Conclusion

2020 ◽  
pp. 97-102
Author(s):  
Benjamin Wiggins
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
The United States ◽  
Blaise Pascal ◽  
Division Problem ◽  
The World

Can risk assessment be made fair? The conclusion of Calculating Race returns to actuarial science’s foundations in probability. The roots of probability rest in a pair of problems posed to Blaise Pascal and Pierre de Fermat in the summer of 1654: “the Dice Problem” and “the Division Problem.” From their very foundation, the mathematics of probability offered the potential not only to be used to gain an advantage (as in the case of the Dice Problem), but also to divide material fairly (as in the case of the Division Problem). As the United States and the world enter an age driven by Big Data, algorithms, artificial intelligence, and machine learning and characterized by an actuarialization of everything, we must remember that risk assessment need not be put to use for individual, corporate, or government advantage but, rather, that it has always been capable of guiding how to distribute risk equitably instead.

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

ARTIFICIAL INTELLIGENCE IN ECONOMY AND POLICY NOWADAYS. ARTICLE 3. ARTIFICIAL INTELLIGENCE IN RUSSIAN AND FOREIGN PUBLIC POLICY

2021 ◽  
pp. 3-32
Author(s):  
V.N. Leksin
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Public Policy ◽  
The United States ◽  
The Third ◽  
China And India ◽  
The World ◽  
Final Article ◽  
Country Differences

The third and final article of the three-part series of articles «Artificial intelligence in the economy and politics of our time» (the first and second articles of the series were published in the fourth and fifth issues of the journal for this year, respectively) presents the results of a study of the goals, motivations and specifics of the adoption of national strategies to support the development of artificial intelligence in different countries. It is shown that such a strategy in Russia is based on the idea of the most important role of using artificial intelligence in solving the most complex economic, social, and military-political problems of the country. Differences in conceptual approaches to the development of research and practical use of artificial intelligence developments in the national strategies of the largest countries of the world — the United States, China and India.

Tools for Educational Researchers Working With Big Data

2018 ◽  
pp. 53-75
Keyword(s):  
United States ◽  
Machine Learning ◽  
Big Data ◽  
Social Network ◽  
Network Analysis ◽  
Language Processing ◽  
The United States ◽  
Institutional Setting ◽  
University Of Texas ◽  
The University

Among the foremost challenges with big data is how to go about analyzing it. What new tools are needed to be able to properly investigate and model the large quantities of highly complex, often messy data? Chapter 4 addresses this question by introducing and briefly exploring the fields of Machine Learning, Natural Language Processing, and Social Network Analysis, focusing on how these methods and toolsets can be utilized to make sense of big data. The authors provide a broad overview of tools, ideas, and caveats for each of these fields. This chapter ends with a look at how one major public university in the United States, the University of Texas at Arlington, is beginning to address some of the questions surrounding big data in an institutional setting. A list of additional readings is provided.

scholarly journals Applications of Machine Learning in Cardiac Electrophysiology

2020 ◽  
Vol 9 (2) ◽  
pp. 71-77
Author(s):  
Rahul G Muthalaly ◽  
Robert M Evans ◽  
◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Diagnostic Accuracy ◽  
Cardiac Electrophysiology ◽  
Electronic Devices ◽  
Cardiac Implantable Electronic Devices ◽  
Supervised And Unsupervised Learning ◽  
The World ◽  
Applications Of Machine Learning

Artificial intelligence through machine learning (ML) methods is becoming prevalent throughout the world, with increasing adoption in healthcare. Improvements in technology have allowed early applications of machine learning to assist physician efficiency and diagnostic accuracy. In electrophysiology, ML has applications for use in every stage of patient care. However, its use is still in infancy. This article will introduce the potential of ML, before discussing the concept of big data and its pitfalls. The authors review some common ML methods including supervised and unsupervised learning, then examine applications in cardiac electrophysiology. This will focus on surface electrocardiography, intracardiac mapping and cardiac implantable electronic devices. Finally, the article concludes with an overview of how ML may impact on electrophysiology in the future.

scholarly journals Mapping U.S. Multinationals' Global AI R&D Activity

2020 ◽  
Author(s):  
Roxanne Heston ◽  
Remco Zwetsloot
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
North America ◽  
Research And Development ◽  
Open Source ◽  
Multinational Corporations ◽  
The United States ◽  
Artificial Intelligence Research ◽  
The World

Many factors influence where U.S. tech multinational corporations decide to conduct their global artificial intelligence research and development (R&D). Company AI labs are spread all over the world, especially in North America, Europe and Asia. But in contrast to AI labs, most company AI staff remain concentrated in the United States. Roxanne Heston and Remco Zwetsloot explain where these companies conduct AI R&D, why they select particular locations, and how they establish their presence there. The report is accompanied by a new open-source dataset of more than 60 AI R&D labs run by these companies worldwide.

scholarly journals Analysis of Covid-19 in the United States using Machine Learning

2020 ◽  
Vol 8 (1) ◽  
pp. 15-21
Author(s):  
James G. Koomson
Keyword(s):  
United States ◽  
Machine Learning ◽  
The United States ◽  
Global Level ◽  
Learning Models ◽  
The World ◽  
Day By Day ◽  
Machine Learning Models

The unprecedented outbreak of COVID-19 also known as the coronavirus has caused a pandemic like none ever seen before this century. Its impact has been massive on a global level. The deadly virus has commanded nations around the world to increase their efforts to fight against the spread of the virus after the stress it has put on resources. With the number of new cases increasing day by day around the world, the objective of this paper is to contribute towards the analysis of the virus by leveraging machine learning models to understand its behavior and predict future patterns in the United States (US) based on data obtained from the COVID-19 Tracking Project.

scholarly journals Innovative Empirical Model for Predicting National Banks’ Financial Failure with Artificial Intelligence Subset Data Analysis in the United States

2020 ◽  
Vol 3 (1) ◽  
pp. 98-111
Author(s):  
Karina Kasztelnik
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Empirical Model ◽  
The United States ◽  
Dividend Yield ◽  
Return On Equity ◽  
Financial Failure ◽  
Stock Value ◽  
The Impact

AbstractThe principal objective of this research study was to investigate the impact of the Great Economic Recession of 2008 on national banks’ equity investment valuations and create an empirical model for predicting national banks’ financial failure in the United States. The focal period of the study was from 2009 to 2012, and public data sources used. It is not known to what extent national banks’ stock value investments are based on the return on equity. This causal-comparative study explores the degree to which national banks’ value investment in terms of the price to earnings ratio impacts their return on equity and the extent to which these banks’ stock value investment in terms of dividend yield impacts their return on equity. We used statistical modeling and the machine learning model to find hidden patterns in the input data. The principal finding of this research is that the median earnings per share in 2012 and the dividend yield in 2009 were significantly larger than the median return on equity in 2009 and 2012. Additionally, the dividend yield in 2012 was significantly smaller than the median return on equity in 2012. These findings can contribute to improving our understanding of how banks can predict financial failure using the new machine learning features of artificial intelligence to build an early warning system with the innovative risk measurement tool.

scholarly journals Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence

2021 ◽  
Vol 13 (6) ◽  
pp. 3196
Author(s):  
Abdellah Chehri ◽  
Issouf Fofana ◽  
Xiaomin Yang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
Smart Grid ◽  
Machine Intelligence ◽  
Electrical Network ◽  
Critical Infrastructures ◽  
Data Sets ◽  
Electricity Grid

Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented.

scholarly journals Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study

10.2196/31983 ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. e31983
Author(s):  
Arriel Benis ◽  
Anat Chatsubi ◽  
Eugene Levner ◽  
Shai Ashkenazi
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Health Care ◽  
Social Media ◽  
Influenza Vaccination ◽  
Information Source ◽  
The United States ◽  
Influenza Vaccines ◽  
Clinical Aspects

Background Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence–based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: “flu,” “influenza,” “vaccination,” “vaccine,” and “vaxx.” We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that “flu” and “covid” occurrences were inversely correlated as “flu” disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: “health and medicine (biological and clinical aspects),” “protection and responsibility,” and “politics.” By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations’ engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process.

Expert System Applications to Ocean Shipping—A Status Report

1990 ◽  
Vol 27 (05) ◽  
pp. 265-284
Author(s):  
John J. Dumbleton
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Expert System ◽  
Expert Systems ◽  
The United States ◽  
Status Report ◽  
Commercial Sector ◽  
The Past ◽  
Ocean Shipping ◽  
The World

Artificial intelligence has been emerging as one of the fastest growing technologies during the past five years. One subset of this discipline that has experienced phenomenal growth, when measured by spending levels by the commercial sector and all levels of government, is that of Expert Systems. The development of Expert Systems applications for shipping operations has also been growing, albeit at a somewhat slower rate. However, a number of very innovative systems are nearing completion in the United States, as well as in Europe and Asia, that will radically change how vessels are operated and managed. This paper provides an overview of Expert Systems concepts, a discussion of the Maritime Administration's program of research and implementation of this technology, and a review of systems and other projects that are nearing completion in the United States as well as abroad. The paper concludes with prospects for future development of Expert Systems as the merchant fleets of the world look for ways to improve their competitiveness.

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