Deep learning (DL) is revolutionizing the development of artificial intelligence systems. For example, before 2015, humans were better than artificial machines at classifying images and solving many problems of computer vision (related to object localization and detection using images), but nowadays, artificial machines have surpassed the ability of humans in this specific task. This is just one example of how the application of these models has surpassed human abilities and the performance of other machine-learning algorithms. For this reason, DL models have been adopted for genomic selection (GS). In this article we provide insight about the power of DL in solving complex prediction tasks and how combining GS and DL models can accelerate the revolution provoked by GS methodology in plant breeding. Furthermore, we will mention some trends of DL methods, emphasizing some areas of opportunity to really exploit the DL methodology in GS; however, we are aware that considerable research is required to be able not only to use the existing DL in conjunction with GS, but to adapt and develop DL methods that take the peculiarities of breeding inputs and GS into consideration.

中文翻译：

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基因组选择的深度学习能力和观点

深度学习 (DL) 正在彻底改变人工智能系统的发展。例如，在 2015 年之前，人类在对图像进行分类和解决计算机视觉的许多问题（与使用图像进行对象定位和检测有关）方面优于人工机器，但如今，人工机器在这一特定任务上已经超过了人类的能力。这只是这些模型的应用如何超越人类能力和其他机器学习算法的性能的一个例子。因此，DL 模型已被用于基因组选择 (GS)。在本文中，我们将深入了解 DL 在解决复杂预测任务方面的能力，以及结合 GS 和 DL 模型如何加速 GS 方法在植物育种中引发的革命。此外，我们将提到 DL 方法的一些趋势，强调在 GS 中真正利用 DL 方法的一些机会领域；然而，我们知道需要进行大量研究才能不仅能够将现有的 DL 与 GS 结合使用，而且能够适应和开发考虑育种输入和 GS 特性的 DL 方法。