The phenomenal success of physics in explaining nature and designing hardware is predicated on efficient computational models. A universal codebook of physical laws defines the computational rules and a physical system is an interacting ensemble governed by these rules. Led by deep neural networks, artificial intelligence (AI) has introduced an alternate end-to-end data-driven computational framework, with astonishing performance gains in image classification and speech recognition and fueling hopes for a novel approach to discovering physics itself. These gains, however, come at the expense of interpretability and also computational efficiency; a trend that is on a collision course with the expected end of semiconductor scaling known as the Moore's Law. With focus on photonic applications, this paper argues how an emerging symbiosis of physics and artificial intelligence can overcome such formidable challenges, thereby not only extending the latter's spectacular rise but also transforming the direction of physical science.

中文翻译：

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物理-人工智能共生

物理学在解释自然和设计硬件方面取得的巨大成功是建立在高效的计算模型之上的。物理定律的通用码本定义了计算规则，而物理系统是受这些规则支配的相互作用的集合。在深度神经网络的带领下，人工智能 (AI) 引入了一种替代的端到端数据驱动计算框架，在图像分类和语音识别方面取得了惊人的性能提升，并点燃了人们对发现物理本身的新方法的希望。然而，这些收益是以可解释性和计算效率为代价的。这种趋势正与被称为摩尔定律的半导体缩放的预期终结相冲突。专注于光子应用，