Min menu


Hot News

Ground-breaking Data from Elsevier Shows China Set to Become Global Leader in AI Research


One-of-a-kind analysis presents trends in artificial intelligence research around the world, with a focus on China, Europe and the United States, and uncovers a global 'brain drain' of universities to the private sector

China is growing in importance as a global leader in artificial intelligence (AI) research, and researchers are increasingly moving from academia to industry, especially states United, according to a new study released today by Elsevier, the Information Analytics company specializing in science and health.

The report shows that, globally, AI research has accelerated, growing by more than 12% per year over the past five years (2013-2017), compared to less than 5% for the 5 previous years (2008-2012). In contrast, research results overall, in all areas of the world, have increased by 0.8% per year over the past five years (2013-2017).

Elsevier's analysis reveals that the industry in the United States attracts the most AI talent from local and international universities. In Europe, there is a greater movement of academic talent towards non-European industry.

Over the past three years, data shows that Chinese universities are attracting more AI talent than they are losing, confirming that the country is on track to establish a leadership position in AI research. After overtaking the United States in AI research in 2004, China is poised to overtake Europe and become the largest source of AI research globally in the next four years, if the pace of current trends continues.

Another important revelation is that despite the growing societal benchmark of AI and media attention to the ethical implications of AI, academic research on the ethics of AI has been limited.

“The new generation of technology, commonly referred to as AI, is so important and yet there doesn't seem to be a common understanding of its exact definition,” said Dan Olley, chief technology officer at Elsevier. “With this in-depth study of AI research performance, we aim to provide clarity and new insight into the dynamics, trends and parameters of the field. The report is not a conclusion, but the start of a discussion on how best to enter the age of AI and increasingly symbiotic technology.

Enrico Motta, professor of knowledge technologies at the Open University in the UK and expert contributor to the report, said: “This report applies an in-depth analysis of text mining and semantic analysis across the literature. different industries to find out how to more fully define the field of AI primarily using AI to map AI. This is the most comprehensive characterization of AI products across different industries delivered to date. "

Examining 600,000 documents and over 700 domain-specific keywords across four industries - research, education, technology, and media - semantic analysis reveals that the field of AI focuses on seven distinct research areas:

  • Research and optimization,
  • Fuzzy systems,
  • Natural language processing and knowledge representation,
  • Computer vision,
  • Machine learning and probabilistic reasoning,
  • Planning and decision making, and
  • Neural networks.

Of these areas, research on machine learning and probabilistic reasoning, neural networks and computer vision shows the greatest volume of research and growth results.

Other regional findings highlighted in the report:

International mobility and collaborative models suggest that China operates in relative isolation from the wider research community.

Europe is the largest and most diverse region of AI scientific production (in terms of research areas within AI), with high and increasing levels of international collaborations outside of Europe.

In 2017, India was the third country in terms of production of AI research after China and the United States. Iran is ninth in publication production, tied with France and Canada. Germany and Japan remain the fifth and sixth largest AI research results.

Data used in the report is sourced from Elsevier's Scopus, Fingerprint Engine, PlumX, ScienceDirect and SciVal, TotalPatent from RELX, and is further supported by public sources including dblp, arXiv, Stanford AI Index, kamishima.net and Kaggle, as well as data sets