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The impact of artificial intelligence on international trade

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.

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.

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.

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.

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.

 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.

For instance, AI could be used to better analyze economic trajectories of each negotiating partner under different assumptions, including outcomes contingent on trade negotiation (growth pathways under various forms of trade liberalization), how these outcomes are affected in a multiplayer scenario where trade barriers are adjusted down at different rates, as well as predicting the trade response from countries not party to the negotiation.

Here, USMCA makes progress, including a recognition by the Parties of the importance of access to government information for economic and social development, and to the extent possible making government data accessible in machine-readable and open format.16 Commitments to cross-border data flows in trade agreements are balanced with scope for governments to restrict data flows in order to achieve legitimate public policy objectives.

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.18 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.

In the U.S., 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.20 Fair use provides a flexible principles-based set of copyright exceptions.21 Fair use exceptions have been a significant legal underpinning in the development, and demise, of digital business models in the U.S.22 Yet, even in the U.S., whether fair use exceptions will cover some of the more complex uses of data to train AI remains to be tested.23 Even in the U.S., whether fair use exceptions will cover some of the more complex uses of data to train AI remains to be tested.

John Wiley & Sons : The Learning House Releases Research Brief on Artificial Intelligence in Higher Education

Louisville, KY (December 6, 2018) - Learning House, a Wiley brand and leading online program manager, today released Artificial Intelligence in Higher Education, a research brief summarizing AI developments in higher education.

The brief assesses current, future and in-development opportunities for AI in four areas: 1) student acquisition, 2) learning and instruction, 3) student affairs and 4) institutional efficiency.

Additionally, the report highlights how AI will affect what students need to learn and any additional certificates or degrees universities should offer to meet this need and suggests necessary policy changes to fully realize those benefits.

The brief features four case studies of higher education institutions piloting or implementing AI technology as well as highlighting more than a dozen specific AI-related tools or systems that are either in development, testing or already in use in universities around the globe.

Justin Klutka, senior vice president of technology at Learning House and co-author of the research brief notes that, 'AI solutions exist in the market that free up brainpower and time, allowing us to pursue a rigorous, adaptive and personalized experience for students.

In addition, the report highlights important policy guidance and recommendations that are likely to accelerate AI innovation or, if unrealized, stifle its growth and adoption.

'The biggest questions are which schools move first to embrace the power of AI, how those decisions will aid their students and enhance their stability and success and whether we can create or adjust policies that allow us to reap the full rewards of AI technology in education.'

Our online scientific, technical, medical, and scholarly journals, and our digital learning, assessment, certification and student-lifecycle services and solutions help universities, academic societies, businesses, governments and individuals to achieve their academic and professional goals.

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