Using finely tuned hardware, a dedicated network, and large data storage, supercomputers have long been used for compute-intensive projects that require large amounts of data processing. With the rise of artificial intelligence and machine learning, the demand for these powerful computers is increasing and hence the processing power is increasing rapidly. As such, the growth of AI is inextricably linked with the growth in processing power of these high-performance devices.
Supercomputers are nothing new. The term appeared in the late 1920s and the CDC 6600 (released in 1964) is generally regarded as the first true supercomputer. The first supercomputers only used a few extremely powerful processors, but in the late 1990s, computer scientists realized that the combination of thousands of commercial processors would result in the most processing power. State-of-the-art supercomputers have more than 60,000 massively parallel processors to approach petaflopic performance.
Recognizing the security threats posed by supercomputers, a consortium of countries, including the United States, Germany and South Korea, developed the Wassenaar arrangement, which restricts the sale of, among other things, supercomputers that can be used for military purposes. Nonetheless, supercomputers can be found in most countries pursuing AI research.
As such, much of the development of AI rests on two pillars: technologies and the availability of human capital. Our previous reports for Brookings, “How Different Countries See Artificial Intelligence” and “Analysis of Artificial Intelligence Plans in 34 Countries,” detailed how countries approach national AI plans and how to interpret those plans. In a follow-up article, “Winners and Losers in Realizing National Aspirations in Artificial Intelligence,” we discussed how different countries are achieving their aspirations along both technology and people-centric dimensions. In our most recent article, “The People’s Dilemma: How Human Capital Is Driving or Constraining the Achievement of National AI Strategies,” we discussed the people dimension and, in this article, we’ll take a look at how each country is ready to respond to their AI. objectives of the second pillar, the technological dimension.
Development of technological factors
In order to analyze the technological readiness of each country, we have assembled a country-level dataset containing: number and size of supercomputers in each country, the amount of Public and private spending on AI initiatives in each country, the number of AI startups in each country, and the number of AI patents and conference papers academics from each country produced. This resulted in ten separate data elements.
As with our previous analyzes, we performed factor analysis to determine if any of the data elements were closely related. Closely related elements can be mathematically combined into a composite factor, which facilitates interpretation. In this factor analysis, two clear factors emerged. The first factor contained country rankings based on theoretical maximum computing performance, number of processing cores, number of supercomputers, and maximum LINPACK performance achieved; ranking of countries for the number of conference papers and journal articles; and the country’s ranking for the number of patents. The second factor concerned private and public investment in AI. One area, AI startups, was not closely associated with either factor and was excluded from further analysis.
It is clear that all areas of the first factor are either directly related to the technology or to its use in research. Accordingly, we call this factor Technology and Research. The second factor is only focused on investments, which is why we call this field Investments.
Figure 1 shows where a selected group of countries lies along these sub-dimensions.
We interpret and name the quadrants as follows. The countries in the upper right corner we call them “Leaders”; these have both a robust technological and research platform (factor one) and substantial public / private investments (factor two). The countries in the lower right quadrant that we call “Technology Skilled”. These countries currently have a strong technological and research platform, but lack solid public and private investments. The countries in the upper left quadrant that we call “Positioned Funding” are countries that have a large flow of funding but are lagging behind in terms of technology and research. Finally, we call the lower left quadrant “Unprepared,” which reflects countries that lack both technology and research, and also lack funding.
United States and China
The race for technological dominance is clearly a two-horse race between the United States (94th percentile for technology and research and 96th percent for investment) and China (94th percentile for technology and research and 91st percentile for technology and research for investments). While the United States has a very slight lead overall, both countries are in the top three positions for each of our data elements. This is not surprising, as the size of the US and Chinese economies (the largest and second largest respectively at $ 20 trillion and $ 15 trillion respectively) eclipse Japan, which is the third largest economy (4 900 billion dollars). As a result, we see no technological barriers for either country to continue to excel.
United Kingdom, France, Japan and Germany
United Kingdom (75th percentile in technology and research and 88th percentile in investments), France (75th percentile in technology and research and 81st percentile in investments), Japan (87th percentile in technology and research and 75th percentile in investments) and Germany (83rd percentile in technology and research and 68th percentile in investments) form a strong group of hunting for the two leaders. Of the four countries, we see the UK as being in the strongest position to challenge the US and China and this is based on their larger investments in technology. We believe these investments will allow them to close the gap faster than other countries are able to. However, we cannot ignore the fact that Japan’s economy is the largest of the four and it suggests that if they decide to do so, they can quickly step up their efforts.
India, Canada, South Korea and Italy
India (57th percentile in technology and research and 78th percentile in investments), Canada (68th percentile in technology and research and 60th percentile in investments), South Korea (71st percentile in technology and research and 60th percentile in investments) and Italy (71st percentile in technology and research and 60th percentile in investments) complete the Leaders quadrant. Like the UK, India is also well positioned financially and is expected to separate from the other four countries quickly.
Tale of savings
Almost without exception, there is a strong relationship between the economic size of the country and its position in our quadrants. The United States certainly capitalizes on its status as the world’s largest economy and makes significantly more technology investments than almost any other country; China is just behind. While we were concerned with the position of the United States from a human point of view, there are no similar concerns from a technological point of view. America remains a global leader in digital innovation, and supercomputers are no exception.
The uncomfortable reality for the United States is that its economic strength is very helpful in making the investments in technological infrastructure that are necessary but not sufficient to be successful in the pursuit of AI, but this economic strength has little to do with it. or not affect the other necessary element – the ability to attract the people necessary to develop and implement its AI strategy. In contrast, China also has the economic strength for the necessary investment in technological infrastructure, but also has a large population to provide the energy which is also needed. In other words, China has the two conditions necessary for the success of AI while the United States has only one. As such, China is currently in much better shape than the United States in meeting its AI goals, and without human changes, the United States will fall further and further behind.
In our next article we will focus exclusively on what the United States needs to do to improve its position and in our following articles we will look at different team strategies that leverage each country’s respective strengths.
: These are: Rpeak (ranking of the country by the theoretical maximum IT performance), Cores (ranking of the country by the number of processing cores), Count (ranking of the country by the number of supercomputers), Rmax (ranking of the country by the maximum LINPACK floating-point computation performance achieved), AI Startups (country ranking for the number of AI-based startups), Private Investment (country rate for private investments in AI), Public Investments (country ranking for public investment in AI), AI Conference Papers (country ranking for the number of conference papers on AI), AI Journal Papers (ranking by country for the number of articles on the IA) and AI Patents (ranking by country for the number of AI patents).