Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.

翻译：虽然深层次的学习有历史根源可追溯到几十年,但五年多前,“深层学习”一词和这一方法都没有受到欢迎,当时,Krizhevsky、Sutskever和Hinton等论文对实地重新做了重新定义,这些论文包括Krizhevsky、Sutskever和Hinton的经典2012年深层次图像网网络模型。五年后,这个领域又发现了什么?在诸如语音识别、图像识别和游戏以及大众媒体相当热情等领域取得了相当大的进展的背景下,我提出了对深层学习的十种关切,并建议,如果我们要获得人造一般智能,就必须用其他技术来补充深层次的学习。