Deeply sort out the AI development strategies of these 10 countries
The following is the Deeply sort out the AI development strategies of these 10 countries recommended by recordtrend.com. And this article belongs to the classification: artificial intelligence.
At present, almost all major countries in the world have formulated their own AI strategies.
This paper will sort out the strategic ideas of more than a dozen countries that are in a leading position in the world and clearly put forward the national AI development strategy, including China, the United States, France, Japan, Singapore, etc. This will help us estimate the development prospect of AI.
China
China may be in 2030 It became an AI power in, but it is still in a position to catch up with the United States in terms of total investment, human resources and experience in the AI field. In 2012-2016:
1. China’s total investment in AI is US $2.6 billion, while the United States is US $17.2 billion.
2. There are 39000 AI human resources in China and 78000 in the United States.
In addition, as the most populous country in the world, China has a wealth of information and data. It is estimated that by 2020, China will have 20% of the global data (equivalent to 44 ZB). To this end, the government has published the new generation AI development plan, which is as follows:
Promote the close cooperation between the government, academia and industry, formulate AI development strategic objectives and make progress:
2020: the overall technology and application of AI will keep pace with the world’s advanced level. The scale of AI core industries exceeds US $22.5 billion, driving the scale of related industries to exceed US $150.8 billion.
2025: major breakthroughs will be made in AI basic theory, and some technologies and applications will reach the world leading level. The scale of AI core industries exceeded US $60.3 billion, driving the scale of related industries to exceed US $754 billion.
2030: AI theory, technology and application will generally reach the world leading level and become the world’s main AI innovation center. The scale of AI core industries exceeds 150.8 billion US dollars, driving the scale of related industries to exceed 1.5 trillion US dollars.
Six key tasks were identified and fully implemented, including:
Build an open and collaborative AI science and technology innovation system: establish a new generation of AI basic theory system; Establish a new generation of AI key common technology system; Overall layout of AI innovation platform; Accelerate the training and gathering of AI high-end talents.
Cultivate high-end and efficient intelligent economy: vigorously develop AI emerging industries; Accelerate the upgrading of industrial intelligence; Vigorously develop intelligent enterprises; Create AI innovation highland.
Build a safe and convenient intelligent society: develop convenient and efficient intelligent services; Promote the intellectualization of social management; Use AI to improve public safety guarantee ability; Promote social exchanges and share mutual trust.
Strengthen civil military integration in AI field.
Build a ubiquitous, safe and efficient intelligent infrastructure system.
Forward looking layout of major science and technology projects of the new generation of AI.
Identify 9 AI technology areas, including 1 AI full technology area and 8 AI technology areas:
Basic fields of AI technology:
Deep learning, neuroinformatics, neuroinformation processing system and other basic research;
Research and application in computer vision, biometrics, complex environment recognition, human-computer interaction, natural language processing, automatic translation, intelligent control and network security.
Eight AI technology areas:
Public service platform computing
Smart home
Driverless
Intelligent transportation application
Intelligent security
AI terminal application
wearable devices
robot
In addition, four national drivers for the development of AI were identified, including hardware, research, algorithms and AI business ecosystem:
1. Hardware: it advocates catching up with and surpassing advanced countries in chip and supercomputer manufacturing. Promote competition with Chinese methods and combinations, encourage transactions with foreign companies, and encourage technology giants and start-ups to manufacture supercomputers and invest in the production of AI chips.
2. Data: emphasize that promoting data sharing between governments and enterprises can bring advantages when obtaining data, and promote the flow of protected data at different levels by standardizing AI related industries and strengthening people’s discussion against the abuse of data at the commercial level when people pay more and more attention to the hidden dangers of privacy brought by AI.
3. Develop algorithms: on the one hand, attract and cultivate talents (especially the world’s top AI talents) by supporting basic research; on the other hand, encourage technology companies such as Baidu, Huawei, Alibaba, Tencent or iFLYTEK to establish AI research institutes overseas to recruit AI talents, so as to overcome the problems of low paper results and AI education quality.
4. Establish AI business ecosystem: invest more than US $1 billion in domestic start-ups and guide local governments and state-owned enterprises to attract private investment, so as to provide funds for AI projects to obtain data from society and combine the objectives of enterprises with national development plans.
U.S.A
The US government believes that AI is a promising transformation technology with great economic and social benefits. AI can completely change American life, work, study, research and communication. In addition, AI research can promote economic prosperity, increase educational opportunities and quality of life, and improve national security. Because of these potential benefits, the U.S. government has been investing in AI research for many years.
On May 3, 2016, the U.S. government announced the establishment of a new NSTC Subcommittee on machine learning and AI to help coordinate federal work on AI. On June 15, 2016, the network and information technology R & D plan (nitrd) subcommittee was appointed to plan the national ai r & D strategy. After that, they established the nitrd AI team to determine the strategic focus of federal AI research and development, and pay special attention to areas that enterprises can’t deal with.
This national ai r & D strategic plan sets a series of objectives for federally funded AI research, including government internal research and federally funded government external research, such as research institutes and universities. This research aims to create new AI knowledge and technologies to provide many benefits to society while minimizing negative effects. To achieve this goal, the ai r & D strategic plan outlines the AI research priorities funded by the federal government:
Strategy 1: long term investment in AI research. Priority investment in the next generation of AI will bring more discovery and understanding, so that the United States will continue to lead the world in the field of AI.
Strategy 2: develop effective man-machine cooperation methods. AI systems will not replace humans, but cooperate with humans to achieve the best performance. Therefore, it is necessary to study how to establish effective interaction between human and AI system.
Strategy 3: understand and solve the ethical, legal and social problems of AI. AI technology needs to work under the premise of formal and informal standards. Therefore, research is needed to understand the ethical, legal and social impact of AI. At the same time, develop and design AI systems that meet ethical, legal and social goals.
Strategy 4: ensure the security of AI system. Before AI systems are widely used, it is necessary to ensure that they operate safely in a controllable, clear and understandable manner. This requires further research to address this challenge by building a reliable and trusted AI system.
Strategy 5: develop shared common data sets and environments for AI training and testing. The depth, quality and accuracy of data sets and training resources will affect the performance of AI. Therefore, researchers should develop high-quality data sets and environments, and be able to responsibly access high-quality data sets and testing and training resources.
Strategy 6: measure and evaluate AI technology through standards and benchmarks. The progress, level, standard, demonstration and group participation of AI are the key to guide and measure the development of AI. More research is needed to develop various evaluation techniques.
Strategy 7: better understand the needs of national ai r & D workforce. The progress of AI needs a strong AI research group. Therefore, increasing the understanding of current and future ai r & D personnel will help to ensure that there are enough AI experts to solve the strategic R & D areas outlined in this plan.
The entire federal government can complete the seven strategies of the plan and realize its vision by supporting the following recommendations:
Recommendation 1: Based on strategies 1-6, potential market opportunities should be considered when formulating ai r & D framework, and the cooperation between AI R & D and investment should be more effectively integrated: federal agencies should cooperate through nitrd to formulate an R & D framework to promote the coordination and progress of R & D work mentioned in this plan. This will enable agencies to plan, effectively coordinate and cooperate in support of the strategic plan. The implementation framework should consider the R & D priorities of each institution according to its mandate, capability, authority and budget. According to this implementation framework, it may be necessary to establish some funding programs to speed up the ai r & D agenda. To help implement this strategic plan, nitrd should consider forming an inter agency working group dedicated to promoting cooperation between different agencies.
Recommendation 2: according to strategy 7 of this plan, study the possibility of creating and maintaining a healthy AI research and development team at the national level: a healthy and dynamic ai r & D team is essential to meet the strategic challenges of research and development outlined in this report. Although some reports point out that there may be a shortage of AI research experts, there is no official labor data report to describe the current situation of AI labor force, the expected labor force input plan, and the comparison of supply and demand forces of AI labor force. Nitrd should study how to accurately describe and define the current and future ai r & D labor demand as much as possible, and formulate additional research or suggestions to ensure that there is enough R & D labor to meet the country’s AI demand. According to the research results, it is suggested that federal agencies should ensure that a healthy national AI research and development team can be established and maintained.
Canada
Canada is one of the first countries to release AI strategy. In the 2017 federal budget, a five-year plan called “Pan Canadian AI strategy” was released, including $125 million for investment research and AI. The strategy has four objectives:
1. Increase the number of scientists and graduates;
2. Identify three outstanding groups of scientists;
3. Develop ideological leadership in the economic, ethical, political and legal impact of AI.
4. Support national AI research groups.
The Canadian Institute of advanced research led the strategy and worked closely with the Canadian government and three new AI Institutes: the Alberta Institute of machine intelligence (amii) in Edmonton, the vector Institute in Toronto and the algorithm learning institute in Montreal.
France
Europe lags behind the United States and China in formulating AI development strategies. When Germany focused on the fourth industrial revolution and Britain focused on brexit, French President macron announced that the government had recognized the national “Ai leadership” strategy and would invest 1.5 billion euros in five years (2018-2022) as the representative of the European national AI strategy. The French president’s statement on AI development strategy summarizes the main points of the French and European AI strategy report prepared by C é dric Villani (French mathematician, 2010 fields award winner and member of the French parliament) and his partners. The seven key elements of the report include:
First, formulate appropriate data policies, encourage enterprises to create and share data, develop data of social interest, and support data backup rights.
Second, the four strategic key areas of AI development are health, transportation, environment, national defense and security. For key issues, each strategic area formulates policies separately to lay the foundation for the platform in specific regions and check the Innovation Zone in each region.
Third, give play to the potential advantages of French AI research and development, establish interdisciplinary AI organizations in selected universities and research institutions, and allocate appropriate research resources (including supercomputers specially designed for AI applications in cooperation with manufacturers); Raise the salary of researchers and strengthen the exchange between industry, University and research.
Fourth, it is planned to deal with the impact of AI technology on workers, set up public laboratories to deal with job changes, carry out research on the complementarity between machines and humans, and evaluate new methods of vocational training.
Sixth, ensure the transparency of AI development technology, establish clarity and algorithm audit system, and pay attention to AI agents are responsible for moral threats. A private ethics committee related to digital and AI technology is established to organize public debates on AI ethics and adhere to the principle of human obligations (mainly when using AI tools in public services).
Australia
Australia has no clear AI strategy. However, in Australia’s budget for 2018-2019, the government announced an investment of $29.9 million over four years to support the development of AI. In addition, the government has developed a technology roadmap, a standard framework and a national AI ethics framework to responsibly support the development of this issue. NGOs also support joint research center projects, doctoral scholarships and other initiatives to improve the supply of AI talents in Australia.
In addition, in the 2017 innovation roadmap, Australia 2030: prosperity through innovation, “the government announced that it would give priority to AI in the government’s digital economy strategy. The strategy was released in the second quarter of 2018.”
Germany
Germany’s AI plan requires the investment amount to reach 3 billion euros by 2025, and hopes to double the investment amount through cooperation with private enterprises, so as to achieve the goal of “making Germany and Europe become AI centers”. Germany’s national AI plan focuses on developing AI centers nationwide, investing in education and attracting the next generation of AI talents to prepare for data digitization.
Germany has set a number of output goals, such as forming a national network composed of 12 new AI research centers, 100 new AI teacher positions, and the government’s funding plan: providing AI related support services to 1000 small and medium-sized enterprises every year. In addition, in order to make extensive and sustainable changes in education, the German AI association has put forward some policy suggestions, including the introduction of compulsory education in Data Science in the third year. In addition, the government will decide on internships and academic courses at universities. Therefore, when the government released the national AI plan in 2018, German states scrambled to set up new organizations to apply for federal funds.
The government also called for the attraction of German researchers working abroad. Germany plans to recruit 30 new international lecturers (6 new lecturers per year) through the Alexander von Humboldt scholarship program from 2018 to 2024. The project provides a start-up investment fund of € 3.5 million or € 5 million, depending on the content of the study.
Subsequently, the government also called for promoting the process of enterprise data digitization. Through the new consulting program outlined in the AI strategy, they implemented an integrated “Digital Center” to support the digitization of 1000 small and medium-sized enterprises every year.
The United Arab Emirates
The UAE government launched the AI strategy in October 2017. It is the first country in the Middle East to formulate the AI strategy and establish the AI department. The strategy is the first initiative of the United Arab Emirates’ 2071 Centennial plan. The main objective of the master plan is to use AI to improve government efficiency.
The government will invest in AI technology in nine areas: transportation, health, space, renewable energy, water, science and technology, education and environment. The government aims to reduce the cost of the whole government, achieve economic diversification, and position the UAE as a global leader in AI applications.
Japan
In terms of GDP, Japan has always been the fourth largest economy in the world. However, Japan’s AI development market increased from about 3.7 trillion (2015) to about 87 trillion (2030). Lead the world’s AI development strategy in the following aspects:
First, the government established the Japan AI technology development strategy committee, which vertically manages five national R & D institutions and three core development centers (Information Technology Research Institute, National Center, physical and Chemical Research Institute and National Institute of Industrial Science and Technology).
Secondly, the AI industrialization process focuses on three priority areas, including productivity, health care and services. Among them, medical health is divided into three stages: the first stage (2020): promote the application of AI direct data to promote the application in related fields; The second stage (2020-2025): expand the public application of AI and its data to a broader field; Phase III (2025-2030): establish AI ecosystem based on multi domain connection and mixing.
Third, the three core R & D centers focus on social AI technology based on diversified data. Among them, diversified data include: personal, voice dialogue, internal medicine, action and search history, living and working space, sales and manufacturing, transportation, nature, weather and maps (land, urban area); AI technology includes image recognition, natural language processing, speech recognition / synthesis and prediction. Over the past 10 years, the government has tripled the investment of university affiliated companies and R & D institutions in AI R & D, and promoted more excellent private R & D investment.
Finally, create a development environment for young researchers, especially in the first stage, attract high-level AI development talents at home and abroad, and encourage AI researchers to actively participate in the development of AI technology.
the republic of korea
In May 2018, Korea’s Fourth Industrial Revolution Committee announced the national AI development strategy, invested 2.2 trillion won, attracted 5000 experts, and became one of the four major powers in global AI development. The strategy continues until 2030 and consists of four phases:
Phase 1 (2020)
Core technology: the development of audio-visual understanding technology. Extended Technology: AI question answering system in professional field. Shorten the time for the health department to look for new drugs from five years to one year. Background technology: complex information analysis involves the use of high-power illustrative operations. Attract and train 590 senior AI talents and 2250 ordinary AI employees. Build 66.7 million shared data, 4.3 million industrial data and 9.2 billion Korean understandings. Supercomputing support for 300 organizations per year.
Phase 2 (to 2022)
Basic technology: Master unsupervised learning theory, image synthesis technology, tracking detection and prediction technology, and illustrative functional inference learning (until 2025). Scalable technology: real-time risk detection system. Shorten the development cycle of new drugs in the medical industry by more than half (from 15 to 7 years). Key technologies: cognitive information exchange between brain neural network and AI neural network; Overall brain and machine safety (developing until 2025). Attract and train 1370 senior AI talents and 3600 ordinary AI employees, and build 111 million shared data, 48.5 million industrial data and 15.3 billion Korean understanding. Supercomputing support for 400 organizations per year.
Phase 3 (until 2025)
Core technology: continue to learn functional illustrative inference. Commercialization of artificial neural network chip. Extended Technology: picture question and answer system. Develop new drugs for each individual. Key technologies: cognitive information exchange between brain neural network and AI neural network; Integrated interface for brain and machine security. Cultivate talents with world-class AI Leadership (until 2030). Strengthen infrastructure research in the form of improved cooperation (by 2030).
Phase 4 (to 2030)
Core technology: AI and human use unsupervised learning technology for autonomous cooperation. Extended Technology: provide food and drug preparations suitable for each specific audience. Background technology: strengthen and improve human cognitive ability through the application of AI. Cultivate talents with world-class AI leadership. Strengthen research infrastructure through enhanced cooperation.
The choice of investment is to focus on new technologies; It is difficult for the public sector to attract private investment and to establish original markets in areas of private competitiveness. Therefore, the motto of practice is to ensure technical ability and develop AI according to international standards when the technology of basic science (new generation AI based on cognitive science and Neural Network Computing) is low. AI chip layer, high performance AI computing, application fields according to AIX formula (new drugs, future materials, industrial applications).
Establish AI Development Postgraduate and postdoctoral training institutions, and strengthen AI training and research support in Colleges and universities and research institutions. Build public and private AI brain laboratories, AI centers and AI infrastructure platforms. Saltlux, the first AI company in South Korea, received an AI product investment of 32 billion won.
Singapore
Singapore’s national AI plans to invest US $150 million within five years to promote Singapore’s digital economy in combination with national AI capacity. The plan has three objectives:
1. Use AI to solve key social and industrial problems in transportation, medical care and other fields.
2. Invest to improve AI capability (explain AI system of next generation AI, cognitive science, AI talent training, etc.)
3. Provide 100 projects to promote the application of AI and machine learning in the industry.
Advantages and disadvantages of national AI strategies around the world
Generally speaking, AI strategies published at the national level mainly focus on AI training and talent attraction. However, no country has more detailed guidance on vocational guidance of AI training.
Most of the suggestions come from seminars of educational institutions and private companies, so there is no consensus on this issue. At present, some advantages and disadvantages of the national AI strategy 2020-2030 in AI vocational guidance are as follows.
Advantages:
1. Focus on key economic entities and sectors to promote the implementation of national AI strategies.
2. Assist in determining the scale of the impact of AI technology on each country’s economy.
3. Help identify the challenges and benefits of AI, how to maximize competitive advantage, and provide solutions to overcome the obstacles caused by AI to the economy.
4. Clearly define the development road map to achieve the established strategic objectives.
inferiority:
1. All predictions about the benefits of AI are still somewhat vague, subjective and without clear scientific basis.
2. The AI strategy at the national level is very general and challenging to implement.
3. The shortage of human resources in AI remains a challenge for education planners in each country.
Strategic priorities for AI development
The uniqueness of policy development in this field is that governments around the world have adopted a wide range of methods to promote the use and development of AI. They not only promote different policies, but also focus on different areas of public policy.
This framework roughly classifies AI strategies in the field of public policy, and evaluates the relationship between AI strategic priorities and their research funds and attention through the heat map (see explanation below).
The development of a global list of AI strategies has been hampered by two challenges. First, different AI strategies vary greatly; They may come from a website, an official white paper, a work report or a budget announcement. Therefore, it is possible that a strategy may be ignored due to the rapid and diversified development in this field.
Secondly, some governments announced new measures after releasing the original methods. In order to make a more systematic analysis of each plan, this analysis only focuses on the content at the time of initial announcement.
Finally, the policy announcements for each strategy are classified into eight public policy areas:
1. Scientific research: establish new research centers or projects for basic and applied AI research, or promise to increase existing AI public research funds.
2. AI talent training: provide funds to attract, retain and train AI talents at home and abroad, including funding some leading talents or establishing master’s and doctoral courses specialized in AI.
3. Technology and job prospects: help students and the whole labor market develop corresponding job skills, such as investing in stem (Science, technology, engineering and Mathematics) education, digital skills, or lifelong learning.
4. Industrialization of AI technology: encourage the private sector to adopt AI technology, including investment in strategic sectors, funding for AI start-ups and small and medium-sized enterprises, and strategies for creating AI clusters or ecosystems.
5. Ethical standards of AI: establish a Council, committee or working group to formulate standards or regulations for the use ethics and related development of AI. This area also includes specific funding for research or pilot projects to develop interpretable and transparent AI.
6. Data and digital infrastructure: fund open data partnerships, platforms and datasets, and commit to creating test environments and regulatory sandboxes.
7. AI in Government Governance: establish pilot projects to use AI to improve government efficiency, service provision and public management.
8. Inclusiveness and social well-being: ensure that AI promotes social inclusiveness, as well as the inclusiveness of AI’s own industry background and perspective.
From: Data pragmatists read more: Accenture: the survey shows that enterprises are reluctant to spend money on artificial intelligence training for employees. Top 1002019, the most influential organization in the global AI field in 2018. Global artificial intelligence talent distribution map: the United States accounts for 44% of China’s net inflow of talents. List of 2000 most influential scholars in artificial intelligence in 2020 FICO: responsible artificial intelligence report in 2021 Gartner : it is estimated that the scale of artificial intelligence software market will reach US $62 billion in 2022. Talent demand data of artificial intelligence industry in 2017. In 2016, Google ranked far ahead in global AI system IQ. Tsinghua University: 2018 white paper on artificial intelligence chip technology (with download) Lingdong Zhixing announced that it was recognized by Chi Rui and Li Yucheng, members of the board of directors of China AI Capital Limited: depicting the development blueprint of China’s artificial intelligence (with download). Indeed released the AI talent employment report: the annual salary of machine learning engineers is 980000, but the AI employment growth is declining! Ten thousand people gather to talk about AI. Waie will show you AI in the next five years! Understanding the past and present life of deep learning with one picture — the rise of information map machine: artificial intelligence in the eyes of Chinese executives (with download)
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