Hinton's networks become the neuron of novel networks. It is important to know that these types of weights don't learn features, they map a compressed represen

Hinton's networks become the neuron of novel networks. It is important to know that these types of weights don't learn features, they map a compressed representation of the learned info, which is the input. Classification through error correction. That is actually what labels do for supervised learning (IOW they learn many ways to represent the label, and that is what the weights are). Modern AI do that plus learn features, but the weights are nevertheless a representation of what was learned, plus a fancy way to encode and decode into that domain. What Hinton and Deepmind will do is use neural-network learned-data, or perhaps the weights, as input to this kind of network. In other words, the output of another NN is labeled a priori, ergo you can use it "unsupervised" networks, which this research expounds. This will allow them to cook the input network into a specific dish, by labels even. Now give me my phd. edit: edit