The Re-examination of the Dangers and Implications of Artificial Intelligence for the Future of Scholarship and Learning

Technology is rapidly developing, and integrating artificial intelligence (AI) into education has become a topic of great interest. While it promises to revolutionize how we learn and acquire knowledge, some significant downsides remain. From reducing human interaction to potentially losing jobs for educators, the impact of AI in education is far-reaching. In this article, we will explore the downsides of artificial intelligence in education and its effect on future generations. The study shows that the dangers inherent in integrating AI into scholarship and learning are multi-faceted. From the potential loss of human judgment and unintended consequences in education delivery to fostering dependency and narrowing research avenues, these risks emphasize the need for an informed and cautious approach. As the academic community embraces the benefits of AI, it must navigate these challenges to ensure that the core values of scholarship and learning remain intact and resilient

Development of Computer Science Disciplines - A Social Network Analysis Approach

In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published JCR (Journal Citation Report). Although this data covers most of important journals, it lacks computer science conference and workshop proceedings. That results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

University Knowledge Management Tool for Academic Research Activity Evaluation

The implementation of an efficient university knowledge management system involves the de-velopment of several software tools that assist the decision making process for the three main activities of a university: teaching, research, and management. Artificial intelligence provides a variety of techniques that can be used by such tools: machine learning, data mining, text mining, knowledge based systems, expert systems, case-based reasoning, decision support systems, intelligent agents etc. In this paper it is proposed a generic structure of a university knowledge management system, and it is presented an expert system, ACDI_UPG, developed for academic research activity evaluation, that can be used as a decision support tool by the university knowledge management system for planning future research activities according to the main objectives of the university and of the national / international academic research funding organizations.University Knowledge Management, Research Activity Evaluation, Artificial Intelligence, Expert Systems, Decision Support System

Playing Smart - Artificial Intelligence in Computer Games

Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games