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The impact of artificial intelligence on international trade

人工智能对国际贸易的影响

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【来源】: 布鲁金斯学会
【时间】: 2018-12-13
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Artificial intelligence (AI) stands to have a transformative impact on international trade. Already, specific applications in areas such as data analytics and translation services are reducing barriers to trade. At the same time, there are challenges in the development of AI that international trade rules could address, such as improving global access to data to train AI systems. The following provides an overview of some of the key AI opportunities for trade as well as those areas where trade rules can help support AI development.

What do we mean by artificial intelligence?

Before proceeding to the impact of AI on trade, it is important to clarify what is meant by AI. More specifically, that there is a key difference between narrow AI such as translation services, chatbots, and autonomous vehicles and general AI—“self-learning systems that can learn from experience with humanlike breadth and surpass human performance on all tasks.” General AI raises broader existential concerns, such as how to align the goals of such a system with our own to prevent catastrophic outcomes,1 but general AI remains a technology still to be developed in the distant future.

To understand the potential significance of narrow AI for trade, it is also important to briefly consider its core parts. In particular, narrow AI is based on machine learning, which uses large amounts of data and powerful algorithms to develop increasingly robust predictions about the future.2 The data used for machine learning can be either supervised—data with associated facts, such as labels—or unsupervised—raw data that requires the identification of patterns without prior prompting.3 This includes reinforcement learning—where machine-learning algorithms actively choose and even generate their own training data.

Another key development underpinning narrow AI is the Deep Neural Network (DNN). DNNs are comprised of layers of nonlinear transformation node functions, where the output of each layer becomes an input to the next layer in the network. Each layer is highly modular, making it possible to take a layer optimized for one type of data (say, images) and to combine it with other layers for other types of data (e.g., text).4 Deep Neural Networks combine multiple machine learning tasks—creating what is referred to as general purpose machine learning (GPML)—which allows AI to effectively live on top of the types of chaotic data that humans are able to digest, such as video, audio, and text.

Narrow AI also includes specific tools such as out-of-sample validation to validate models, stochastic gradient descent for training models on streams of data, and graphical processing units (GPUs)—originally developed for video games but which have proven well-suited to support the types of massive parallel computations needed to train DNNs.5

Applying these developments in a real-world context requires large data sets to initialize AI systems. Here, quantity matters because machine learning needs to be able to incorporate into future predictions as many possible past outcomes as possible. This means that access to the tails of data—less usual and irregular data—matters.

The impact of AI on economic growth and international trade

The development of AI will affect international trade in a number of ways. One is the macroeconomic impacts of AI and the related trade effects. For instance, should AI increase productivity growth, then this will increase economic growth and provide new opportunities for international trade. Current rates of productivity growth globally are low and there are various suggested causes.6 One reason for low productivity growth particularly relevant for understanding the potential link with AI is that it takes time for an economy to incorporate and make effective use of new technologies, particularly complex ones with economy-wide impacts such as AI.7 This includes time to build a large enough capital stock to have an aggregate effect and for the complimentary investments needed to take full advantage of AI investments, including access to skilled people and business practices.8

AI will also affect the type and quality of economic growth, with international trade implications. For instance, AI is likely to accelerate the transition towards services economies.

AI will also affect the type and quality of economic growth, with international trade implications. For instance, AI is likely to accelerate the transition towards services economies. This is a corollary to concerns about the impact of AI and jobs, as AI is likely to expand automation and speed up job losses for low-skill, blue-collar workers in manufacturing fields.9 In parallel, AI will also emphasize particular worker skills as it is used to add value to production and products. This should lead to further expansion of the share of services in production as well as international trade.

Specific AI applications to international trade

AI and global value chains

AI is already having an impact on the development and management of global value chains. It can be used to improve predictions of future trends, such as changes in consumer demand, and to better manage risk along the supply chain. By allowing business to better manage complex and dispersed production units, such tools improve the overall efficiency of GVCs. For example, business can use AI to improve warehouse management, demand prediction, and improve the accuracy of just-in-time manufacturing and delivery. Robotics can increase productivity and efficiency in packing and inventory inspection. Business can also use AI to improve physical inspection and maintenance of assets along supply chains.

The development of GVCs will be affected by the broader trends toward using AI to develop smart manufacturing. For instance, the German-led conception of industry 4.0 is based on sensors, IoT, and cyber-physical-systems that connect machines, material, supplies, and customers. This will include capacity at the factory level of predictive machines and self-maintenance, complete communications between companies along the supply chain, and the ability to manufacture according to customer specifications, even in small or single batches.10 Such developments could strengthen and extend GVCs. For example, smart manufacturing with its emphasis on connectivity could open up GVCs to more specific participation by specialized service suppliers in areas such as R&D, design, robotics, and data analytics tailored to discrete tasks in the supply chain.

Yet AI could also create trends toward on-shoring of production. Broader automation opportunities as well as scaling of 3D printing could reduce the need for extended supply chains—particularly those that rely on large pools of low-cost labor. The result could accelerate the process Dani Rodrik describes as “premature industrialization” in developing countries.11

Trade using digital platforms

Another area where AI is already being deployed is on digital platforms such as eBay. For small business in particular, digital platforms have provided unprecedented opportunity to go global. In the U.S., for instance, 97 percent of small businesses on eBay export, compared to just 4 percent of offline peers.12

For small business in particular, digital platforms have provided unprecedented opportunity to go global.

AI-developed translation services are further enabling digital platforms as drivers of international trade. For example, as a result of eBay’s machine translation service, eBay-based exports to Spanish-speaking Latin America increased by 17.5 percent (value increased by 13.1 percent).13 To put this growth into context, a 10 percent reduction in distance between countries is correlated with increased trade revenue of 3.51 percent—so a 13.1 percent increase in revenue from eBay’s machine translation is equivalent to reducing the distance between countries by over 35 percent.

Trade negotiations

AI also has the potential to be used to improve outcomes from international trade negotiations. For instance, AI could be used to better analyze economic trajectories of each negotiating partner under different assumptions, including outcomes contingent on trade

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