The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), led by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry. The annual report tracks, collates, distills, and visualizes data relating to artificial intelligence, enabling decision-makers to take meaningful action to advance AI responsibly and ethically with humans in mind.
The AI Index collaborates with many different organizations to track progress in artificial intelligence. These organizations include: the Center for Security and Emerging Technology at Georgetown University, LinkedIn, NetBase Quid, Lightcast, and McKinsey. The 2023 report also features more self-collected data and original analysis than ever before. This year’s report included new analysis on foundation models, including their geopolitics and training costs, the environmental impact of AI systems, K-12 AI education, and public opinion trends in AI. The AI Index also broadened its tracking of global AI legislation from 25 countries in 2022 to 127 in 2023.
Until 2014, most significant machine learning models were released by academia. Since then, industry has taken over. In 2022, there were 32 significant industry-produced machine learning models compared to just three produced by academia. Building state-of-the-art AI systems increasingly requires large amounts of data, compute, and money, resources that industry actors inherently possess in greater amounts compared to nonprofits and academia.
AI continued to post state-of-the-art results, but year-over-year improvement on many benchmarks continues to be marginal. Moreover, the speed at which benchmark saturation is being reached is increasing. However, new, more comprehensive benchmarking suites such as BIG-bench and HELM are being released.
New research suggests that AI systems can have serious environmental impacts. According to Luccioni et al., 2022, BLOOM’s training run emitted 25 times more carbon than a single air traveler on a one-way trip from New York to San Francisco. Still, new reinforcement learning models like BCOOLER show that AI systems can be used to optimize energy usage.
AI models are starting to rapidly accelerate scientific progress and in 2022 were used to aid hydrogen fusion, improve the efficiency of matrix manipulation, and generate new antibodies.
According to the AIAAIC database, which tracks incidents related to the ethical misuse of AI, the number of AI incidents and controversies has increased 26 times since 2012. Some notable incidents in 2022 included a deepfake video of Ukrainian President Volodymyr Zelenskyy surrendering and U.S. prisons using call-monitoring technology on their inmates. This growth is evidence of both greater use of AI technologies and awareness of misuse possibilities.
Across every sector in the United States for which there is data (with the exception of agriculture, forestry, fishery and hunting), the number of AI-related job postings has increased on average from 1.7% in 2021 to 1.9% in 2022. Employers in the United States are increasingly looking for workers with AI-related skills.
Global AI private investment was $91.9 billion in 2022, which represented a 26.7% decrease since 2021. The total number of AI-related funding events as well as the number of newly funded AI companies likewise decreased. Still, during the last decade as a whole, AI investment has significantly increased. In 2022 the amount of private investment in AI was 18 times greater than it was in 2013.
The proportion of companies adopting AI in 2022 has more than doubled since 2017, though it has plateaued in recent years between 50% and 60%, according to the results of McKinsey’s annual research survey. Organizations that have adopted AI report realizing meaningful cost decreases and revenue increases.
An AI Index analysis of the legislative records of 127 countries shows that the number of bills containing “artificial intelligence” that were passed into law grew from just 1 in 2016 to 37 in 2022. An analysis of the parliamentary records on AI in 81 countries likewise shows that mentions of AI in global legislative proceedings have increased nearly 6.5 times since 2016.
In a 2022 IPSOS survey, 78% of Chinese respondents (the highest proportion of surveyed countries) agreed with the statement that products and services using AI have more benefits than drawbacks. After Chinese respondents, those from Saudi Arabia (76%) and India (71%) felt the most positive about AI products. Only 35% of sampled Americans (among the lowest of surveyed countries) agreed that products and services using AI had more benefits than drawbacks.
This chapter captures trends in AI R&D. It begins by examining AI publications, including journal articles, conference papers, and repositories. Next it considers data on significant machine learning systems, including large language and multimodal models. Finally, the chapter concludes by looking at AI conference attendance and open-source AI research. Although the United States and China continue to dominate AI R&D, research efforts are becoming increasingly geographically dispersed.
This year’s technical performance chapter features analysis of the technical progress in AI during 2022. Building on previous reports, this chapter chronicles advancement in computer vision, language, speech, reinforcement learning, and hardware. Moreover, this year this chapter features an analysis on the environmental impact of AI, a discussion of the ways in which AI has furthered scientific progress, and a timeline-style overview of some of the most significant recent AI developments.
Fairness, bias, and ethics in machine learning continue to be topics of interest among both researchers and practitioners. As the technical barrier to entry for creating and deploying generative AI systems has lowered dramatically, the ethical issues around AI have become more apparent to the general public. Startups and large companies find themselves in a race to deploy and release generative models, and the technology is no longer controlled by a small group of actors.
In addition to building on analysis in last year’s report, this year the AI Index highlights tensions between raw model performance and ethical issues, as well as new metrics quantifying bias in multimodal models.
Increases in the technical capabilities of AI systems have led to greater rates of AI deployment in businesses, governments, and other organizations. The heightening integration of AI and the economy comes with both excitement and concern. Will AI increase productivity or be a dud? Will it boost wages or lead to the widespread replacement of workers? To what degree are businesses embracing new AI technologies and willing to hire AI-skilled workers? How has investment in AI changed over time, and what particular industries, regions, and fields of AI have attracted the greatest amount of investor interest?
This chapter examines AI-related economic trends by using data from Lightcast, LinkedIn, McKinsey, Deloitte, and NetBase Quid, as well as the International Federation of Robotics (IFR). This chapter begins by looking at data on AI-related occupations and then moves on to analyses of AI investment, corporate adoption of AI, and robot installations.
Studying the state of AI education is important for gauging some of the ways in which the AI workforce might evolve over time. AI-related education has typically occurred at the postsecondary level; however, as AI technologies have become increasingly ubiquitous, this education is being embraced at the K–12 level. This chapter examines trends in AI education at the postsecondary and K–12 levels, in both the United States and the rest of the world.
We analyze data from the Computing Research Association’s annual Taulbee Survey on the state of computer science and AI postsecondary education in North America, Code.org’s repository of data on K–12 computer science in the United States, and a recent UNESCO report on the international development of K–12 education curricula.
The growing popularity of AI has prompted intergovernmental, national, and regional organizations to craft strategies around AI governance. These actors are motivated by the realization that the societal and ethical concerns surrounding AI must be addressed to maximize its benefits. The governance of AI technologies has become essential for governments across the world.
This chapter examines AI governance on a global scale. It begins by highlighting the countries leading the way in setting AI policies. Next, it considers how AI has been discussed in legislative records internationally and in the United States. The chapter concludes with an examination of trends in various national AI strategies, followed by a close review of U.S. public sector investment in AI.
AI systems are increasingly deployed in the real world. However, there often exists a disparity between the individuals who develop AI and those who use AI. North American AI researchers and practitioners in both industry and academia are predominantly white and male. This lack of diversity can lead to harms, among them the reinforcement of existing societal inequalities and bias.
This chapter highlights data on diversity trends in AI, sourced primarily from academia. It borrows information from organizations such as Women in Machine Learning (WiML), whose mission is to improve the state of diversity in AI, as well as the Computing Research Association (CRA), which tracks the state of diversity in North American academic computer science. Finally, the chapter also makes use of Code.org data on diversity trends in secondary computer science education in the United States.
Note that the data in this subsection is neither comprehensive nor conclusive. Publicly available demographic data on trends in AI diversity is sparse. As a result, this chapter does not cover other areas of diversity, such as sexual orientation. The AI Index hopes that as AI becomes more ubiquitous, the amount of data on diversity in the field will increase such that the topic can be covered more thoroughly in future reports.
AI has the potential to have a transformative impact on society. As such it has become increasingly important to monitor public attitudes toward AI. Better understanding trends in public opinion is essential in informing decisions pertaining to AI’s development, regulation, and use.
This chapter examines public opinion through global, national, demographic, and ethnic lenses. Moreover, we explore the opinions of AI researchers, and conclude with a look at the social media discussion that surrounded AI in 2022. We draw on data from two global surveys, one organized by IPSOS, and another by Lloyd’s Register Foundation and Gallup, along with a U.S-specific survey conducted by PEW Research.
It is worth noting that there is a paucity of longitudinal survey data related to AI asking the same questions of the same groups of people over extended periods of time. As AI becomes more and more ubiquitous, broader efforts at understanding AI public opinion will become increasingly important.