The fourth edition of the AI Index Report has been published this week by Stanford’s Institute for Human-Centred Artificial Intelligence (HAI). This report is an annual study that tracks, collates, distils, and visualises data related to artificial intelligence. The report is organised into seven chapters, namely: Research and Development, Technical Performance, The Economy, AI Education, Ethical Challenges of AI Applications, Diversity in AI, and AI Policy and National Strategies. Following are the top 9 takeaways as highlighted in the report:

• AI investment in drug design and discovery increased significantly: “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.
• The industry shift continues: 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.
• Generative everything: 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.
• AI has a diversity challenge: In 2019, 45% new U.S. resident AI PhD graduates were white—by comparison, 2.4% were African American and 3.2% were Hispanic.
• China overtakes the US in AI journal citations: After surpassing the United States in the total number of journal publications several years ago, China now also leads in journal citations; however, the United States has consistently (and significantly) more AI conference papers (which are also more heavily cited) than China over the last decade.
• The majority of the US AI PhD grads are from abroad—and they’re staying in the US: 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.
• Surveillance technologies are fast, cheap, and increasingly ubiquitous: The technologies necessary for large-scale surveillance are rapidly maturing, with techniques for image classification, face recognition, video analysis, and voice identification all seeing significant progress in 2020.
• AI ethics lacks benchmarks and consensus: 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.
• AI has gained the attention of the U.S. Congress: The 116th Congress is the most AI-focused congressional session in history with the number of mentions of AI in congressional record more than triple that of the 115th Congress.

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Image by Michi Turner for Stanford HAI