1、2019 annual report ar intelligence index Raymond Perrault (report coordinator) SRI International Yoav Shoham (chair) Stanford University Erik Brynjolfsson MIT Jack Clark OpenAI John Etchemendy Stanford University Barbara Grosz Harvard University Terah Lyons Partnership On AI James Manyika McKinsey G
2、lobal Institute Juan Carlos Niebles Stanford University Project Manager and Report Editor-in-Chief Saurabh Mishra Stanford University Steering Committee Artificial Intelligence Index Report 2019 Steering Commitee Introduction Report Highlights Acknowledgements Chapter 1 Chapter 2 Chapter 3 Chapter 4
3、 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Artificial Intelligence Index Report 2019 Table of Contents Research and Development Conferences Technical Performance The Economy Education Autonomous Systems Public Perception Societal Considerations National Strategies and Global AI Vibrancy Tabl
4、e of Contents Technical Appendix 182 4 5 9 12 37 47 71 106 127 136 146 156 How to cite this Report: Raymond Perrault, Yoav Shoham, Erik Brynjolfsson, Jack Clark, John Etchemendy, Barbara Grosz, Terah Lyons, James Manyika, Saurabh Mishra, and Juan Carlos Niebles, “The AI Index 2019 Annual Report”, AI
5、 Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, December 2019. (c) 2019 by Stanford University, “The AI Index 2019 Annual Report” is made available under a Creative Commons Attribution- NoDerivatives 4.0 License (International) https:/creativecommons.org/li
6、censes/by-nd/4.0/legalcode The AI Index is as an independent initiative at Stanford Universitys Human-Centered Artificial Intelligence Institute (HAI). The AI Index was conceived within the One Hundred Year Study on AI (AI100). We thank your supporting partners We welcome feedback and new ideas for
7、next year. Contact us at AI-Index-Reportstanford.edu. Artificial Intelligence Index Report 2019 AI Index Report Table_of_Contents3 Artificial Intelligence Index Report 2019 AI Index Report - Introduction The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intel
8、ligence. Its mission is to provide unbiased, rigorously-vetted data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Expanding annually, the Report endeavors to include data on AI development from communities around t
9、he globe. Before diving into the data, it is worth noting the following about the 2019 edition of the AI Index Report: 1. This edition tracks three times as many data sets as the 2018 edition. It includes an update of previous measures, as well as numerous new ones, across all aspects of AI: technic
10、al performance, the economy, societal issues, and more. 2. This volume of data is challenging to navigate. To help, weve produced a tool that provides a high-level global perspective on the data. The Global AI Vibrancy Tool (vibrancy.aiindex.org) compares countries global activities, including both
11、a cross-country perspective, as well as a country-specific drill down. Though it is tempting to provide a single ranking of countries, such comparisons are notoriously tricky. Instead, weve provided a tool for the reader to set the parameters and obtain the perspective they find most relevant when c
12、omparing countries. This tool helps dispel the common impression that AI development is largely a tussle between the US and China. Reality is much more nuanced. Our data shows that local centers of AI excellence are emerging across the globe. For example, Finland excels in AI education, India demons
13、trates great AI skill penetration, Singapore has well-organized government support for AI, and Israel shows a lot of private investment in AI startups per capita. 3. We are also releasing the AI Index arXiv Monitor (arxiv.aiindex.org), a tool to support research on current technological progress in
14、AI via full-text searches of papers published on the pre-print repository. Given that measurement and evaluation in complex domains remain fraught with subtleties, the AI Index has worked hard to avoid bias and seek input from many communities. As part of this effort, on October 30, 2019, the Stanfo
15、rd HAI-AI Index Workshop: Measurement in AI Policy: Opportunities and Challenges (https:/ hai.stanford.edu/ai-index/workshops) convened over 150 industry and academic experts from a variety of disciplines related to AI to discuss the many pressing issues that arise from data measurement of AI. The W
16、orkshop Proceedings will be available shortly here. 4 Introduction to the AI Index 2019 Report Table_of_Contents Artificial Intelligence Index Report 2019 AI Index Report - Highlights Each of the nine chapters presents well-vetted data on important dimensions related to the activity in, and technica
17、l progress of artificial intelligence. Here is a sample of the findings. 1. Research and Development Between 1998 and 2018, the volume of peer-reviewed AI papers has grown by more than 300%, accounting for 3% of peer-reviewed journal publications and 9% of published conference papers. China now publ
18、ishes as many AI journal and conference papers per year as Europe, having passed the US in 2006. The Field-Weighted Citation Impact of US publications is still about 50% higher than Chinas. Singapore, Switzerland, Australia, Israel, Netherlands, and Luxembourg have relatively high numbers of Deep Le
19、arning papers published on arXiv in per capita terms. Over 32% of world AI journal citations are attributed to East Asia. Over 40% of world AI conference paper citations are attributed to North America. North America accounts for over 60% of global AI patent citation activity between 2014-18. Many W
20、estern European countries, especially the Netherlands and Denmark, as well as Argentina, Canada, and Iran show relatively high presence of women in AI research. 2. Conferences Attendance at AI conferences continues to increase significantly. In 2019, the largest, NeurIPS, expects 13,500 attendees, u
21、p 41% over 2018 and over 800% relative to 2012. Even conferences such as AAAI and CVPR are seeing annual attendance growth around 30%. The WiML workshop has eight times more participants than it had in 2014 and AI4ALL has 20 times more alumni than it had in 2015. These increases reflect a continued
22、effort to include women and underrepresented groups in the AI field. 3. Technical Performance In a year and a half, the time required to train a large image classification system on cloud infrastructure has fallen from about three hours in October 2017 to about 88 seconds in July, 2019. During the s
23、ame period, the cost to train such a system has fallen similarly. Progress on some broad sets of natural-language processing classification tasks, as captured in the SuperGLUE and SQuAD2.0 benchmarks, has been remarkably rapid; performance is still lower on some NLP tasks requiring reasoning, such a
24、s the AI2 Reasoning Challenge, or human-level concept learning task, such as the Omniglot Challenge. Prior to 2012, AI results closely tracked Moores Law, with compute doubling every two years. Post-2012, compute has been doubling every 3.4 months. 4. Economy Singapore, Brazil, Australia, Canada and
25、 India experienced the fastest growth in AI hiring from 2015 to 2019. 5 AI Index 2019 Report Highlights Table_of_Contents Artificial Intelligence Index Report 2019 AI Index Report - Highlights In the US, the share of jobs in AI-related topics increased from 0.26% of total jobs posted in 2010 to 1.32
26、% in October 2019, with the highest share in Machine Learning (0.51% of total jobs). AI labor demand is growing especially in high-tech services and the manufacturing sector. The state of Washington has the highest relative AI labor demand. Almost 1.4% of total jobs posted are AI jobs. California ha
27、s 1.3%, Massachusetts 1.3%, New York 1.2%, the District of Columbia (DC) 1.1%, and Virginia has 1% online jobs posted in AI. In the US, the share of AI jobs grew from 0.3% in 2012 to 0.8% of total jobs posted in 2019. AI labor demand is growing especially in high-tech services and the manufacturing
28、sector. In 2019, global private AI investment was over $70B, with AI-related startup investments over $37B, M AI4All board; Bloomberg Philanthropies; WiML board; Pedro Avelar (UFRGS), Dhruv Batra (Georgia Tech / FAIR); Zoe Bauer; Sam Bowman (NYU); Cody Coleman (Stanford); Casey Fiesler (University o
29、f Colorado Boulder); Brenden Lake (NYU); Calvin LeGassick; Natalie Garrett (University of Colorado Boulder); Bernard Ghanem (King Abdullah University of Science and Technology); Carol Hamilton (AAAI); Arthur Jago (University of Washington Tacoma); Zhao Jin (University of Rochester); Lars Kotthoff (U
30、niversity of Wyoming); Luis Lamb (Federal University of Rio Grande do Sul); Fanghzhen Lin (Hong Kong University of Science and Technology); Don Moore (UC Berkeley Haas School of Business), Avneesh Saluja (Netflix), Marcelo Prates (UFRGS), Michael Gofman (University of Rochester); Roger McCarthy (McC
31、arthy Engineering); Devi Parikh (Georgia Tech / FAIR); Lynne Parker (White House Office of Science and Technology Policy); Daniel Rock (MIT); Ayush Shrivastava (Georgia Tech College of Computing); Cees Snoek (University of Amsterdam); Fabro Steibel (ITS-RIO); Prasanna Tambe (Wharton); Susan Woodward
32、 (Sandhill Econometrics); Chenggang Xu (Cheung Kong Graduate School of Business). Matthew Kenney (Duke University) built the arXiv search engine tool. Tamara Pristc (Stanford) and Agata Foryciarz (Stanford) provided research contributions. Biswas Shrestha (Stanford) supported editing. Special thank
33、you to Monique Tuin (McKinsey Global Institute) for invaluable comments and feedback. Michael Chang (graphic design and cover art), Kevin Litman-Navarro (data visualization), Ruth Starkman (editor) and Biswas Shrestha (Stanford) were integral to the daily production of the report. Artificial Intelli
34、gence Index Report 2019 AI Index Report Overview SYMBOLS Pages appear with following symbols that denote global, sectoral, sub-regional, or other attributes for a given chapter. Beginning: The first section of each chapter generally corresponds to either global, national, or regional metrics. Middle
35、: The middle section of each chapter corresponds to sectoral, cross country comparisons, or deep dives specific to each chapter. End: The end section of each chapter offers sub- regional and state level analyses, results from cities, and data relevant to societal considerations of AI such as ethics
36、and applications to the UN Sustainable Development Goals (SDGs) metrics. Measurement Questions: Each chapter concludes with a short discussion on measurement questions related data and metrics presented in the chapter. 11Table_of_Contents Artificial Intelligence Index Report 2019 Chapter 1 Research
37、and Development Chapter 1: Research and Development Table_of_Contents Research_Development_Technical_Appendix Journal Publications: Elsevier Papers on arXiv Microsoft Academic Graph Journals Conferences Patents Github Stars Women in AI Research Measurement Questions 14 21 24 27 30 33 34 36 Chapter P
38、review Artificial Intelligence Index Report 2019 Chapter 1 Research and Development - Introduction 2see these studies by Anne-Wil Harzing and Martijn Visser. 13 Introduction This chapter presents bibliometrics data, including volume of journal, conference and patent publications and their citation i
39、mpacts by world regions. The chapter also presents Github Stars for key AI software libraries followed by societal considerations and gender diversity of AI researchers based on arXiv. The Report has used different datasets to comprehensively assess the state of AI R low degree of collaboration but
40、high impact (top left quadrant); low degree of collaboration and low impact (bottom left quadrant); high degree of collaboration but low impact (top left quadrant); Chart for countries across scholarly output metrics is available in the Appendix. Artificial Intelligence Index Report 2019 Chapter 1 R
41、esearch followed by Europe accounting for over 31%, and North America over 27% of world AI journal citations (Figure 1.9). Published Papers: AI Journal Citation Box 1.1 Between 2014-18, 17% of world citation was self- citation with East Asia; 15% was self-citation within Europe; 9% was self-citation
42、 within North America. Between regions, 8% of world citations were East Asian journals papers citing North American journal papers and 7% papers citing North American papers. 7% of world citations were East Asian journal papers citing European papers. The share of European and North American journal
43、 papers citing East Asian journals was 5% of world citation each. Note: Percentage of journal citations to unknown country is 19.1%. Self- citation in these sections is referred to citation from one region to the same, not the more conventional author-cites-self interpretation. Research_Development_
44、Technical_Appendix_Methodology_Paper Access_Data_Regional_Pairwise_Data_Interactive_Tool 26 Fig. 1.9. Note: Percentage of journal citations to unknown country is 19.1%. AI Journal Citation Attributed to Region (% of world journal citations), 2014-18 Source: MAG, 2019. AI journal citations to East As
45、ia journal papers account for over 32% of world citations; followed by Europe accounting for over 31%, and North America over 27% Artificial Intelligence Index Report 2019 Chapter 1 Research followed by East Asia with over 22%, and Europe with over 17% of AI patent citation (Figure 1.13). Box 1.3. O
46、ver 60% of AI patent citation activity is related to North America, with almost 45% (of world AI patent citation) self-citation, 9% from East Asia patents, and 7% from European patents North American patents cited European and East Asian patents around 6,000 times betwen 2015-18, with the individual
47、 regions accounting for 6-7% each of world patent citations note: Percentage of patent citations to unknown country is 37.2% 32Research_Development_Technical_Appendix_Methodology_Paper Access_Data_Interactive_Tool Artificial Intelligence Index Report 2019 Chapter 1 Research privacy and data rights;
48、and the role of AI in the context of governance and elections, censorship and content moderation, and trade and labor. All sessions specifically on and related to artificial intelligence are available here and 2019 here. Relevant outcomes related to AI in Toronto (2018) and Tunis (2019) RightsCon To
49、ronto (2018) the Toronto Declaration: Protecting the rights to equality and non- discrimination in machine learning systems was launched by Access Now and Amnesty International. RightsCon Toronto (2018) Integrate.ai, an artificial intelligence firm launched the first draft of their white paper on Responsible AI in Consumer Enterprise, which provided a framework for organizations to operationalize ethics, privacy and security in the application of machine learning and AI. RightsCon Tunis (2019) introdu
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