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Baidu's Supercomputer Beats Google At Image Recognition
catchblue22 writes: Using the ImageNet object classification benchmark, Baidu’s Minwa supercomputer scanned more than 1 million images and taught itself to sort them into about 1,000 categories and achieved an image identification error rate of just 4.58 percent, beating humans, Microsoft and Google. Google's system scored a 95.2% and Microsoft's, a 95.06%, Baidu said. “Our company is now leading the race in computer intelligence,” said Ren Wu, a Baidu scientist working on the project. “I think this is the fastest supercomputer dedicated to deep learning,” he said. “We have great power in our hands—much greater than our competitors.”
This is actually News for Nerds.
I'm curious how much difference in computational power was thrown at training these by Google, Microsoft, and Baidu, though it's going to be great to watch how these continue to evolve.
I only took basic AI in university but...
The power of the computers is not the important thing here if it takes 3 weeks to train the neural network or 1 day does not change the ACCURACY. The running of the NN to identify a picture is also only a fraction of the training time.
Maybe its more about the TRAINING SET here rather than CPU power. It seems extraordinary. 1 000 000 images sorted into 1000 categories must have been done by humans right? Humans sitting like dog,dog,dog,airplane,dog, house, dog OHPLEASELETMEJUSTDIE.
A bigger trainingset is usually a good thing with NN to avoid overtraining which makes it generalize worse on test examples.
The computer has 72 processors and 144 GPU's. That's tiny. Seriously tiny. Sure, GPU's are powerful, especially for image processing. But the larger computers these days are running tens to hundreds of thousands of processors in parallel.
For example, assuming each shelf has 2 processors and 4 GPU's, and they can fit 12 shelves into a single rack, that's a total of 2 racks. Compare that to this image of one of Google's datacenters, where you can see dozens of racks, each containing 14 shelves by my count. And that's just one row. These are gigantic warehouses, with row upon row of racks.
The level of processing power claimed here is closer to the level of a university processing cluster. The larger scientific clusters can be ten or a hundred times larger, and it's not clear just how big private datacenters are.
So overall I'm very, very skeptical. There's a very good chance that they fudged the data somehow to make theirs appear better. But if it is better, well, there's no reason why Google and Microsoft couldn't easily outcompete them in short order.