Everything related to Deep Learning (DL) and deep(er) networks seems successful, at least progressing very fast, and cultivating the belief that AGI is at reach. This is popular imagination. DL is a tremendous tool to tackle so many problems, including the creation of AGIs. It is not enough, though. A tool is a necessary ingredient, but often insufficient.Leading figures in the domain are looking elsewhere to make progress. This report/claim gathers links to statements by Yoshua Bengio, Yann LeCun and Geoff Hinton. The report also explains:The main weaknesses of DL (as I see them) are: reliance on the simplest possible model neurons (cartoonish as LeCun calls them); use of ideas from 19th century Statistical Mechanics and Statistics, which are the basis of energy functions and log-likelihood methods; and the combination of these in techniques like backprop and stochastic gradient descent, leading to a very limited regime of application (offline, mostly batched, supervised learning), requiring highly-talented practitioners (aka Stochastic Graduate Descent), large amounts of expensive labelled training data and computational power. While great for huge companies who can lure or buy the talent and deploy unlimited resources to gather data and crunch it, DL is simply neither accessible nor useful to the majority of us.Although interesting and relevant, such kind of explanation does not really address the gist of the problem: What is lacking?The question seems broad, but it may be by lack of a simple answer. Is there a way to pin-point what DL is lacking for an AGI ?
Why are deep neural networks and deep learning insufficient to achieve general intelligence?
deep learning;deep network;agi