"The AI Index Report tracks, collates, distills, and visualizes data relating to artificial intelligence. Its mission is to provide unbiased, rigorously-vetted data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Expanding annually, the Report endeavors to include data on AI development from communities around the globe."
There are nine chapters in the report, which I will list below with what I think are some of the more interesting take aways.
Of most interest to AI ethics followers will be the chapters on Conferences, to see the topics covered, Economy, to find out about diversity in AI hiring + jobs, Education, to assess the integration of ethics into computer science courses, Autonomous Systems, to discover the types of autonomous weapons being deployed, Public Perception, to compare governmental discourse on AI around the world, but most of all Societal Considerations, which in their words:
"Examines ethical challenges, global news on AI ethics, and AI applications for sustainable development. We present ethical challenge data by looking across ethical AI guidelines and also examine news coverage around AI’s ethical use. This section also maps AI use cases to the UN’s Sustainable Development Goals."
The nine chapters are:
- Research and Development.
"China now publishes as many AI journal and conference papers per year as Europe, having passed the US in 2006"
"Many Western European countries, especially the Netherlands and Denmark, as well as Argentina, Canada, and Iran show relatively high presence of women in AI research."
"Attendance at AI conferences continues to increase significantly. In 2019, the largest, 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%."
- Technical Performance
"Prior to 2012, AI results closely tracked Moore’s Law, with compute doubling every two years. Post-2012, compute has been doubling every 3.4 months."
"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."
"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%)."
"Only 19% of large companies surveyed say their organizations are taking steps to mitigate risks associated with explainability of their algorithms, and 13% are mitigating risks to equity and fairness, such as algorithmic bias and discrimination"
"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.1"
"Industry has become, by far, the largest consumer of AI talent. In 2018, over 60% of AI PhD graduates went to industry, up from 20% in 2004. In 2018, over twice as many AI PhD graduates went to industry as took academic jobs in the US."
- Autonomous Systems
"The total number of miles driven and total number of companies testing autonomous vehicles (AVs) in California has grown over seven-fold between 2015-2018. In 2018, the State of California licensed testing for over 50 companies and more than 500 AVs, which drove over 2 million miles."
- Public Perception
"There is a significant increase in AI related legislation in congressional records, committee reports, and legislative transcripts around the world."
- Societal Considerations
"Fairness, Interpretability and Explainability are identified as the most frequently mentioned ethical challenges across 59 Ethical AI principle documents."
"In over 3600 global news articles on ethics and AI identified between mid-2018 and mid-2019, the dominant topics are framework and guidelines on the ethical use of AI, data privacy, the use of face recognition, algorithm bias and the role of big tech."
"AI can contribute to each of the 17 United Nations (UN) Sustainable Development Goals (SDGs) through use cases identified to-date that address about half of the 169 UN SDG targets, but bottlenecks still need to be overcome to deploy AI for sustainable development at scale."