"Deep learning" (DL) As Subfield of "Machine Learning" (ML) - The Chris Manning View

Chris Manning at the Departments of Computer Science and Linguistics at Stanford University describes deep learning as a subfield of machine learning - which is a form of computational statistics. 

He emphasises the human-computer partnership in successful machine learning, in the sense that ML methods shown to work well have done so due to "human-designed features or representations". 

Examples given are SIFT (scale-invariant feature transform) or HoG (Histogram of Oriented Gradients) features for vision and MFCC (mel-frequency cepstral coefficients) or LPC (linear predictive coding) features for speech. 

In these cases, ML becomes a weighting scheme optimization process to make the best prediction.

OK, but so how does deep learning (DL) differentiate itself from more "conventional" machine learning (ML)? What are the key characteristics of this much touted subfield?

One element is representation learning (also known as "feature" learning) to learn good features and representations, with DL learning multiple levels of these representations. Neural networks are currently the tool of choice for this.  

One could almost claim that "DL" is the new marketing spin on Neural Networks. "Differentiable programming" is another trendy name for this.

Why now for DL - the large amounts of training data, modern multi-core CPUs/GPUs, and just maybe, some progress in algorithmic science along the way?