论文标题
人工智能深度学习的不合理效力
The Unreasonable Effectiveness of Deep Learning in Artificial Intelligence
论文作者
论文摘要
深度学习网络已经经过训练，可以识别语音，标题照片并以高度表现的语言之间翻译文本。尽管深度学习网络在现实世界中的应用已变得无处不在，但我们对为什么缺乏如此有效的理解。根据统计和非凸优化理论的样本复杂性，不应可能进行这些经验结果。但是，正在研究深度学习网络的训练和有效性中的悖论，并在高维空间的几何形状中发现了见解。深度学习的数学理论将阐明它们的运作方式，使我们能够评估不同网络体系结构的优势和劣势并导致重大改进。深度学习为人类提供了与数字设备进行交流的自然方式，并且是建立人工通用情报的基础。深度学习的灵感来自大脑皮层的建筑，并且在其他大脑地区可能会发现对计划和生存至关重要的一般智力的见解，但是要实现这些目标需要重大突破。
Deep learning networks have been trained to recognize speech, caption photographs and translate text between languages at high levels of performance. Although applications of deep learning networks to real world problems have become ubiquitous, our understanding of why they are so effective is lacking. These empirical results should not be possible according to sample complexity in statistics and non-convex optimization theory. However, paradoxes in the training and effectiveness of deep learning networks are being investigated and insights are being found in the geometry of high-dimensional spaces. A mathematical theory of deep learning would illuminate how they function, allow us to assess the strengths and weaknesses of different network architectures and lead to major improvements. Deep learning has provided natural ways for humans to communicate with digital devices and is foundational for building artificial general intelligence. Deep learning was inspired by the architecture of the cerebral cortex and insights into autonomy and general intelligence may be found in other brain regions that are essential for planning and survival, but major breakthroughs will be needed to achieve these goals.
