Offers a thorough analysis of AI policies across 54 countries. The report uses computer science techniques to examine the empirical factors of dominant global AI strategies.
Key Insights and Findings:
- Comprehensive Analysis: Employs LDA and e-LDA techniques to analyze 213 AI strategy documents, identifying topics for a nuanced understanding of each country’s policy depth and priorities.
- Policy Depth and Priorities: Shows variations in policy depth across countries. The U.S. and China excel in basic research capabilities, while the EU stands out in data governance and ethics, indicating different national AI development priorities.
- Interdisciplinary Approach: Emphasizes the significance of interdisciplinary cooperation in AI, calling for policymakers, business leaders, and students to drive AI’s technical, commercial, civil, and ethical aspects for a better future.
- Global AI Infrastructure Market: The AI infrastructure market, valued at USD 37.03 billion in 2023, is expected to reach USD 421.44 billion by 2033, growing at a 27.53% CAGR from 2024-2033, driven by rising demand for AI infrastructure across various sectors to enhance productivity and competitiveness.
- Regional Insights: North America leads the global AI infrastructure market, with the U.S. worth USD 11.39 billion in 2023. The Asia Pacific will grow fastest due to rapid AI adoption across industries and government support.
- Machine Learning and Deep Learning: Machine learning dominates the AI infrastructure market, with deep learning gaining traction due to its wide use in applications like voice and image recognition and natural language processing.
Overview
Summary & Recommendations
The 2023 Global AI Infrastructures Report summarizes key findings from 54 national AI plans. It highlights the importance of the US National AI Research and Development Strategic Plan. Over 60 countries have since unveiled their AI policies. It uses Latent Dirichlet Analysis (LDA) to analyze 213 AI strategy documents. It identifies latent topics within each document and determines the probability of words co-occurring. The report uses ensemble-LDA for stable results across different models. Also, it contains a detailed examination of national AI infrastructures, comparing strategies of 54 countries and providing insights into their AI infrastructure plans.
Introduction
This section details the research method, which involves analyzing national AI policies using natural language processing techniques, specifically Latent Dirichlet Allocation (LDA). This analysis identifies key topics and themes, offering insights into factors influencing national AI infrastructures. It underscores the importance of understanding narratives on technological diffusion, security, and the societal impact of AI. It assesses national AI strategies to comprehend the ideas and narratives driving AI policy development and implementation. This contributes to an extensive understanding of global value diffusion in AI infrastructures.
National Landscapes:
Discusses the AI infrastructure plans initially published by countries like the US, Canada, and China, and the subsequent AI policies of over 60 countries since the US’s 2016 strategic plan. The analysis contrasts the strategies of 54 nations to identify commonalities and differences in their AI infrastructures.
Stages in the Development of AI Strategies:
Highlights the diversity in national AI strategies. Some countries focus on key AI infrastructures, while others detail steps for enhancing AI technologies and regulations. It provides evidence of how these steps can impact AI infrastructures in other countries.
Existing Narratives in AI Infrastructures:
Investigates the main themes in AI development as seen in national plans. These narratives are disseminated via media, political discourse, and policy documents. The study seeks to reveal the variety of narratives in global AI policies, highlighting the motivations and values that influence AI strategies.
AI Wardrobes:
AI wardrobes refer to policy mechanisms used by nations to build their AI infrastructures. The report shows how countries choose and mix these mechanisms based on needs and priorities. By analyzing countries’ AI policies, it finds distinct clusters. This understanding provides insights into nations’ various strategies for building their AI infrastructures.
Methods and Data-Set
The Methods and Data-Set section of the 2023 Global AI Infrastructures Report details the research methodology, and data sources used to analyze the AI policies of 54 countries. The report examines the AI research landscape, highlighting traditional methods such as close text readings and keyword frequency analyses and noting the limited use of NLP methods in previous studies.
Introduces Latent Dirichlet Analysis (LDA), a computer science technique, to analyze 213 documents on AI strategies. LDA is used to identify hidden topics within each document by determining the likelihood of words co-occurring. This methodology aims to identify the main themes and narratives in national AI policies from various countries. The section refers to Susan Aaronson’s report on government AI programs and uses the OECD AI policy website to evaluate global AI capabilities. Unlike the CIGI research, this study seeks to understand the granular motivations for developing and implementing AI policies.
The Empirical Findings
Presents an analysis of AI policies from 54 countries. Comparing policy priorities provides insights into their approaches to AI infrastructure development. It uses a three-tier analysis method, encompassing national, intra-national, and document levels, for a comprehensive view of AI strategies. It first examines top-level policy documents per country to identify their goals and priorities. Then, it explores multiple policies per country to understand the breadth and depth of AI infrastructure policies.
The document-level analysis identifies the top five topics within each nation’s AI strategy, providing insight into global focus areas. It also compares key documents from different countries addressing these topics, highlighting the commonalities and variances in national AI policies.
Comparing National AI Infrastructures: Policy Priorities Revealed
Compares the AI policy priorities of 54 countries. Examining each nation’s policy documents identifies key themes and approaches to AI development. This analysis offers insights into diverse strategies and goals, providing a global view of AI policies.
Comparing Intra-national AI Strategies: Policy Depth
Analyses intra-national AI strategies to assess policy depth and coverage. It explores various policy documents to gauge how nations address regulatory frameworks, technology development, and ethical considerations in their AI strategies, providing a nuanced understanding of national AI policies.
Document Level Analysis: Analyzing the 5 Most Important Topics in Our Intra-national Documents
Evaluates the five crucial topics from the intra-national AI documents. It highlights each country’s AI strategy focus areas and uncovers common themes and priorities, offering insights into AI policy development’s main areas.
Conclusion
Summarizes findings from 213 AI policy analyses, highlighting the research’s significance. It examines various countries’ AI strategies, revealing their priorities, narratives, and policy depth. The report highlights the need for comparative analyses in creating efficient national AI infrastructures specific to each country’s capabilities and priorities.
It acknowledges the variety of approaches and asserts there’s no universal solution for AI policy development. It also mentions the regulatory hurdles linked to AI implementation, stressing the role of political readiness in forming AI governance frameworks. The Conclusion notes AI policies’ positive impact on healthcare, education, research, and transportation sectors. It underscores AI’s potential to improve services and spur innovation. It ends with three policy recommendations, stressing tailored AI governance, accountability, and exploring AI’s benefits in service delivery.
