The AI Index Report 2019 is prepared by Stanford Institute of Human-Centered Artificial Intelligence.
Between 1998 and 2018, the volume of peer-reviewed AI papers has grown by more than 300%, accounting for 3% of all peer-reviewed journal publications and 9% of published conference papers.
China now publishes as many AI journals and conference papers per year as Europe, having passed the USA in 2006. The Field-Weighted Citation Impact of USA publications is still about 50% higher than China’s.
Attendance at AI conferences continues to increase significantly. In 2019, NeurIPS expects 13,500 attendees, up 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 effort to include women and underrepresented groups in the AI field.
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 same period, the cost to train such a system has fallen similarly.
Progress on some broad sets of natural-language processing (NLP) 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 as the AI2 Reasoning Challenge, or human-level concept learning task, such as the Omniglot Challenge.
In the US, the share of AI jobs grew from 0.3% in 2012 to 0.8% of total jobs in 2019. AI labor demand is growing, especially in high-tech services and the manufacturing sector.
Globally, investment in AI startups continues its steady ascent. From a total of $1.3B raised in 2010 to over $40.4B in 2018 (with $37.4B in 2019 as of November 4th), funding has increased at an average annual growth rate of over 48%.
Autonomous Vehicles (AVs) received the largest share of global investment over the last year with $7.7B (9.9% of the total), followed by Drug, Cancer and Therapy ($4.7B, 6.1%), Facial Recognition ($4.7B, 6.0%), Video Content ($3.6B, 4.5%), and Fraud Detection and Finance ($3.1B, 3.9%).
58% of large companies surveyed report adopting AI in at least one function or business unit in 2019, up from 47% in 2018.
At the graduate level, AI has rapidly become the most popular specialization among computer science PhD students in North America, with over twice as many students as the second most popular specialization (security/information assurance). In 2018, over 21% of graduating Computer Science PhDs specialized in Artificial Intelligence/Machine Learning.
Diversifying AI faculty along gender lines has not shown great progress, with women comprising less than 20% of the new faculty hires in 2018. Similarly, the share of female AI PhD recipients has remained virtually constant at 20% since 2010 in the US.
There is a significant increase in AI-related legislation in congressional records, committee reports, and legislative transcripts around the world.
Fairness, Interpretability and Explainability are identified as the most frequently mentioned ethical challenges across 59 ethical AI principle documents.