2022-03-17-斯坦福大学-2022年人工智能指数报告（EN）_230页_30mb

AI Index Report 2022 Summary

Core Content

The AI Index Report 2022, published by Stanford University's Human-Centered AI Institute, provides an extensive overview of the state of artificial intelligence (AI) research, development, ethics, economic impact, and policy across the globe. The report includes data from a wide range of academic, private, and nonprofit organizations, as well as original analysis, offering a comprehensive and nuanced understanding of AI's growth and challenges.

Main Points

1. Global AI Development and Collaboration

• The U.S. and China have been the dominant forces in AI research, with a significant increase in cross-country collaborations since 2010.
• From 2010 to 2021, the number of cross-country collaborations between the U.S. and China increased fivefold, and this collaboration produced 2.7 times more AI publications than that between the U.K. and China.
• China led the world in AI publications (journals, conferences, and repositories) in 2021, with a 63.2% higher volume than the U.S. combined.
• The U.S. remains the leader in AI conference and repository citations.
• Educational and nonprofit institutions have been the most productive in AI publications, followed by private companies and government bodies.

2. AI Research and Development Trends

• The total number of AI publications globally increased from 162,444 in 2010 to 334,497 in 2021, doubling over the period.
• Journal articles made up the largest share (51.5%), followed by conference papers (21.5%) and repositories (17.0%) in 2021.
• The number of AI conference papers has declined since 2018, while journal and repository publications have grown significantly.
• Publications in pattern recognition and machine learning have more than doubled since 2015, and other areas like computer vision and natural language processing have also seen growth, though less dramatic.

3. AI Ethics and Bias

• Large language models are becoming more capable but also more biased, with a 280 billion parameter model showing a 29% increase in elicited toxicity compared to a 117 million parameter model from 2018.
• Research on fairness and transparency in AI has grown fivefold since 2014, with algorithmic fairness and bias now a mainstream research topic.
• Industry-affiliated researchers have contributed 71% more publications at ethics-focused conferences compared to previous years.

4. Economic and Educational Impact

• Private investment in AI surged in 2021, reaching $93.5 billion—more than double the 2020 total. However, the number of newly funded AI companies dropped from 1051 in 2019 to 746 in 2021.
• "Data management, processing, and cloud" received the most private AI investment in 2021, followed by "medical and healthcare" and "fintech."
• The U.S. led in both total private investment and the number of newly funded AI companies, with China following closely.
• AI job postings in the U.S. were highest in California, Texas, New York, and Virginia, with California having over 2.35 times more postings than Texas.
• AI hiring growth was most pronounced in New Zealand, Hong Kong, Ireland, Luxembourg, and Sweden.
• In 2020, 20% of CS PhD graduates specialized in AI, the most popular specialty in the past decade. Most U.S. AI PhDs joined the private sector, with only a small fraction entering government.

5. AI Policy and Governance

• AI-related legislation in 25 countries increased from 1 bill in 2016 to 18 in 2021.
• Spain, the U.K., and the U.S. passed the most AI-related bills in 2021, each adopting three.
• The U.S. saw a sharp rise in AI-related bills proposed from 2015 to 2021, but only 2% were passed into law.
• State legislators in the U.S. passed 1 out of every 50 AI-related bills proposed in 2021, while the number of such bills increased from 2 in 2012 to 131 in 2021.
• The 117th U.S. Congress had the highest number of AI-related mentions since 2001, with 295 mentions by mid-2021.

6. Technical Performance and Affordability

• AI systems have become more affordable and higher performing. The cost to train an image classification system fell by 63.6% since 2018, and training times improved by 94.4%.
• This trend is consistent across other MLPerf categories like recommendation, object detection, and language processing.
• Robotic arms have become significantly cheaper, with the median price dropping fourfold from $50,000 in 2016 to $12,845 in 2021.
• AI research is increasingly focused on real-world applications, as seen in the rising interest in subtasks like medical image segmentation and masked-face identification.

7. Open-Source and Accessibility

• Open-source AI software and libraries have become central to AI development, with widespread use in research and industry.
• The Global AI Vibrancy Tool was updated to compare up to 29 countries across 23 indicators, enhancing the accessibility and understanding of AI development trends.

Key Information

• The report is co-directed by Jack Clark and Raymond Perrault and includes contributions from numerous researchers and organizations.
• It is licensed under the Attribution-NoDerivatives 4.0 International License.
• The report draws on data from CSET, Bloomberg Government, LinkedIn, and other sources, offering a global perspective on AI trends.
• The AI Index is part of the Stanford Institute for Human-Centered AI and was conceived within the One Hundred Year Study on AI (AI100).

Conclusion

The AI Index Report 2022 highlights the rapid advancement and global expansion of AI research and development, while also emphasizing the growing ethical concerns and the need for more inclusive and fair AI systems. The report underscores the increasing affordability and performance of AI technologies, which are driving their adoption in various sectors. It also calls attention to the need for more robust ethical frameworks and policy measures to address the challenges associated with AI's global deployment.