Here is the guy who tried to get his own accuracy on imagenet: https://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/
Getting 5.1% error was really hard, takes a lot of time to get familiar with the classes and to sort through reference images. The 3% error was an entirely hypothetical, optimistic estimate, of a group of humans that make no mistakes.
If you want to appreciate it, you can try the task yourself here: http://cs.stanford.edu/people/karpathy/ilsvrc/
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This seems like an impressive first step towards AGI. The games, like 'pong' and 'space invaders' are perhaps not the most cerebral games, but given that deep blue can only play chess, this is far more impressive IMO. They didn't even need to adjust hyperparameters between games.
I'd also like to see whether they can train a network that plays the same game on different maps without re-training, which seems a lot harder.