Can People Experience Romantic Love for Artificial Intelligence? An Empirical Study of Intelligent Assistants

2022 ◽  
pp. 103595
Author(s):  
Xia Song ◽  
Bo Xu ◽  
Zhenzhen Zhao
Keyword(s):  
Artificial Intelligence ◽  
Empirical Study ◽  
Romantic Love ◽  
Intelligent Assistants

scholarly journals Game Playing: Proposing and Developing Queen Challenge Puzzle Game from 1 to 25 Levels

2021 ◽  
Vol 14 (2) ◽  
pp. 87
Author(s):  
Hussain Mohammad Abu-Dalbouh ◽  
Fahad Almansour ◽  
Nehal Aldowighri
Keyword(s):  
Artificial Intelligence ◽  
Empirical Study ◽  
Positive Emotions ◽  
Descriptive Statistics ◽  
Game Playing ◽  
Game Development ◽  
Game Board ◽  
The Cost

In recent decades, massive improvements in graphic sophistication have begun to produce declining returns. The creative focus in game development has shifted to artificial intelligence. The queens’ task game is part of a sequence of popular games. It is the challenge of putting n chess queens on a game board such that no two queens are threatening each other. The plan does not involve two queens sharing the same row, column or diagonal. Each column contains exactly one queen, each row contains exactly one queen, and each diagonal contains exactly one queen. For every level in the game, there are many ways to solve it. For example, there are 92 solutions to the 8×8 problem. There are many levels in the literature, but each level should be downloaded separately. Thus, it causes a lot of difficulties for players, and they should download each level to complete the challenge. This will lead to more time and effort being spent by the players, and the cost of each level will cost the players more and more. As a result, the number of players who want to play this game will decrease. The aim of this paper is to incorporate a number of levels in order to save time, money and effort by downloading each level separately. This paper also aims to develop the proposed prototype and display all the solutions while playing a puzzle game at any level. The proposed game was tested by a questionnaire-based empirical study. Descriptive statistics on the questions revealed that the players had achieved the objectives of the game by applying their skills and knowledge and that the players had positive emotions about the effectiveness of the proposed game.

scholarly journals Adoption of artificial intelligence technologies in German SMEs — Results from an empirical study

2021 ◽  
Author(s):  
Patrick Ulrich ◽  
Vanessa Frank ◽  
Mona Kratt
Keyword(s):  
Artificial Intelligence ◽  
Empirical Study ◽  
German Economy ◽  
Success Factor ◽  
International Context ◽  
The Future

Artificial intelligence (AI) is globally regarded as one of the most important technologies of the future. Germany is not considered a pioneer in the field of AI in the international context, and the implementation of AI technologies is rather sluggish. As the German economy is mainly driven by small and medium-sized enterprises (SMEs), the implementation of AI in SMEs is the main success factor. This study discusses the implementation perspectives of AI in German SMEs based on an empirical study from the year 2020 among 283 companies

A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

2018 ◽  
pp. 2135-2160
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Empirical Study ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Prediction Performance ◽  
Critical Area ◽  
Classifier Selection ◽  
Comprehensive Knowledge

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

A Comprehensive Workflow for Enhancing Business Bankruptcy Prediction

2015 ◽  
pp. 216-238
Author(s):  
Rui Sarmento ◽  
Luís Trigo ◽  
Liliana Fonseca
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Empirical Study ◽  
Sustainability Assessment ◽  
Business Environment ◽  
Bankruptcy Prediction ◽  
Prediction Performance ◽  
Critical Area ◽  
Classifier Selection ◽  
Comprehensive Knowledge

Forecasting enterprise bankruptcy is a critical area for Business Intelligence. It is a major concern for investors and credit institutions on risk analysis. It may also enable the sustainability assessment of critical suppliers and clients, as well as competitors and the business environment. Data Mining may deliver a faster and more precise insight about this issue. Widespread software tools offer a broad spectrum of Artificial Intelligence algorithms and the most difficult task may be the decision of selecting that algorithm. Trying to find an answer for this decision in the relatively large amount of available literature in this area with so many options, advantages, and pitfalls may be as informative as distracting. In this chapter, the authors present an empirical study with a comprehensive Knowledge Discovery and Data Mining (KDD) workflow. The proposed classifier selection automation selects an algorithm that has better prediction performance than the most widely documented in the literature.

scholarly journals An Empirical Study on Emerging Trends in Artificial Intelligence and its Impact on Higher Education

2020 ◽  
Vol 175 (12) ◽  
pp. 43-47
Author(s):  
Virendra Gawande ◽  
Huda Al Badi ◽  
Khaloud Al Makharoumi
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Empirical Study ◽  
Emerging Trends
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