In recent years we have seen quite a lot of impressive display of Deep Neural Network (DNN), as demonstrated most famously by AlphaGo and its cousin programs.
But if I understand correctly, deep neural network is just a normal neural network with a lot of layers. We know about the principles of the neural network since the 1970s (?) and deep neural network is just the generalization of a one-layer neural network to many.
From here it doesn't seem like the recent explosion of DNN has anything to do with a theoretical breakthrough. It seems like DNN successes can be entirely attributed to better hardware and more data, and not to any new theoretical insights or better algorithms.
I would go even as far as saying that there are no new theoretical insights/algorithms that contribute significantly to the DNN recent successes; that all theoretical underpinning of DNN was done in the 1970s or prior.
An I right on this?