- By Erik R. Peterson
- Executive Corner
Summary
By Erik R. Peterson
Much attention is now focused on the technologies underpinning the fourth industrial revolution (4IR)-artificial intelligence (AI), 3D printing, advanced robotics, the Internet of Things (IoT), and augmented reality. These are reshaping production processes and redefining global value chains. While the global competitive landscape is intensifying across a variety of digital technologies, two areas in particular stand out in the short term: 5G wireless networks and AI.
Designers are creating 5G wireless networks to facilitate instant, reliable connections between billions of devices dramatically faster than is possible today. Greater bandwidth will allow it to support massive machine-to-machine communications, such as between cellphones, sensors, smart machinery and appliances, and other IoT devices. 5G will also enable a range of new and future applications in the other next-generation 4IR technologies, many of which will be the backbone of innovations in manufacturing and production processes.
The large-scale deployment of 5G wireless networks will therefore be one of the most intense-and consequential-technology races in the near term. National competitiveness will increasingly be determined by the level of 4IR technological adoption and innovation, which in turn will depend on the depth and breadth of national 5G wireless networks.
But 5G deployment is not without its challenges. As urban areas prepare to install 5G infrastructure, its uneven deployment is likely to become a limiting factor for the expansion of 5G-dependent technologies. More broadly, disparities in access to 5G could exacerbate regional inequities in digital connectivity.
One of the greatest challenges to a hyperconnected 5G future is lack of cybersecurity. According to a 2018 study conducted by Ericsson, 79 percent of business leaders across 10 industries regard data security and privacy as their top concern for 5G adoption. Such cybersecurity concerns are a matter of national security. As 5G enables the explosion of IoT, so too will it provide greater opportunities for potential exploitation of these devices by a range of malicious national and subnational actors.
South Korea, the U.S., and China appear to be the leaders in the race to 5G. The E.U. appears to be falling behind, which the chief executive of Ericsson recently attributed to regulations and high costs.
AI is the other area of technology driving intense national competition. From digital assistants and self-driving cars to job candidate selection and personalized medicine, AI-driven technologies are more available than ever before in the business, healthcare, retail, and entertainment sectors. Data security is one of the most common business use cases, with cybersecurity companies leveraging AI to detect malware and search for anomalies in how data is processed in the cloud.
These advances also come with concerns. As AI becomes more capable of performing both blue- and white-collar jobs, worker displacement seems inevitable. Regardless of the precise time frame and scope of job losses, AI will certainly have a transformative effect on the global workforce. Governments, companies, and educational institutions need to develop strategies to give workers the AI skills necessary to compete in the new environment.
AI also presents a new range of vexing ethical questions, such as concerns about bias and discrimination in AI algorithms. There are also worries that the growth of AI will cause even greater levels of income inequality as owners of AI-enabled companies reduce their human workforce. Despite these concerns, AI is generally acknowledged to be one of the major forces in the near term that will define national competitiveness in the coming decades.
The U.S. is the leader in three of the four main drivers of AI, according to the University of Oxford: hardware, research and algorithms, and the commercial AI sector. But on the final driver, data, China is far ahead of the pack. China's large population and data-sharing agreements between leading Chinese technology companies and the government have enabled significant real-world AI testing. But the race is by no means over.
Many governments, including all five of the world's largest economies, have released national-or in the case of the E.U., supranational-strategies in recent years to develop AI. Each is unique, but common themes include investment in R&D, efforts to upgrade skills and workforce training, greater private-sector innovation, and evaluation of AI's ethical implications.
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