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Nvidia GPU-Powered Autonomous Car Teaches Itself To See And Steer (networkworld.com)
An anonymous reader quotes a report from Network World discussing Nvidia's project called DAVE2, where their engineering team built a self-driving car with one camera, one Drive-PX embedded computer and only 72 hours of training data: Neural networks and image recognition applications such as self-driving cars have exploded recently for two reasons. First, Graphical Processing Units (GPU) used to render graphics in mobile phones became powerful and inexpensive. GPUs densely packed onto board-level supercomputers are very good at solving massively parallel neural network problems and are inexpensive enough for every AI researcher and software developer to buy. Second, large, labeled image datasets have become available to train massively parallel neural networks implemented on GPUs to see and perceive the world of objects captured by cameras. The Nvidia team trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. Nvidia's breakthrough is the autonomous vehicle automatically taught itself by watching how a human drove, the internal representations of the processing steps of seeing the road ahead and steering the autonomous vehicle without explicitly training it to detect features such as roads and lanes.
No, not like people. An NN of the type described doesn't learn further; won't generalize from other experiences (FI, this thing won't know what to do with a pedestrian unless it's observed the human driver stopping for some number of them. Otherwise it has no training that places value on a pedestrian. Same for cat, chunks of some trucks blown-off retread, potholes, etc.) A person will know from their far, far deeper experience that it's inherently a bad idea to run down the three year old that walked in front of the car, will usually at least try not to run down little Susie's pet cat, and will choose to go around the pothole if it is reasonable to do so.
An NN is a low-dimensional approach to solving problems. But most non-trivial problems -- and driving a vehicle is an entirely representative proxy for such problems -- tend not to be uniformly low-dimensional. Many aspects come into play suddenly and unpredictably. Some might not be seen for years, or ever. But then again, they might. In order to deal with such things, more than low-dimensional problem solving is required. NN's can't do it. They're inherently limited.
It's going to require very high level software. Not intelligent, but damned well informed and replete with a huge rulespace that can solve all of the types of reasonably solvable problems in the space.
That's all IMHO, of course. But I'm right, so there's that. :)
I've fallen off your lawn, and I can't get up.
Sorry, there is no algorithm that makes algorithms..
Although there might not be an algorithm that makes algorithms, there are algorithms to configure a meta-algorithm implementations. And example meta-algorithm implementation would be a deep neural net, or a human brain. I don't think it is a stretch to call the algorithm used to configure meta-algorithm implementation "learning" (although commonly this is called training)...
But this is merely a semantic point.