a vector of numbers) of the scene as a whole. Then it's the job of the generation network to reverse this process: starting with the vector representing the scene, accepting a camera location as input, and generating an image representing how the scene would look like from that angle. The team used the standard machine learning technique of stochastic gradient descent to iteratively improve the two networks. The software feeds some training images into the network, generates an output image, and then observes how much this image diverged from the expected result. [...] If the output doesn't match the desired image, then the software back-propagates the errors, updating the numerical weights on the thousands of neurons to improve the network's performance.
Under the hood, the GQN is really two different deep neural networks connected together. On the left, the representation network takes in a collection of images representing a scene (together with data about the camera location for each image) and condenses these images down to a compact mathematical representation (essentially a vector of numbers) of the scene as a whole. Then it's the job of the generation network to reverse this process: starting with the vector representing the scene, accepting a camera location as input, and generating an image representing how the scene would look like from that angle. The team used the standard machine learning technique of stochastic gradient descent to iteratively improve the two networks. The software feeds some training images into the network, generates an output image, and then observes how much this image diverged from the expected result. [...] If the output doesn't match the desired image, then the software back-propagates the errors, updating the numerical weights on the thousands of neurons to improve the network's performance.
no joke. AI based monitoring software. 2 years ago it was worthless. They just replaced the whole team with it. It's not some kneejerk thing either. They've been testing it for months and it's more accurate than people. That didn't used to be true. Used to be if you just ran monitoring scripts you were just asking for trouble. You needed somebody to watch the script. Not anymore.
This next step here is getting AI to imagine. To think through problems. 20 years from now IT will be gone. The old timer's reading this probably don't care because they'll be retired or dead. Anyone under 50 should take notice. We need to start thinking about a post-work future now. Sure, eventually tech might catch up and employ people... in 80 years. Just remember you're gonna live through those 80 years of joblessness.
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Everything that's called "AI" today is just advanced pattern recognition. I hope that the /. editors quit using the term "AI" so frequently. ...
For decades "AI was a failure". But that was because intelligence seems to involve a number of different components, and every time AI researchers got one of the components working and useful, somebody gave it a name, stopped calling it AI, and the field of "AI" shrunk to exclude it, leaving only the problems not yet solved.
It's nice to finally see some of the pieces retain the "AI" label once they're up and running well enough to be impressive..
Sure it's not the whole of "intelligence". But it's obviously a part of it - or (if not the SAME thing that our brains do), at least a part of something that, once more pieces are added, would be recognized as "intelligence" in a Turing test.
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I don't understand the fixation on the terminology, while ignoring the interesting aspects of what it does.
The terminology is very well defined in the industry, and accepted by most who participate. Deep neural network are a part of the family of machine learning algorithms (https://en.wikipedia.org/wiki/Deep_learning), which is, in turn, a subset of the field of artificial intelligence (https://en.wikipedia.org/wiki/Machine_learning).
They key different from what you call "pattern recognition" is that there is no explicit coding of the algorithm, but the algorithm instead is "learned" through examples.
Nobody is saying that the machine is intelligent. You'd do yourself good to look past the disagreement with the established terminology and look at the technology itself. You might find it interesting.
I think that the fixation on the terminology is due to two simple concepts. First, we've seen terminology used as marketing speak for both vaporware and for products that are much more limited than suggested (i.e. the devil is in the details). Second, hardly anyone cares how a particular subset of field of research defines localized terminology except people within that field. Redefining the term AI to mean less than the general usage seems to be just plain silly. It's like calling an apartment a house and being irked when someone calls you out on it.
Should the focus be on the actual technology (which is actually kinda cool)? Yes.
Should the terminology used be more accurate? Yes.
In fact, I think that we just might be smart enough to do both... maybe.... .grin.