论文标题
深度学习和人工通用情报：还有很长的路要走
Deep Learning and Artificial General Intelligence: Still a Long Way to Go
论文作者
论文摘要
近年来，使用神经网络架构（即深度神经网络）的深度学习一直在计算机科学研究的前沿。在某些问题（例如，在计算机视觉，游戏和生物学）中，它甚至导致超人的表现，因此，深度学习革命一词是创造的。深度学习的无可争议的成功和快速发展表明，将来它可能成为人工通用智能（AGI）的推动者。在本文中，我们对此声明进行了关键的态度，这表明了为什么深层神经网络在当前状态下尚未准备好成为达到AGI的首选技术的主要原因。
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.
