Welcome to the
2021 AI Index Report
This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with Stanford HAI.
The 2021 report also shows the effects of COVID-19 on AI development from multiple perspectives. The Technical Performance chapter discusses how an AI startup used machine-learning-based techniques to accelerate COVID-related drug discovery during the pandemic, and our Economy chapter suggests that AI hiring and private investment were not significantly adversely influenced by the pandemic, as both grew during 2020. If anything, COVID-19 may have led to a higher number of people participating in AI research conferences, as the pandemic forced conferences to shift to virtual formats, which in turn led to significant spikes in attendance.
“Drugs, Cancer, Molecular, Drug Discovery” received the greatest amount of private AI investment in 2020, with more than USD 13.8 billion, 4.5 times higher than 2019.
In 2019, 65% of graduating North American PhDs in AI went into industry—up from 44.4% in 2010, highlighting the greater role industry has begun to play in AI development.
AI systems can now compose text, audio, and images to a sufficiently high standard that humans have a hard time telling the difference between synthetic and non-synthetic outputs for some constrained applications of the technology.
In 2019, 45% new U.S. resident AI PhD graduates were white—by comparison, 2.4% were African American and 3.2% were Hispanic.
After surpassing the US in the total number of journal publications several years ago, China now also leads in journal citations; however, the US has consistently (and significantly) more AI conference papers (which are also more heavily cited) than China over the last decade.
The percentage of international students among new AI PhDs in North America continued to rise in 2019, to 64.3%—a 4.3% increase from 2018. Among foreign graduates, 81.8% stayed in the United States and 8.6% have taken jobs outside the United States.
Though a number of groups are producing a range of qualitative or normative outputs in the AI ethics domain, the field generally lacks benchmarks that can be used to measure or assess the relationship between broader societal discussions about technology development and the development of the technology itself. Furthermore, researchers and civil society view AI ethics as more important than industrial organizations.
R&D is fundamental to AI progress. Since the technology first captured the imagination of computer scientists and mathematicians in the 1950s, AI has grown into a major research discipline with significant commercial applications. The number of AI publications has increased dramatically in the past 20 years. The rise of AI conferences and preprint archives has expanded the dissemination of research and scholarly communications. Major powers, including China, the European Union, and the United States, are racing to invest in AI research. The R&D chapter aims to capture the progress in this increasingly complex and competitive field.
This chapter highlights the technical progress in various subfields of AI, including computer vision, language, speech, concept learning, and theorem proving. It uses a combination of quantitative measurements, such as common benchmarks and prize challenges, and qualitative insights from academic papers to showcase the developments in state-of-the-art AI technologies.
The rise of AI inevitably raises the question of how much the technologies will impact businesses, labor, and the economy more generally. AI offers substantial benefits and opportunities for businesses, from increasing productivity gains with automation to tailoring products to consumers using algorithms, analyzing data at scale, and more. However, the boost in efficiency and productivity promised by AI also presents great challenges: Companies must scramble to find and retain skilled talent to meet their production needs while being mindful about implementing measures to mitigate the risks of using AI. Moreover, the COVID-19 pandemic has caused chaos and continued uncertainty for the global economy. This chapter looks at the increasingly intertwined relationship between AI and the global economy from the perspective of jobs, investment, and corporate activity.
As AI has become a more significant driver of economic activity, there has been increased interest from people who want to understand it and gain the necessary qualifications to work in the field. At the same time, rising AI demands from industry are tempting more professors to leave academia for the private sector. This chapter focuses on trends in the skills and training of AI talent through various education platforms and institutions.
As artificial intelligence–powered innovations become ever more prevalent in our lives, the ethical challenges of AI applications are increasingly evident and subject to scrutiny. As previous chapters have addressed, the use of various AI technologies can lead to unintended but harmful consequences, such as privacy intrusion; discrimination based on gender, race/ethnicity, sexual orientation, or gender identity; and opaque decision-making, among other issues. Addressing existing ethical challenges and building responsible, fair AI innovations before they get deployed has never been more important. This chapter tackles the efforts to address the ethical issues that have arisen alongside the rise of AI applications.
While AI systems have the potential to dramatically affect society, the people building AI systems are not representative of the people those systems are meant to serve. The AI workforce remains predominantly male and lacking in diversity in both academia and the industry, despite many years highlighting the disadvantages and risks this engenders. The lack of diversity in race and ethnicity, gender identity, and sexual orientation not only risks creating an uneven distribution of power in the workforce, but also, equally important, reinforces existing inequalities generated by AI systems, reduces the scope of individuals and organizations for whom these systems work, and contributes to unjust outcomes. This chapter presents diversity statistics within the AI workforce and academia.
AI is set to shape global competitiveness over the coming decades, promising to grant early adopters a significant economic and strategic advantage. To date, national governments and regional and intergovernmental organizations have raced to put in place AI-targeted policies to maximize the promise of the technology while also addressing its social and ethical implications. This chapter navigates the landscape of AI policymaking and tracks efforts taking place on the local, national, and international levels to help promote and govern AI technologies.