Current Artificial Intelligence (AI) methods, most based on deep learning, have facilitated progress in several fields, including computer vision and natural language understanding. The progress of these AI methods is measured using benchmarks designed to solve challenging tasks, such as visual question answering. A question remains of how much understanding is leveraged by these methods and how appropriate are the current benchmarks to measure understanding capabilities. To answer these questions, we have analysed existing benchmarks and their understanding capabilities, defined by a set of understanding capabilities, and current research streams. We show how progress has been made in benchmark development to measure understanding capabilities of AI methods and we review as well how current methods develop understanding capabilities.
翻译:目前的人工智能(AI)方法大多以深层次学习为基础,促进了若干领域的进展,包括计算机视野和自然语言理解。这些人工智能方法的进展是用旨在解决具有挑战性的任务的基准来衡量的,例如直观回答。一个问题仍然是这些方法利用了多少理解,衡量理解能力的现有基准如何适当。为了回答这些问题,我们分析了以一套理解能力界定的现有基准及其理解能力,以及当前的研究流。我们展示了在基准发展以衡量对人工智能方法的理解能力方面取得的进展,我们审查了当前方法如何发展理解能力。