Many nations are racing to achieve a global innovation advantage in artificial intelligence (AI) because they understand that AI is a foundational technology that can boost competitiveness, increase productivity, protect national security, and help solve societal challenges.
Nations wherein firms fail to develop successful AI products or services are at risk of losing global market share. As Andrew Moore, former dean of computer science at Carnegie Mellon University and current head of Google Cloud AI stated, this part of the race will determine “who will be the Googles, Amazons, and Apples in 2030.” Nations that lag in AI adoption will see diminished global market share in a host of industries, from finance to manufacturing to mining. And nations that underinvest in AI R&D, particularly for military applications, will put their national security at risk. Consequently, nations that fall behind in the AI race can suffer economic harm and weakened national security, thereby diminishing their geopolitical influence.
The Centre for data innovation report in August 2019 compared China, the European Union, and the United States in terms of their relative standing in the AI economy by examining six categories of metrics—talent, research, development, adoption, data, and hardware. It finds that despite China’s bold AI initiative, the United States still leads in absolute terms. China comes in second, and the European Union lags further behind.
The authors explained their rationale for choosing these categories: First, nations with the requisite AI talent will be able to better develop and implement AI systems, attract businesses, and ensure their universities have enough talented AI professors to teach the next generation of AI researchers. Second, research will help nations expand AI innovation and solve problems related to domestic priorities and industries. Third, the number of AI companies and start-ups, combined with related investment capital, lays the groundwork for a strong AI industry that will continue to innovate. Fourth, adoption of AI systems will not only allow organizations to learn how to solve problems related to implementation, but generate demand for AI services, thereby likely helping domestic AI developers. Fifth, more and higher-quality data will create new opportunities to use machine learning in AI applications. Finally, leading in hardware will reduce nations’ dependency on other nations—something that, given the current trade dispute between China and the United States, may play an important role going forward.
The United States leads in four of the six categories of metrics this report examines (talent, research, development, and hardware), China leads in two (adoption and data), and the European Union leads in none—although it is closely behind the United States in talent. Out of 100 total available points in this report’s scoring methodology, the United States leads with 44.2 points, followed by China with 32.3 and the European Union with 23.5.
As David Wipf, a lead researcher at Microsoft Research in Beijing has said, “The future [of AI] is going to be a battle for data and for talent.” Lack of talent not only limits firms’ ability to deploy and adopt AI, it increases costs, thereby reducing competitiveness. Given the increased demand for AI talent in a wide range of industries, including transportation, finance, and manufacturing, the current shortage is likely to only grow in the near to moderate term.
AI Talent and R&D
It is not just the number of researchers that matters, but their quality. One measure of quality is the h-index, which measures the productivity and influence of researchers. This indicator examines the number of AI researchers ranking in the top 10 percent internationally according to their h-index. Through 2017, the European Union led with an estimated 5,787 researchers, ahead of the United States (5,158) and China (977). The United Kingdom (1,177), Germany (1,119), France (1,056), Italy (987), and Spain (772) combined for 5,111 such individuals.
Governments in China, the European Union, and the United States have announced or begun initiatives to improve and expand their AI talent. For example, in 2018, China’s Ministry of Education announced a plan to promote AI education. In response, several leading Chinese universities have created new AI departments and majors. The U.K. government has announced that it will pay up to £115 million ($129 million) for 1,000 students to earn AI doctorate degrees at 16 of its universities. President Trump issued an executive order that focuses on measures to expand fellowships, training programs, and funding for early-career university faculty conducting AI R&D.
An analysis of the data shows the United States leading in AI research, both because of its immense spending on R&D and its elite research organizations. Nonetheless, China is catching up to the United States and European Union not only because it produces more research, but because it has begun producing higher-quality research.
The United States leads in research in part because it has elite organizations. For example, the top-five software and computer services firms for R&D are U.S. firms. Another way to assess the quality of research a nation produces is to examine the impact of its organizations publishing the most AI papers. The United States leads in this measure as well. U.S.-based organizations that published the most AI papers between 2013 and 2017 were Carnegie Mellon University, the Massachusetts Institute of Technology, Microsoft, IBM, and Stanford University. Collectively, these five- organizations had an FWCI of 4.0, which was significantly higher than the FWCI of the top-five EU (1.9) and Chinese (1.4) organizations.
The European Union is a close second in AI talent, but it may continue to fall behind in commercially leveraging AI because it has less AI talent in businesses than the United States, the United States is attracting significant amounts of foreign talent (including European talent), and China is implementing robust plans to increase its AI talent.
China, however, is clearly behind both the United States and the European Union in high-quality AI talent. Several European Union member states, including Italy, had more AI researchers ranked in the top 10 percent internationally than China as of 2017.
China is ahead of the European Union in AI and appears to be quickly reducing the gap between itself and the United States. It has more access to data than the European Union and the United States, which is important because many of today’s AI systems use large datasets to train their models accurately.
U.S. firms perform strongly in patents and dominant AI acquisitions. For example, Microsoft and IBM have applied for more patents than any other entity in 8 of 15 subcategories of machine learning, including supervised learning and reinforcement learning. The Chinese Academy of Sciences has applied for the most patents in deep learning, however, and Siemens (Germany) has applied for the most patents in neural networks. Nonetheless, a U.S. firm leads in patent applications in 12 of 20 fields, including agriculture (John Deere), security (IBM), and personal devices, computing, and human-computer interaction (Microsoft). In addition, between 2012 and 2016, IBM led in AI patent applications (3,677) globally, with Google parent company Alphabet (2,185) and Microsoft (1,952) ranking in the top five.
Large Chinese Internet firms likely have a data advantage compared with their Western counterparts for at least two reasons. First, services in the West are relatively divided between firms. For example, Amazon users are able to buy groceries but not book a hotel. Chinese technology companies, on the other hand, have created all-in-one super apps. For example, Kai-Fu Lee has written that WeChat, an app owned by Chinese technology company Tencent, allows users to “hail a taxi, order a meal, book a hotel, manage a phone bill, and buy a flight to the United States, all without ever leaving the app.” In the United States, these services, and thus the data, are divided between such firms as Uber, Postmates, Expedia, Verizon, and Venmo.
Second, Chinese technology companies have embedded themselves in traditionally off-line activities. For example, Didi, the Chinese equivalent of Uber, has bought gas stations and auto repair shops. In addition, Meituan Dianping, whose origins are similar to that of Yelp, not only provides users with a platform to compare businesses, but also handles food delivery. Chinese Internet companies are therefore afforded the opportunity to collect a greater variety and depth of data than their American counterparts.
It should be noted, however, that the broader global reach of some U.S. technology giants provides them with their own data advantage. For example, Facebook has more than 2 billion users, while WeChat has only 1.1 billion users. Should Chinese firms achieve more success internationally, such as with the social media video app TikTok, the U.S. advantage will diminish
China’s large population gives it a significant data advantage over other nations. However, China limits the size of this advantage through a weak open data culture and a lack of standardized data formats.
AI Industry: Global firms and startups
To experience the full benefits of AI, nations must have healthy AI ecosystems that lead to the development of innovative AI technologies and firms. For example, nations must have sufficient venture capital and private equity funding to connect inventors with the money, expertise, and contacts necessary to develop and sell their products or services. In addition, the number of firms indicates the health of a nation’s ecosystem. Finally, patents indicate the ability of a firm or nation to innovate.
The United States led in every AI-development indicator, suggesting it is better positioned than China and the European Union to continue to develop leading global firms in AI. Patent and acquisition data also reveals that the United States already has a significant lead in developing world-class AI firms. However, China, partially due to its robust venture capital and private equity ecosystem, is catching up to the EU and the United States. On the contrary, the EU, despite currently ranking slightly higher than China in AI development, likely lacks the funding to seriously challenge U.S. supremacy.
Similar to other technology-based start-ups, AI start-ups can be an important driver of a nation’s economic growth and competitiveness. Roland Berger, a global consultancy, and Asgard, a Berlin-based investment firm, categorized AI start-ups as firms that produce a primary product or service that utilizes AI, excluding hardware. The firms’ research found that the United States was home to 1,393 AI start-ups in 2017, ahead of the European Union (726 start-ups) and China (383 start-ups).
In venture capital and private equity funding, Chinese AI start-ups received more funding than U.S. start-ups in 2017, but not in 2016 or 2018.
In addition, all ten of the companies that lead in AI company acquisitions are based in the United States. The leading companies include Alphabet (19), Apple (16), Microsoft (10), Amazon (7), and Facebook (7). These acquisitions have bolstered U.S. firms, with multiple of the acquired companies having provided significant research and commercial offerings since their purchase. For example, Alphabet acquired DeepMind, one the world’s leading AI organizations, for $500 million in 2014. Since its acquisition, DeepMind has developed an AI system that can analyze eye scans to make diagnoses (e.g., hemorrhages), increased the value of wind energy from Google turbines by 20 percent using AI, and released an interactive dataset of more than 100,000 panoramic images to advance the development of AI systems that can navigate using visual cues instead of maps.
Similarly, Apple acquired Siri for $200 million in 2010, and Amazon acquired Evi Technologies for $26 million in 2013. Amazon used its acquisition’s technology to develop its virtual assistant, Alexa, and has since sold more than 100 million devices that incorporate it.
Semiconductors and AI Chips
AI systems rely on semiconductor devices, such as integrated circuits, that can perform large numbers of operations per second. Indeed, graphics processing units (GPUs), which are circuits that perform mathematical operations in parallel, have catalyzed recent AI developments. In addition, technologies such as supercomputers, which combine processing units such as GPUs and central processing units, can expand the capabilities of AI systems through massive computational power. For example, researchers have combined supercomputers and machine learning techniques to model climate change as well as the merging of blackholes. While China is rising, the European Union is falling. European industry still has market share in areas such as sensors, but it has abandoned the production of advanced digital semiconductors.
Several Chinese AI chip start-ups have recently received hundreds of millions of dollars in funding and firms such as Huawei have developed impressive chip designs. In addition, several leading Chinese technology firms, including Baidu, Tencent, Alibaba, and Huawei, are developing AI-optimized integrated circuits, which large U.S. technology firms are also doing. Huawei—which in particular has demonstrated some design prowess—and Apple were the first firms to create a smartphone processor that uses 7 nanometer (nm) process technology, which refers to the size of the transistors in a processor. Smaller transistors more efficiently use power than larger ones and increase the potential number of transistors in a processor, thereby making it potentially more powerful.
U.S. firms are also developing specialized AI chips, such as Google’s Tensor Processing Unit and Luminous Computing’s optical microchip—which uses different colors of light to move data. Many experts believe AI chips designed specifically for AI applications, such as autonomous vehicles or facial recognition, will outperform such proven technologies as GPUs. As a result, non-semiconductor firms, such as Apple, Alphabet, and Amazon, are designing their own AI chips to meet their specific needs, which could increase the performance of their AI systems and thereby provide them with a competitive advantage
Nonetheless, the complexity of developing chips, China’s shortage of talent, and the lack of multiple Chinese semiconductor firms being in the top 15 globally for sales indicate China still needs to make significant progress in order to match the United States in semiconductors. Nonetheless, China’s development of well-funded AI chip start-ups and advancements in chip design indicate it may be able to close at least some of the gap to the United States.
And some experts have argued China is better positioned to compete in the AI chip market than in the overall semiconductor market. For example, Horizon Robotics, which develops AI chips for robots, received $600 million in a 2018 Series B funding round led by SK Hynix, a world-leading South Korean semiconductor firm. Similarly, Bitmain, which originally developed chips for bitcoin mining, has developed an AI chip and received nearly $765 million in funding between 2017 and 2018. Finally, Cambricon Technologies, which developed the world’s first commercial deep learning processor for phones in 2016, received $100 million from the Chinese government-backed State Development & Investment Corporation in 2018.
China Closing the Gap with US
The United States leads for several reasons. First, it has the most AI start-ups, with its AI start-up ecosystem having received the most private equity and venture capital funding. Second, it leads in the development of both traditional semiconductors and the computer chips that power AI systems. Third, while it produces fewer AI scholarly papers than the EU or China, it produces the highest-quality papers on average. Finally, while the United States has less overall AI talent than the European Union, its talent is more elite.
Nonetheless, China has made clear progress relative to the United States in most metrics, and significantly outpaces the European Union in funding and AI adoption. China has a large talent base—due to its massive population—but still has a shortage of AI talent, lacks elite talent, and frequently loses workers who leave to pursue education abroad
Researchers found several reasons why China may be able to reduce both the talent gap and the talent gap may have diminishing importance.
First, China is investing in AI education. In 2017, the State Council, the chief administrative body in China, released a plan calling for the creation of an AI academic discipline. In 2018, the Ministry of Education launched multiple initiatives to boost education, and the combined initiatives include plans to develop 50 AI research centers, world-class online courses, and a 5-year plan to train more than 500 instructors and 5,000 students.
Three of China’s top universities—Tsinghua University, USTC, and Shanghai Jiao Tong University—have already significantly increased the number of students enrolled in AI and machine learning courses since 2016. For example, between 2016 and 2018, USTC increased its AI and machine learning course enrollment from 1,745 to 3,286 students.
Second, Chinese researchers can and do quickly replicate advanced algorithms developed by other nations because AI researchers frequently detail the architecture of their AI model, and how they implemented and trained it, on openly available prepublications websites. Anecdotal evidence also suggests Chinese researchers translate English AI publications significantly more often and faster than Western nations translate papers in Chinese, thereby creating an information asymmetry.
Third, AI researcher and venture capitalist Kai-Fu Lee has argued that China’s lack of top-end talent is not a significant barrier to it leading in AI, stating “[T]he current age of implementation [AI application commercialization] appears well-suited to China’s strengths in research: large quantities of highly-skilled, though not necessarily best-of-best, AI researchers and practitioners.” Lee believes breakthroughs such as deep learning occur once every several decades, and AI has entered an age in which data will be the decisive factor that determines the ability of AI systems.
The European Union
The European Union has the talent to compete with the United States and China. Indeed, it has more AI researchers than its peers, and typically produces the most research as well. However, there is a disconnect between the amount of AI talent in the EU and its commercial AI adoption and funding. For example, AI start-ups in the United States and China both received more venture capital and private equity funding in 2017 alone than EU AI start-ups received in the three years covering 2016 through 2018.
Moreover, there are signs the EU will remain a laggard in developing advanced chips for AI, which are costly and have a long development cycle. First, no EU semiconductor firm ranks in the top 10 for R&D spend. Second, several of the most innovative chip designs are coming from large yet traditionally digital U.S. and Chinese firms, such as Alphabet, Facebook, and Baidu. But EU digital start-ups have struggled to gain scale due to the continent’s fragmented markets and competition regulations.
As a result, there are fewer European equivalents to Alphabet and Baidu that have the money and motivation to design AI chips. Third, non-EU firms are acquiring promising European semiconductor design firms. Indeed, Softbank, a Japanese conglomerate, purchased ARM, a U.K. semiconductor company, for $32 billion in 2016. Similarly, Canyon Bridge, a Chinese-government-backed private equity firm, purchased Imagination Technologies, a semiconductor designer also based in the United Kingdom, for £550 million ($616 million) in 2017
The European Union’s laggard position reduces its ability to not only enjoy the economic and social benefits of AI, but also influence global AI governance, which is a goal of the European Commission.