Artificial Intelligence Resource Center
Free access to research and expert commentary on AI, machine learning and related technologies — updated 7 September 2018
By the Elsevier Artificial Intelligence Program Posted on 18 July 2018
We created this Artificial Intelligence Resource Center for researchers, research leaders, funders, representatives of corporations, policymakers and anyone else interested in AI and the impact it is having on R&D, science and wider society. The site will be updated as new resources and data become available.
What's in the Artificial Intelligence Resource Center?
Elsevier’s Artificial Intelligence Program
There is no universally agreed upon definition of AI. Using a semantic analysis across four different sectors – Research, Education, Media and Technology – we discovered lexicon overlap is minimal. Yet, we all talk about AI as one field.
To help research leaders, policy makers and others to still be able to navigate this “field”, Elsevier is undertaking a project to build a comprehensive global examination of artificial intelligence. We are combining semantic research with insights from AI experts, practitioners and policy makers.
Some of the questions that may be addressed include, how has AI evolved over the last 20 years? How do the education trends differ regionally? How does knowledge travel between sectors? What are the ethical considerations?
The report on Artificial Intelligence will be published in late 2018 and will be freely available for all.
To contact the program, please feel free to email us.
Comments by experts contributing the AI Program
Roberto M. Cesar Jr., Adjunct Coordinator – FAPESP, Brazil
AI is an unforeseen revolution that has re-organized the scientific community, companies, government and society. A large amount of intellectual tasks are currently automated by AI procedures. Efforts and advances have been implemented in different scientific communities, institutions and companies spread all over the world. AI open-source libraries and training data sets are produced, shared and used interchangeably by researchers, programmers and students from all these communities. It is thus very important to count upon initiatives that help understanding the research landscape on AI, both to be a part of it as well as to analyze the possible outcomes of this ongoing revolution.
Prof. Tieniu Tan, Institute of Automation, Chinese Academy of Sciences
Governments can enforce policies and regulations, provide sufficient funding, and develop & maintain adequate infrastructure to support the Artificial Intelligence field. Different countries may have different initiatives and strategies and compete for AI talents. But they should also collaborate. In our era, international collaboration is essential – no country can strive in the AI field in isolation.
Prof. Zhenan Sun, Institute of Automation, Chinese Academy of Sciences
China's Artificial Intelligence research has developed very fast in recent years, increasing its global significance within the field. China has unique advantages in applied technology research and development, for example, in face recognition. AI education has been receiving more and more attention in recent years, not only in universities, but also in vocational colleges, and even in secondary and primary schools. This growing AI talent base will result in even greater future development of the AI field in China.
Raymond Perrault, Senior Technical Advisor, AI Center, SRI International
Because of the range of its problems of interest, diversity of its technical approaches, and dependence on other scientific areas for both fundamental techniques and applications, AI is a very hard field to circumscribe. Yet doing so is essential to understanding its technical progress, the resources it consumes, and its impact on society. The methodology being developed in this study is a major step in defining its contours.
Q&A with Elsevier CIO Dan Olley in Forbes Magazine
AI tech trends, symbiotic technology – and a new era of “augmented intelligence”
In “A CIO Hall of Famer’s Approach to Machine Learning,” technology author Peter High interviewed Elsevier’s CIO and Executive VP of Product Development about tech trends and their applications at Elsevier and beyond.
During the interview, Dan commented: "This era of symbiotic technology – enabled by combining AI, device miniaturization, 5G and an API-rich ecosystem – will untether us from screens and keyboards. As we think about training rather than programming these systems, new problems around bias and ethics will arise that we need to consider carefully."
Michelle Gregory, Elsevier's Vice President of Content & Innovation in Algemeen Dagblad (AD)
Michelle Gregory, Elsevier Vice President of Content & Innovation, shares her thoughts on AI and how Amsterdam is developing into a technology hot spot - Algemeen Dagblad (17 July)
- The greatest advances in AI: the experts’ view
- Hopes and fears for AI: the experts' view
- The biggest misconceptions about AI: the experts’ view
- Why data science is an art — and how to support the people who do it (Jabe Wilson, PhD, on his presentation at Bio-IT 2018)
- Jabe Wilson on the future of AI and scholarly publishing
Access to published research
The list covers a range of seminal, iconic and top-cited papers published by Elsevier in the space of AI. They have been carefully selected by our computer and data science experts and journal editorial teams. Those that are not published open access have been made freely available.
- Schmidhuber, J: Deep learning in neural networks: An overview, Neural Networks (2015)
- Baldi P, Sadowski P: The dropout learning algorithm Artificial Intelligence (2014) — open access
- Bobadilla, J, Ortega, F, Hernando, A, Gutiérrez, A: Recommender systems survey, Knowledge-Based Systems (2013)
- Ferrucci, D, Levas A, Bagchi S, Gondek D, Mueller E: Watson: Beyond Jeopardy! Artificial Intelligence (2013) — open archive
- Jain, AK: Data clustering: 50 years beyond K-means, Pattern Recognition Letters (2010)
- Hendler, J, Berners-Lee, T: From the Semantic Web to social machines: A research challenge for AI on the World Wide Web (2010) Artificial Intelligence (open archive)
- Salakhutdinov, R, Hinton, G: Semantic hashing, International Journal of Approximate Reasoning (2009)
- Craven, M, Dipasquo, D, Freitag, D, McCallum, A, Mitchell, T, Nigam, K, Slattery, S: Learning to construct knowledge bases from the World Wide Web, Artificial Intelligence (2000) — open archive
- Page, L, Brin, S: The anatomy of a large-scale hypertextual web search engine, Computer Networks (1998)
- Berners-Lee, TJ: The world-wide web, Computer Networks and ISDN Systems (1992)
- Sakai, T, Nagao, M, Fujibayashi, S: Line extraction and pattern detection in a photograph Pattern Recognition (1969)
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