Artificial Intelligence Conference Research Articles

Steering AI in the Right Direction

Abstract The 43rd BCS Specialist Group for Artificial Intelligence conference saw delegates, keynote speakers, students and academics meet, share, debate and learn. Martin Cooper MBCS reports from Peterhouse, Cambridge.

Statistical model and method for analyzing AI conference rankings: China vs USA

Artificial Intelligence (AI) is a rapidly developing field of research that attracts significant funding from both the state and industry players. Such interest is driven by a wide range of AI technology applications in many fields. Since many AI research topics relate to computer science, where a significant share of research results are published in conference proceedings, the same applies to AI. The world leaders in artificial intelligence research are China and the United States. The authors conducted a comparative analysis of the bibliometric indicators of AI conference papers from these two countries based on Scopus data. The analysis aimed to identify conferences that receive above-average citation rates and suggest publication strategies for authors from these countries to participate in conferences that are likely to provide better dissemination of their research results. The results showed that, although Chinese researchers publish more AI papers than those from the United States, US conference papers are cited more frequently. The authors also conducted a correlation analysis of the MNCS index, which revealed no high correlation between MNCS USA vs. MNCS China, MNCS China/MNCS USA vs. MSAR, and MNCS China/MNCS USA vs. CORE ranking indicators.

Firms’ influence on the evolution of published knowledge when a science-related technology emerges: the case of artificial intelligence

Firms with the assets complementary to Artificial Intelligence (AI) have actively conducted AI research and selectively published their results since AI has resurged around 2006. Focusing on the recent AI development, we investigate how and to what extent firms’ deep engagement in the publication of emerging science-related technology can influence the evolution of published knowledge. Using bibliometric analyses applied to the papers in major AI conferences and journals, we find that papers with at least one author affiliated to a firm, and particularly papers with only firm-affiliated author(s), have had higher influence on the formation of published knowledge trajectory than other papers. In addition, papers from firm and non-firm (university and public research institution) collaborations show higher novelty and conventionality than other papers. These findings deepen our understanding of the role of firms in the evolution of emerging science-related technology.

Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers

Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains.

Facilitating a smoother transition to renewable energy with AI

Artificial intelligence (AI) can help facilitate wider adoption of renewable energy globally. We organized a social event for the AI and renewables community to discuss these aspects at the International Conference on Learning Representations (ICLR), a leading AI conference. This opinion reflects on the key messages and provides a call for action on leveraging AI for transition toward net zero.

Institutionalizing ethics in AI through broader impact requirements

Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by one of the world's largest AI conferences. In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. Drawing insights from similar governance initiatives, including institutional review boards (IRBs) and impact requirements for funding applications, we investigate the risks, challenges and potential benefits of such an initiative. Among the challenges, we list a lack of recognised best practice and procedural transparency, researcher opportunity costs, institutional and social pressures, cognitive biases, and the inherently difficult nature of the task. The potential benefits, on the other hand, include improved anticipation and identification of impacts, better communication with policy and governance experts, and a general strengthening of the norms around responsible research. To maximise the chance of success, we recommend measures to increase transparency, improve guidance, create incentives to engage earnestly with the process, and facilitate public deliberation on the requirement's merits and future. Perhaps the most important contribution from this analysis are the insights we can gain regarding effective community-based governance and the role and responsibility of the AI research community more broadly.

Preface

The 2020 Big Data and Artificial Intelligence Conference was successfully held on September 17-18, 2020 online due to COVID-19 restrictions. The conference is devoted to current challenges in Big Data analytics & AI and comprises three tracks: business, technology and science.There were 3 major sections of the scientific track of the conference:-Cluster analysis-Applied systems for data analysis-Natural Language ProcessingThis volume of IOP Conference Series: Journal of Physics: Conference Series (JPCS) is a compilation of the accepted papers in Big Data and AI Conference 2020 and represents contributions that were presented in the conference.On behalf of the organization committee, I would like to thank all of the conference sponsors, partners and volunteers who made conference possible.Looking forward to meeting you during Big Data and AI Conference 2021.On behalf of organizing and program committees of the Big Data and AI Conference 2020Igor Balk, Big Data and AI Conference 2020 co-chair.

Shanghai Metro Constructs an “Intelligent Metro”

The World Artificial Intelligence (AI) Conference 2020 opened in Shanghai on July 9, 2020. The AI application scenarios presented by Shanghai Shentong Metro Group Co., Ltd. (Shentong Metro Group) have attracted a lot of attention of visitors. On July 9, 2020, the total passenger flow of Shanghai Metro exceeded 10 million, reaching 10.048 million, and Shanghai will enter the normal state of “ten million passenger flow” every day. At present, the operating mileage of Shanghai Metro reaches 705km, ranking first in the world. According to the new line planning and construction trend, the Shanghai Metro network will continue to expand, and there will be even more passenger flows in the future.

Reports of the Workshops Held at the 2020 International Association for the Advancement of Artificial Intelligence Conference on Web and Social Media

The workshop program of the Association for the Advancement of Artificial Intelligence's Fourteenth International Conference on Web and Social Media was held June 8 to 20, 2020. The conference venue, which had originally been Atlanta, Georgia, USA, had to be revamped into a virtual edition, due to the 2019 Corona Virus Disease pandemic. There were five full‐day workshops in the program. They included Emoji Understanding and Applications in Social Media (W1), Social Sensing: Narrative Analysis on Social Media (W2), News and Public Opinion (W3), Cyber Social Threats (W4), and Mediate: Social and News Media Misinformation (W5). This report contains summaries of all five workshops.

Correlation between references and citations in artificial intelligence: A preliminary study

AbstractTo reveal whether there exists a correlation between the number of references and the number of citations in a paper of the Artificial Intelligence‐related conference, we present an experiment based on a total number of 9,458 papers published in CVPR, ICML, and AAAI from the year 2014 to 2018. Spearman's rank correlation tests show that the number of references and that of the citations are correlated with a correlation coefficient of 0.435, 0.464, and 0.180 respectively, in each p < .01. This means an increasing number of references in a paper may be contributing to the increase in citations. However, citing more references does not guarantee that a paper will be a highly cited paper. We hope this finding can provide a basis for the prediction of the citation impact of the Artificial Intelligence conference papers in the future.

Research Updates from the Deployed Applications of the 2018 Innovative Applications of Artificial Intelligence Conference (IAAI‐18): Editorial Introduction to the Special Articles on Innovative AI Applications

This editorial introduces several of the deployed applications that were described at the 2018 Innovative Applications of Artificial Intelligence conference, held in New Orleans, Louisiana, in February 2018.

Standing on the Feet of Giants — Reproducibility in AI

A recent study implies that research presented at top artificial intelligence conferences is not documented well enough for the research to be reproduced. My objective was to investigate whether the quality of the documentation is the same for industry and academic research or if differences actually exist. My hypothesis is that industry and academic research presented at top artificial intelligence conferences is equally well documented. A total of 325 International Joint Conferences on Artificial Intelligence and Association for the Advancement of Artificial Intelligence research papers reporting empirical studies have been surveyed. Of these, 268 were conducted by academia, 47 were collaborations, and 10 were conducted by the industry. A set of 16 variables, which specifies how well the research is documented, was reviewed for each paper and each variable was analyzed individually. Three reproducibility metrics were used for assessing the documentation quality of each paper. The findings indicate that academic research does score higher than industry and collaborations on all three reproducibility metrics. Academic research also scores highest on 15 out of the 16 surveyed variables. The result is statistically significant for 3 out of the 16 variables, but none of the reproducibility metrics. The conclusion is that the results are not statistically significant, but still indicate that my hypothesis probably should be refuted. This is surprising, as the conferences use double‐blind peer review and all research is judged according to the same standards.

Preface

The 2019 Big Data and Artificial Intelligence Conference was successfully held on September 18-19, 2019 in Moscow, Russia. The conference is devoted to current challenges in Big Data analytics & AI and comprises three tracks: business, technology and science.There were 3 major sections of the scientific track of the conference:- Cluster analysis- Applied systems for data analysis- Natural Language ProcessingThis volume of IOP Conference Series: Journal of Physics: Conference Series (JPCS) is a compilation of the accepted papers in Big Data and AI Conference 2019 and represents contributions that were presented in the conference.On behalf of the organization committee, I would like to thank all of the conference sponsors, partners and volunteers who made conference possible.Looking forward to meeting you during Big Data and AI Conference 2020.On behalf of organizing and program committees of the Big Data and AI Conference 2019Igor Balk, Big Data and AI Conference 2019 co-chair.

An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications

Altmetrics indices are increasingly applied to measure scholarly influence in recent years because they can reflect the influence of research outputs more timely comparing with traditional measurements. Simultaneously, artificial intelligence (AI), as an emerging interdiscipline, has a rapid development in these years. Traditional indices can’t reflect the influence of the AI research outputs quickly, thus more timely altmetrics indices are needed. In this paper, we conduct four studies about altmetrics indices and AI research outputs based on the datasets collected from Altmetric.com and Scopus database. First, we provide a review of the research status in the AI field. Second, we show the AI researches that attracted the most attention. Third, we demonstrate the general effectiveness of altmetrics indices in the AI field. Last, we examine the effectiveness of altmetrics indices for different levels of AI journal papers and AI conference papers. Our results indicate that there is a rapid increase of AI publications and the public has paid more attention to AI research outputs since 2011. It is found that altmetrics indices are effective to discriminate highly cited publications and publications whose citation counts increase quickly. Among all Altmetric sub-indicators, Number of Mendeley readers is the most effective. Moreover, the results indicate that altmetrics indices are more effective in high levels of AI journal papers and AI conference papers. The main contribution of this paper is investigating the effectiveness of altmetrics indices from the perspective of different levels of publications. This study lays the foundation for further investigations about effectiveness of altmetrics indices from new perspectives, and it has important implication for the studies about the impact of social media on the scientific community.

Canada denied visas to dozens of Africans for a big artificial intelligence conference

Canada denied visas to dozens of Africans for a big artificial intelligence conference

Outline for a German Strategy for Artificial Intelligence

Outline for a German Strategy for Artificial Intelligence

The State of the AIIDE Conference in 2017

This abstract looks at the state of the Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), describing some of the changes in the field and areas of focus for current work.

Advancing artificial intelligence research and dissemination through conference series: Benchmark, scientific impact and the MICAI experience

This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained.

About the Authors

About the Authors

Introduction to the Special Issue: Innovative Applications of Artificial Intelligence 2011

Every year, AI Magazine devotes one fourth of its annual production to a special issue based on the Innovative Applications of Artificial Intelligence conference. Because IAAI is the premier venue for documenting the transition of AI technology into application, these special issues provide a snapshot of the state of the art in AI with the practical syllogism in mind; they present work that has value because it delivers value in use.