There are other big deals. The MS ImageNet win also contained frightening progress on the training meta level.
The other issue is that constructing this kind of mega-neural net is tremendously difficult. Landing on a particular set of algorithms—determining how each layer should operate and how it should talk to the next layer—is an almost epic task. But Microsoft has a trick here, too. It has designed a computing system that can help build these networks.
As Jian Sun explains it, researchers can identify a promising arrangement for massive neural networks, and then the system can cycle through a range of similar possibilities until it settles on this best one. “In most cases, after a number of tries, the researchers learn [something], reflect, and make a new decision on the next try,” he says. “You can view this as ‘human-assisted search.'”
-- extracted from very readable summary at wired: http://www.wired.com/2016/01/microsoft-neural-net-shows-deep-learning-can-get-way-deeper/
Going by that description, it is much much less important than residual learning, because hyperparameter optimization is not new. There are a lot of approaches: grid search, random search, Gaussian processes. Some hyperparameter optimizations baked into MSR's deep learning framework would save some researcher time and effort, certainly, but I don't know that it would've made any big difference unless they have something quite unusual going one.
(I liked one paper which took a Bayesian multi-armed bandit approach and treated error curves as partial informati...
DeepMind's go AI, called AlphaGo, has beaten the European champion with a score of 5-0. A match against top ranked human, Lee Se-dol, is scheduled for March.