What Do We Mean By Artificial Intelligence?
General AI refers to systems that can self-learn from experience with “humanlike breadth” and surpass human performance on tasks. General AI raises broad existential concerns, but remains a technology in the distant future.
“General AI raises broad existential concerns, but remains a technology in the distant future. In contrast, narrow AI includes translation services, chatbots (online customer service performed by a computer), and autonomous vehicles.”
In contrast, narrow AI includes translation services, chatbots (online customer service performed by a computer), and autonomous vehicles. They are technologies based on machine learning that use large amounts of data and powerful algorithms to develop increasingly robust predictions about the future. Applying narrow AI in a real-world context requires large data sets because machine learning needs to be able to incorporate into future predictions as many possible past outcomes as possible. The data can be supervised, such as data associated with labels, or unsupervised. Unsupervised refers to raw data that requires the machine identify patterns without prompting.
The Impact of AI on Economic Growth and International Trade
To the extent AI boosts productivity growth, it will support greater economic growth and provide new opportunities for international trade. But it will take time for economies to incorporate and make effective use of new AI technologies which require significant investments, access to skilled people, and a transformation in business practices.
AI is likely to drive automation in production processes, speeding up job losses for low-skill, blue-collar workers in manufacturing fields, while at the same time generating the need for different worker skills as AI is deployed to add value to production and products. In this way, AI will further expand the share of services in production as well as international trade, accelerating the transition towards services-based economies.
“AI will further expand the share of services in production as well as international trade, accelerating the transition towards services-based economies.”
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. Warehouse stocks can be managed more efficiently to improve the accuracy of just-in-time manufacturing and delivery. Robotics aid in packing and inventory inspections.
Smart manufacturing leverages sensors and Internet-connected machines, materials, and supplies to enable predictive self-maintenance and quick adaptation to customer demands. As more industries incorporate smart manufacturing, greater connectivity could open up global value chains to more specific participation by specialized service suppliers in R&D, design, robotics, and data analytics tailored to discrete tasks along the supply chain.
Conversely, 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.
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., 97 percent of small businesses on eBay export, compared to just four percent of offline peers.
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 in volume and 13.1 percent in revenue.
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 and outcomes, including growth pathways under various forms of trade liberalization. Brazil has already established an Intelligent Tech & Trade Initiative that includes using AI to improve trade negotiations.
Trade Challenges Ahead for AI Development
Access to data for AI
AI systems that can respond to diverse challenges and different population groups requires access to global data. For example, developing AI in areas such as health care requires access to global health data reaching beyond the limits of national populations to improve the accuracy and relevance of AI systems.
Expanded use of AI depends on other digital technologies like cloud computing, big data, and the internet-of-things. These digital technologies rely on cross-border data flows. Data localization measures that restrict global data transfers will hit AI directly, by providing less training data, and indirectly, by undercutting the building blocks on which AI is developed.
“Maintaining domestic privacy standards is a key reason that governments are currently reducing the flow of personal data across borders.”
Privacy and AI
Maintaining domestic privacy standards is a key reason that governments are currently reducing the flow of personal data across borders.
For example, the EU General Data Protection Regulation (GDPR) prohibits transfers of personal data to countries that have not been deemed “adequate” by the European Commission. GDPR limits on the processing and use of personal data could adversely impact the development of AI capabilities. For instance, under GDPR, personal data can only be used for the purpose for which it was collected, which means that personal data collected as part of a transaction cannot then be used to train AI to improve how the service is delivered.
“GDPR limits on the processing and use of personal data could adversely impact the development of AI capabilities.”
On the other hand, strong privacy protections will be required if people are going to be able to trust living their lives online, which includes providing immense amounts of personal data for AI learning. The key challenge will be to design privacy rules that do not create unnecessary restrictions on access to and use of data.
“The key challenge will be to design privacy rules that do not create unnecessary restrictions on access to and use of data.”
Standards and AI
AI-based industrial products will require the development of a range of new standards. Autonomous vehicles, for example, will beget a new set of vehicle manufacturing and safety standards. The development of different domestic standards across countries could increase costs for foreign manufacturers who have to retool in order to export and will engender international discussions around interoperability to reduce barriers to trade in products that incorporate AI.
Protection of source code
Requiring access to source code as a condition of investment or market access poses another challenge to the development of AI. Requiring such access was identified by the Office of the United States Trade Representative (USTR) as part of the broader issue of forced technology transfer in China. As AI is based on algorithms, conditioning market access on providing access to source code operates as an international trade barrier that reduces the diffusion of AI globally.
“As AI is based on algorithms, conditioning market access on providing access to source code operates as an international trade barrier that reduces the diffusion of AI globally.”
Intellectual property protection and AI
The development of AI also raises intellectual property (IP) issues. Training data often needs to be copied and edited for use. Depending on how the data is collected, this could involve unauthorized copying of thousands of protected works. In the United States, it may be that relying on the “transformative” or “non-expressive” fair use exception to copyright protection will provide legal cover for such use of data. Fair use provides a flexible principles-based set of copyright exceptions. Yet, even in the United States, whether fair use exceptions will cover some of the more complex uses of data to train AI remains to be tested.
“The development of AI also raises intellectual property (IP) issues. Training data often needs to be copied and edited for use. Depending on how the data is collected, this could involve unauthorized copying of thousands of protected works.”
Furthermore, fair use exceptions or similar copyright flexibilities do not exist in many other countries. From an international trade perspective, this means that legal copying of data to develop AI in the United States might be deemed illegal in other countries, creating a barrier to deployment of AI in these countries.
AI and trade in goods
While policy discussions surrounding the future development of AI tend to focus on access to data, standards, and IP, access to goods will also affect the diffusion of AI globally. For example, CPUs are a key hardware that underpin the deployment of narrow AI, underscoring the ongoing role for eliminating tariffs on trade in the technologies needed for AI development.
“While policy discussions surrounding the future development of AI tend to focus on access to data, standards, and IP, access to goods will also affect the diffusion of AI globally.”
The Future of AI Needs Trade Rules Now
AI is already changing global value chains and international trade patterns. Trade rules crafted today in the WTO or free trade agreements will play a critical role in further shaping how AI is further developed and deployed globally.
* This article was published on TradeVistas and is a shortened version of the original report, which can be found in full on the Brooking Institution’s website. Use of the article is subject to license under TradeVistas’ Reprint Policy and Copyright Notice.