The field of artificial intelligence is growing at an astounding pace, and Elsevier is keeping up. Our latest report, Artificial Intelligence: How knowledge is created, transferred, and used, breaks new ground by offering a comprehensive mapping and definition of artificial intelligence as a field of study along with insights into global research trends.
“The new generation of technologies, commonly umbrellaed under AI, are so important, and yet there appears to be no shared understanding of its exact definition,” said Dan Olley, Chief Technology Officer at Elsevier. “With this comprehensive study of research performance in AI, we aim to provide clarity on and insights into the field’s dynamics, trends and parameters. The report is not a conclusion but the start of a discussion on how we best enter the era of AI and increasingly symbiotic technology.”
AI is becoming increasingly ubiquitous in daily life, from its use in the devices we depend on to the overarching concept itself. However, there is no standard international definition for the term. In an attempt to change that, the study reviews 600 documents and more than 700 field-specific keywords, applying text mining and semantic analytics across publications from four sectors –research, education, technology and media – to define and understand the field and its reach.
In the words of Dr. Enrico Motta, Professor of Knowledge Technologies at the Open University (UK) and one of the expert contributors to the report, we are “essentially using AI to map AI.”
“It is the most comprehensive characterization of AI outputs across different sectors delivered so far,” he said of the report.
With this approach, the report confirms seven distinct research areas of artificial intelligence:
- Search and Optimization
- Fuzzy Systems
- Natural Language Processing and Knowledge Representation
- Computer Vision
- Machine Learning and Probabilistic Reasoning
- Planning and Decision Making
- Neural Networks
With that definition as a foundation, the report revealed the following key insights:
- Of the above areas, research in Machine Learning and Probabilistic Reasoning, Neural Networks, and Computer Vision show the largest volume of research output and growth.
- Global research into AI has grown exponentially, especially compared to other research fields: AI research has increased by 12 percent over the past five years alone (2013-17) compared to less than 5 percent for the previous five years (2008-12). By contrast, research output overall, globally across all subject areas, has grown by 0.8 percent annually over the past five years (2013-17).
- Industry in the United States attracts the most AI talent from both local and international academia. In Europe there’s a stronger move of academic talent moving to non-European industry.
- Over the last three years, the data shows Chinese academia attracting more AI talent than it is losing, confirming that the country is on track to establish a leading position in AI research. Having overtaken the United States in AI research output in 2004, China is set to overtake Europe and become the biggest source of AI research globally in four years , if the pace of current trends continues. However, international mobility and collaboration patterns suggest that China operates in relative isolation from the wider research community.
- Europe is the largest and most diverse (in terms of research areas within AI) region in AI scholarly output, with high and rising levels of international collaborations outside of Europe.
- In 2017, India was the third largest country in terms of research output in AI after China and the US. Iran is ninth in publication output, on par with France and Canada. Germany and Japan remain the fifth and sixth largest in AI research output.
- Despite the increasing prevalence of AI in daily life and a growing understanding of AI’s potential ethical dilemmas, limited research exists on the ethics of AI.
These findings, along with many others in the report, illuminate the potential for the future of global AI research; they also point toward the field’s current limitations. We look forward to the chance to learn more about the field as it continues to develop at a rapid pace, and to work alongside the international AI scholars of the future to help change the face of the world we live in.
About the report data
Data used in the report come from Elsevier’s Scopus, Fingerprint Engine, PlumX Metrics, ScienceDirect, and SciVal; and TotalPatent by LexisNexis (part of Elsevier’s parent company, RELX Group). It further draws on public sources, including dblp, arXiv, the Stanford AI Index, kamishima.net and Kaggle, as well as datasets provided by the Institute of Automation, Chinese Academy of Science.
Analyses were further informed by experts from around the world who advised on the report’s development, including the research questions, methodologies and analytics, and who provided a policy context for the findings.