As I've come to understand it when implementing deep learning machine learning the hardware requirement for running a trained neural network model efficiently is much lower than the one required to train it. I've also read somewhere that if an instance of AlphaGo run on a modern powerful desktop machine would compete with the cluster used in Lee See-dol match which consisted of something close to 2000 of CPUs and more than 100 gpus then the ratio in wins would be only be 3:1 in favor of the cluster. Much less than if you would compare it to the ratio of computational power so that gives you hint of how much of the intelligence in it is about previous knowledge gained vs brute computing power. To me that also implies that a lot of the innovation done at DeepMind is more about improving the speed of which a neural net learns and aquires knowledge and less about the running time of applying the model. And that's probably an important distinction in the discussion of this technology.

> If you take the naive approach and assume it's just a matter of getting models with the same number of parameters as the brain has synapses, our biggest models are about a million times too small.

And if you had that hardware, what would you run on it, exactly? There is no off the shelf algorithm which we could plop on that futuristic GPU and say, yes, this will definitely yield a general human-level intelligence. That is my point. We may or may not have the necessary hardware, but we sure don't have the necessary software right now.