论文标题
深度神经网络的局限性：G. Marcus对深度学习的批判性评估的讨论
Limitations of Deep Neural Networks: a discussion of G. Marcus' critical appraisal of deep learning
论文作者
论文摘要
深层神经网络已经引发了人工智能的革命，在医学成像，半自治，电子商务，遗传学研究，语音识别，粒子物理学，实验艺术，经济预测，环境科学，工业制造以及几乎在每个领域的各种应用中都应用。然而，这种突然的成功可能使研究界陶醉，并使他们视而不见，因为他们为深入学习的潜在陷阱比保证更高的地位。同样，旨在减轻深度学习弱点的研究似乎对科学家和工程师的吸引力较低，他们专注于为深度学习模型寻找越来越多的应用程序的低点果实，从而使短期福利妨碍了长期的科学进步。加里·马库斯（Gary Marcus）写了一篇题为《深度学习：批判性评估》的论文，在这里我们讨论了马库斯的核心思想，并尝试对该主题进行一般评估。这项研究研究了深层神经网络的一些局限性，目的是指向潜在的未来研究途径，并清除许多可能误导它们的研究人员所持有的一些形而上学的误解。
Deep neural networks have triggered a revolution in artificial intelligence, having been applied with great results in medical imaging, semi-autonomous vehicles, ecommerce, genetics research, speech recognition, particle physics, experimental art, economic forecasting, environmental science, industrial manufacturing, and a wide variety of applications in nearly every field. This sudden success, though, may have intoxicated the research community and blinded them to the potential pitfalls of assigning deep learning a higher status than warranted. Also, research directed at alleviating the weaknesses of deep learning may seem less attractive to scientists and engineers, who focus on the low-hanging fruit of finding more and more applications for deep learning models, thus letting short-term benefits hamper long-term scientific progress. Gary Marcus wrote a paper entitled Deep Learning: A Critical Appraisal, and here we discuss Marcus' core ideas, as well as attempt a general assessment of the subject. This study examines some of the limitations of deep neural networks, with the intention of pointing towards potential paths for future research, and of clearing up some metaphysical misconceptions, held by numerous researchers, that may misdirect them.
