Artificial Intelligence

2019 ◽  
pp. 13-22
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow, well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

Artificial Intelligence

2018 ◽  
pp. 98-105
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Intelligent Machines ◽  
History Of ◽  
Future Research Directions ◽  
New Research

Artificial intelligence is the science of creating intelligent machines. Human intelligence is comprised of numerous pieces of knowledge as well as processes for utilizing this knowledge to solve problems. Artificial intelligence seeks to emulate and surpass human intelligence in problem solving. Current research tends to be focused within narrow well-defined domains, but new research is looking to expand this to create global intelligence. This chapter seeks to define the various fields that comprise artificial intelligence and look at the history of AI and suggest future research directions.

scholarly journals Artificial Intelligence-Based Method to Improve Tourism with the Help of Android Studio

2021 ◽  
Vol 10 (6) ◽  
pp. 50-53
Author(s):  
Anusha . ◽  
Poorva Agrawal ◽  
Gagandeep Kaur
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Computer Science ◽  
Mobile Application ◽  
New Technology ◽  
Future Research ◽  
Human Intelligence ◽  
Research Directions ◽  
Research Areas ◽  
Future Research Directions

Artificial intelligence is a new technology in the field of computer science and information technology. Artificial intelligence techniques are very useful and also applied in many businesses. There are many research areas and opportunities in the field of Artificial intelligence. Artificial intelligence refers to the simulation of human intelligence in machines. Artificial intelligence allows app developers to achieve better mobile application experiences and also helps to improve personalized selections for users. This paper explores the different areas of the artificial intelligence-based system in tourism. This paper presents important future research directions.

The Same but Different

2019 ◽  
pp. 1-32
Author(s):  
Maria Northcote
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Online Courses ◽  
Future Research ◽  
Technological Innovations ◽  
Research Directions ◽  
Learning Programs ◽  
History Of ◽  
Future Research Directions ◽  
Practical Recommendations

The field of online learning, like many other technological innovations, has not burgeoned without controversy. Despite the debates about the role and value of online learning, it has continued to grow in many sectors, especially in higher education. Alongside the growth of online learning, discussions about its benefits and limitations have also flourished, and many studies have investigated the quality and integrity of online courses. This chapter offers an investigation of some of the history of online learning, concluding with a collection of practical recommendations and suggestions for future research directions to guide institutions embarking on online learning programs.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Main Features and Types of Educational Use of Wiki Technology

2021 ◽  
pp. 213-228
Author(s):  
Nikolaos Karipidis ◽  
Jim Prentzas
Keyword(s):  
Learning Communities ◽  
Learning Process ◽  
Future Research ◽  
Learning Approaches ◽  
Research Directions ◽  
Online Learning Communities ◽  
Wiki Technology ◽  
History Of ◽  
Future Research Directions ◽  
The Web

Wiki technology has become very popular during the last years and is used in many fields. It enables the collaborative creation and management of content retaining the history of changes. There is abundant wiki-based content on the web covering a large number of subjects. A significant contribution of wikis involves education. Under certain conditions, technology may enhance the learning process due to the unique features it encompasses. In this context, wikis may prove very helpful as they provide the infrastructure for collaborative learning approaches and the development of online learning communities. This chapter discusses main features of wikis, wiki features specifically required in education, and typical uses of wikis in education. Representative examples of successful wikis are presented. Future research directions are also outlined.

Hacia una conclusión

2020 ◽  
pp. 322-330
Author(s):  
Allison Margaret Bigelow
Keyword(s):  
Research Ethics ◽  
Indigenous Studies ◽  
Future Research ◽  
Research Directions ◽  
Theoretical Approaches ◽  
Colonial Archives ◽  
History Of ◽  
Future Research Directions ◽  
Historical Silences ◽  
The Relationship

This chapter reviews the major methodological and theoretical approaches used in Mining Language, at once concluding the book and gesturing toward future research directions in the fields of history of colonial science and technology and Indigenous Studies. Specifically, it reflects on the relationship between history and literary studies within these intersecting fields. By reflecting on what colonial archives say and do not say, the conclusion argues for the importance of research ethics and methods that confront, acknowledge, and respond to historical silences.

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

Organizational learning and Industry 4.0: findings from a systematic literature review and research agenda

2020 ◽  
Vol 27 (8) ◽  
pp. 2435-2457 ◽  
Author(s):  
Ricardo Belinski ◽  
Adriana M.M. Peixe ◽  
Guilherme F. Frederico ◽  
Jose Arturo Garza-Reyes
Keyword(s):  
Organizational Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Future Research ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions ◽  
New Research

PurposeIndustry 4.0 has been one of the most topics of interest by researches and practitioners in recent years. Then, researches which bring new insights related to the subjects linked to the Industry 4.0 become relevant to support Industry 4.0's initiatives as well as for the deployment of new research works. Considering “organizational learning” as one of the most crucial subjects in this new context, this article aims to identify dimensions present in the literature regarding the relation between organizational learning and Industry 4.0 seeking to clarify how learning can be understood into the context of the fourth industrial revolution. In addition, future research directions are presented as well.Design/methodology/approachThis study is based on a systematic literature review that covers Industry 4.0 and organizational learning based on publications made from 2012, when the topic of Industry 4.0 was coined in Germany, using data basis Web of Science and Google Scholar. Also, NVivo software was used in order to identify keywords and the respective dimensions and constructs found out on this research.FindingsNine dimensions were identified between organizational learning and Industry 4.0. These include management, Industry 4.0, general industry, technology, sustainability, application, interaction between industry and the academia, education and training and competency and skills. These dimensions may be viewed in three main constructs which are essentially in order to understand and manage learning in Industry 4.0's programs. They are: learning development, Industry 4.0 structure and technology Adoption.Research limitations/implicationsEven though there are relatively few publications that have studied the relationship between organizational learning and Industry 4.0, this article makes a material contribution to both the theory in relation to Industry 4.0 and the theory of learning - for its unprecedented nature, introducing the dimensions comprising this relation as well as possible future research directions encouraging empirical researches.Practical implicationsThis article identifies the thematic dimensions relative to Industry 4.0 and organizational learning. The understanding of this relation has a relevant contribution to professionals acting in the field of organizational learning and Industry 4.0 in the sense of affording an adequate deployment of these elements by organizations.Originality/valueThis article is unique for filling a gap in the academic literature in terms of understanding the relation between organizational learning and Industry 4.0. The article also provides future research directions on learning within the context of Industry 4.0.

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