Alex Rubin, Alan Omar Loera Martinez, Jake Dow, and Anna Puglisi
| July 2021
For the first time, a Chinese company—Huawei—is set to lead the global transition from one key national security infrastructure technology to the next. How did Washington, at the beginning of the twenty-first century, fail to protect U.S. firms in this strategic technology and allow a geopolitical competitor to take a leadership position in a national security relevant critical infrastructure such as telecommunications? This policy brief highlights the characteristics of 5G development that China leveraged, exploited, and supported to take the lead in this key technology. The Huawei case study is in some ways the canary in the coal mine for emerging technologies and an illustration of what can happen to U.S. competitiveness when China’s companies do not have to base decisions on market forces.
AI technologies will likely alter great power competitions in foundational ways, changing both how nations create power and their motives for wielding it against one another. This paper is a first step toward thinking more expansively about AI & national power and seeking pragmatic insights for long-term U.S. competition with authoritarian governments.
This issue brief explores whether artificial intelligence and AI-related certifications serve as potential pathways to enter the U.S. AI workforce. The authors find that according to U.S. AI occupation job postings data over 2010–2020, there is little demand from employers for AI and AI-related certifications. From this perspective, such certifications appear to present more hype than promise.
Ryan Fedasiuk, Emily S. Weinstein, and Anna Puglisi
| May 2021
“Science and technology diplomats” act as brokers as part of China’s broader strategy to acquire foreign technology. Each year, they file hundreds of official reports on their activities. This issue brief illuminates trends in the 642 reports filed by the S&T directorates of Chinese embassies and consulates from 2015 to 2020, quantifying which types of technologies the Chinese government is most focused on acquiring, and from where.
Artificial intelligence is said to be transforming the global economy and society in what some dub the “fourth industrial revolution.” This data brief analyzes media representations of AI and the alignments, or misalignments, with job postings that include the AI-related skills needed to make AI a practical reality. This potential distortion is important as the U.S. Congress places an increasing emphasis on AI. If government funds are shifted away from other areas of science and technology, based partly on the representations that leaders and the public are exposed to in the media, it is important to understand how those representations align with real jobs across the country.
Matthew Daniels, Autumn Toney, Melissa Flagg, and Charles Yang
| May 2021
The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.
Andrew Imbrie, Rebecca Gelles, James Dunham, and Catherine Aiken
| May 2021
The narrative of an artificial intelligence “arms race” among the great powers has become shorthand to describe evolving dynamics in the field. Narratives about AI matter because they reflect and shape public perceptions of the technology. In this issue brief, the second in a series examining rhetorical frames in AI, the authors compare four narrative frames that are prominent in public discourse: AI Competition, Killer Robots, Economic Gold Rush and World Without Work.
Leading U.S. companies are investing in the broad research field of artificial intelligence (AI), but where, specifically, are they making these investments? This data brief provides an analysis of the research papers published by Amazon, Apple, Facebook, Google, IBM, and Microsoft over the past decade to better understand what work their labs are prioritizing, and the degree to which these companies have similar or different research agendas overall. The authors find that major “AI companies” are often focused on very different subfields within AI, and that the private sector may be failing to make research investments consistent with ensuring long-term national competitiveness.
A lack of good data on the U.S. artificial intelligence workforce limits the potential effectiveness of policies meant to increase and cultivate this cadre of talent. In this issue brief, the authors bridge that information gap with new analysis on the state of the U.S. AI workforce, along with insight into the ongoing concern over AI talent shortages. Their findings suggest some segments of the AI workforce are more likely than others to be experiencing a supply-demand gap.
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