  In recent years, deep learning using neural network architecture, i.e. deep
neural networks, has been on the frontier of computer science research. It has
even lead to superhuman performance in some problems, e.g., in computer vision,
games and biology, and as a result the term deep learning revolution was
coined. The undisputed success and rapid growth of deep learning suggests that,
in future, it might become an enabler for Artificial General Intelligence
(AGI). In this article, we approach this statement critically showing five
major reasons of why deep neural networks, as of the current state, are not
ready to be the technique of choice for reaching AGI.
