Saurabh Mishra

Academia

This chapter presents trends in public awareness of AI by tracking the AI mentions in public domains such as communications of central banks, parliament sessions in select countries and earning calls of publicly-traded companies in US.— July 7, 2020

Academia

AI is a key component of Autonomous Systems. This chapter presents data on Autonomous Systems divided in two sections: Autonomous Vehicles (AV’s) and Autonomous Weapons (AW’s). The AV section shows the countries and cities testing AV’s. This is followed by US state policy on AV from the National Conference on State Legislation (NCSL). The section on AW presents the known types of autonomous weapon deployments and by which country based on expert survey data collected by the Stockholm International Peace Research Institute (SIPRI).— July 7, 2020

Academia

This chapter presents trends in AI education from a variety of data sources, starting first with global data from Coursera and Udacity ML and AI training courses. Second, trends in undergraduate enrollment in introductory ML and AI courses are presented for the US and international universities. Third, trends in PhD hires on industry hiring, faculty hiring and faculty departures are presented based on the Taulbee Survey and Goffman and Jin (2019). Fourth, trends in gender and international diversity for AI PhDs are presented, along with faculty diversity across select university departments. — July 7, 2020

Academia

This chapter is broken into three sections: Jobs, Investment Activity, and Corporate Activity. The first section on AI Jobs shows data relating to AI jobs, hiring, and skill levels around the globe as well as in US regions. The section concludes with trends in skill penetration and labor demand for AI jobs from a sub-regional US perspective. The second section on Investment presents startup investment trends for the world, by countries, and by sectors. The third section on Corporate Activity includes data on adoption of AI capabilities in industry, drawing from McKinsey’s Global AI survey. — July 7, 2020

The technical performance chapter tracks technical progress in tasks across Computer Vision (Images, Videos, and Image+Language), Natural Language, potential limitations (Omniglot Challenge), and trends in computational capabilities.— July 7, 2020

Academia

This chapter presents bibliometrics data, Github Stars for key AI software libraries and discusses societal and gender diversity of AI researchers.— July 7, 2020

Academia

This chapter presents data on AI conference attendance and summary of topics and policy milestones achieved. — July 6, 2020

Academia

This chapter discusses AI ethical challenges; key topics in global news on AI and Ethics; and AI use cases supporting each the UN SDGs.— June 30, 2020

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. It begins with an overview of national and regional AI strategies and then reviews activities on the intergovernmental level. The chapter then takes a closer look at public investment in AI in the United States as well as how legislative bodies, central banks, and nongovernmental organizations are responding to the growing need to institute a policy framework for AI technologies.— March 15, 2021

This chapter presents diversity statistics within the AI workforce and academia. It draws on collaborations with various organizations—in particular, Women in Machine Learning (WiML), Black in AI (BAI), and Queer in AI (QAI)— each of which aims to improve diversity in some dimension in the field. The data is neither comprehensive nor conclusive. In preparing this chapter, the AI Index team encountered significant challenges as a result of the sparsity of publicly available demographic data. The lack of publicly available demographic data limits the degree to which statistical analyses can assess the impact of the lack of diversity in the AI workforce on society as well as broader technology development. The diversity issue in AI is well known, and making more data available from both academia and industry is essential to measuring the scale of the problem and addressing it.— March 15, 2021

This chapter tackles the efforts to address the ethical issues that have arisen alongside the rise of AI applications. It first looks at the recent proliferation of documents charting AI principles and frameworks, as well as how the media covers AI-related ethical issues. It then follows with a review of ethics-related research presented at AI conferences and what kind of ethics courses are being offered by computer science (CS) departments at universities around the world.— March 15, 2021

This chapter focuses on trends in the skills and training of AI talent through various education platforms and institutions. What follows is an examination of data from an AI Index survey on the state of AI education in higher education institutions, along with a discussion on computer science (CS) undergraduate graduates and PhD graduates who specialized in AI-related disciplines, based on the annual Computing Research Association (CRA) Taulbee Survey. The final section explores trends in AI education in Europe, drawing on statistics from the Joint Research Centre (JRC) at the European Commission.— March 15, 2021

This chapter looks at the increasingly intertwined relationship between AI and the global economy from the perspective of jobs, investment, and corporate activity. It first analyzes the worldwide demand for AI talent using data on hiring rates and skill penetration rates from LinkedIn as well as AI job postings from Burning Glass Technologies. It then looks at trends in private AI investment using statistics from S&P Capital IQ (CapIQ), Crunchbase, and Quid. The third, final section analyzes trends in the adoption of AI capabilities across companies, trends in robot installations across countries, and mentions of AI in corporate earnings, drawing from McKinsey’s Global Survey on AI, the International Federation of Robotics (IFR), and Prattle, respectively.— March 15, 2021

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.— March 15, 2021

Academia

This chapter begins by examining AI publications—from peer-reviewed journal articles to conference papers and patents, including the citation impact of each, using data from the Elsevier/Scopus and Microsoft Academic Graph (MAG) databases, as well as data from the arXiv paper preprint repository and Nesta. It examines contributions to AI R&D from major AI entities and geographic regions and considers how those contributions are shaping the field. The second and third sections discuss R&D activities at major AI conferences and on GitHub.— March 15, 2021

The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the world’s most credible and authoritative source for data and insights about AI. 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’s Institute for Human-Centered Artificial Intelligence (HAI).— March 15, 2021

Academia

This chapter presents trends in public awareness of AI by tracking the AI mentions in public domains such as communications of central banks, parliament sessions in select countries and earning calls of publicly-traded companies in US.— July 7, 2020

Academia

AI is a key component of Autonomous Systems. This chapter presents data on Autonomous Systems divided in two sections: Autonomous Vehicles (AV’s) and Autonomous Weapons (AW’s). The AV section shows the countries and cities testing AV’s. This is followed by US state policy on AV from the National Conference on State Legislation (NCSL). The section on AW presents the known types of autonomous weapon deployments and by which country based on expert survey data collected by the Stockholm International Peace Research Institute (SIPRI).— July 7, 2020