标题： 深度学习，深度变革？ 映射人工智能通用技术的发展 显示英文标题

标题： Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology

摘要： 通用目的技术（GPTs）可以应用于许多行业，是经济增长和国家及地区竞争力的重要驱动力。尽管如此，它们的开发和扩散的地理分布在文献中并未受到足够的关注。我们通过分析深度学习（DL），一种人工智能（AI）的核心技术，日益被认可为最新的GPT，来解决这一问题。我们在ArXiv的一个新数据集中识别出DL论文，ArXiv是一个流行的预印本网站，并使用CrunchBase，一个技术商业目录来衡量与之相关的工业能力。在证明DL符合GPT的定义后，经历了快速增长并扩散到新领域，产生了影响，我们描述了其地理变化。我们的分析显示，中国在人工智能排名中的上升以及几个欧洲国家的相对下降。我们还发现，DL的地理初期波动之后出现了整合，这表明随着新的DL研究中心变得主导，新进入者的机遇窗口可能正在关闭。最后，我们研究了DL集群的区域驱动因素。我们发现，竞争性的DL集群往往位于结合了相关研究和工业活动的地区。这可能是因为靠近彼此的GPT开发者和采用者可以更容易地协作和共享知识，从而克服GPT部署中的协调失败。我们的分析还显示，在控制了其他解释因素后，中国在DL方面具有比较优势，这或许强调了数据访问和支持性政策对于这种复杂、"全用途"技术成功发展的重要性。 显示英文摘要

摘要： General Purpose Technologies (GPTs) that can be applied in many industries are an important driver of economic growth and national and regional competitiveness. In spite of this, the geography of their development and diffusion has not received significant attention in the literature. We address this with an analysis of Deep Learning (DL), a core technique in Artificial Intelligence (AI) increasingly being recognized as the latest GPT. We identify DL papers in a novel dataset from ArXiv, a popular preprints website, and use CrunchBase, a technology business directory to measure industrial capabilities related to it. After showing that DL conforms with the definition of a GPT, having experienced rapid growth and diffusion into new fields where it has generated an impact, we describe changes in its geography. Our analysis shows China's rise in AI rankings and relative decline in several European countries. We also find that initial volatility in the geography of DL has been followed by consolidation, suggesting that the window of opportunity for new entrants might be closing down as new DL research hubs become dominant. Finally, we study the regional drivers of DL clustering. We find that competitive DL clusters tend to be based in regions combining research and industrial activities related to it. This could be because GPT developers and adopters located close to each other can collaborate and share knowledge more easily, thus overcoming coordination failures in GPT deployment. Our analysis also reveals a Chinese comparative advantage in DL after we control for other explanatory factors, perhaps underscoring the importance of access to data and supportive policies for the successful development of this complex, `omni-use' technology.