Gary Marcus

There's a major difference: DL was guided in its infancy by ideas from neuroscience, so there is a relatively direct link between them. In contrast, the application of quantum mechanics to the c-word is taking two distinct fields and tying them together on speculative grounds.

The relation between deep learning - with its single neuron type and largely homogenous architecture - and the actual complexity of the human brain, with > 1000 neuron types, hundreds of proteins at each synapse and > 100 distinct brain regions - is risible.

Every model is an abstraction. Newtonian mechanics ignores air turbulence, molecular interactions, etc. Climate models capture coarse grained interactions, not the multitude of animals, plants, and wind-currents that truly shape the climate. Neural networks are no different.

If you think it's the wrong level of abstraction, fine, that's an opinion one can hold. But, the claim that it has no relation just doesn't hold water. You can actually capture a lot of the variance in a single neuron's firing rate responses with a linear-non-linear model.