In 1986, Geoffrey Hinton* co-authored a paper that, three decades later, is central to the explosion of artificial intelligence (AI). But he says his breakthrough method should be dispensed with, and a new path to AI found.
Recently, Hinton said some suggestions about Artificial Intelligence (AI):
"We clearly don't need all the labeled data during training of AI methods".
"New methods will probably have to be invented instead of back-propagation".
In contrast, Hinton suggested that what is known as "unsupervised learning" will rid of back-propagation."
*G. Hinton is a professor emeritus at the University of Toronto and a Google researcher !
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https://www.axios.com/artificial-intelligence-pioneer-says-we-need-to-start-over-1513305524-f619efbd-9db0-4947-a9b2-7a4c310a28fe.html
In my opinion and based on my experience, its hitting the wall since we have been successful in applying deep learning in biometrics and medical image analysis applications though on small scale.
Fundamentally, it is still insufficient to mimic the human brain in the biological form of the neuron connectivity.
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