I have chosen deep learning as the topic of my graduation thesis and lately and I had the opportunity to study the evolution of artificial neural networks. I think deep learning is having it's descending period on the hype curve in an interesting way, it is interesting because the prominent figures of the field are delivering it. If my memory serves me well, this is the third article brought to HN within last month on the topic of "deep learning does not represent how real brain works and is actually unsuitable for artificial general intelligence".
On one of his seminars Andrew Ng talks about "the algorithm": he pointed out that human brain may re-wire itself to handle different tasks, for example one part of the brain which is responsible for sight might take up the task for hearing. The idea of ANN undisputedly inspired from research investigating how brain actually works, and now it has set the state-of-the-art on many areas it has been applied, but to do this we had to resort to "ugly hacks" in the perspective of "the algorithm", to name some of them would be Autoencoders and Restricted Boltzmann Machines. It works, yes it works very well, but we had to pay a price of taking a detour from finding "the algorithm".
In my opinion, such banterings as in OP should not be taken as an aggressive stance against deep learning. Just because it is not "the" method doesn't mean it has no value whatsoever, but it has a point; after the limitations of deep learning is explored well, we will need a new idea to push the field further.
State-of-the-art results no longer require unsupervised pretraining with autoencoders or RBMs, but back when unsupervised pretraining was more popular, top researchers were rationalizing that it was consider more biologically plausible than the standard nets trained with back prop, since brains generalize through observing a large amount of data over their lifetime to quickly recognize new objects and since the nets aren't trained for a specific task, they would hopefully generalize better and be a step closer to general intelligence.
On one of his seminars Andrew Ng talks about "the algorithm": he pointed out that human brain may re-wire itself to handle different tasks, for example one part of the brain which is responsible for sight might take up the task for hearing. The idea of ANN undisputedly inspired from research investigating how brain actually works, and now it has set the state-of-the-art on many areas it has been applied, but to do this we had to resort to "ugly hacks" in the perspective of "the algorithm", to name some of them would be Autoencoders and Restricted Boltzmann Machines. It works, yes it works very well, but we had to pay a price of taking a detour from finding "the algorithm".
In my opinion, such banterings as in OP should not be taken as an aggressive stance against deep learning. Just because it is not "the" method doesn't mean it has no value whatsoever, but it has a point; after the limitations of deep learning is explored well, we will need a new idea to push the field further.