Can the g Factor Play a Role in Artificial General Intelligence Research?

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach

Competing Perspectives on Water Pollution for High School Students: A Q-Method Approach and Extended AI-Based Responses

High school environmental education faces significant challenges from diverse competing perspectives, ranging from sustainability advocates to political conflicts and economic interests.  This study critiques existing research on environmental education based on two key points: First, the conventional approach to high school environmental education predominantly concentrates on nature-related aspects.  Past research tends to overlook political, economic, and community dimensions, essentially providing an incomplete view of environmental education education.  Secondly, little empirical research has compared human perspectives on environmental education with generative AI-based viewpoints.  This comparison can contribute to enhancing the holistic view of environmental education by incorporating diverse human perspectives alongside AI-generated responses.  This study employs the Q-methodology that can uncover latent viewpoints by analyzing diverse opinions.  Moreover, this study attempts to compare the differences and similarities of responses from generative AI chatbots and humans.  While some issues receive recognition from both humans and AI, others are acknowledged only by humans.  Combining the insights from the Q-methodology and the comparison of human  and AI chatbot responses, this research contributes to a deeper understanding of water-related environmental education and perspectives &nbsp

Artificial intelligence and robots inindividuals’ lives: how to aligntechnological possibilities andethical issues

Purpose: This paper reports the panel discussion on the topic of artificial intelligence (AI) and robots in our lives. This discussion was held at the Digitization of the Individual (DOTI) workshop at the International Conference on Information Systems in 2019. Three scholars (in alphabetical order: Ting-Peng Liang, Lionel Robert, and Suprateek Sarker) who have done AI- and robot-related research (to varying degrees) were invited to participate in the panel discussion. The panel was moderated by Manuel Trenz. Design/methodology/approach: This paper introduces the topic, chronicles the responses of the three panelists to the questions the workshop chairs posed, and summarizes their responses, such that readers can have an overview of research on AI and robots in individuals’ lives, and insights about future research directions. Findings: The panelists discussed four questions with regards to their research experiences on AI- and robot-related topics. They expressed their viewpoints on the underlying nature, potential, and effects of AI in work and personal life domains. They also commented on the ethical dilemmas for research and practice, and provided their outlook for future research in these emerging fields. Originality/values: This paper aggregates the panelists’ viewpoints, as expressed at the DOTI workshop. Crucial ethical and theoretical issues related to AI and robots in both work and personal life domains are addressed. Promising research directions to these cutting-edge research fields are also proposed

Experimental evaluation of algorithms forsolving problems with combinatorial explosion

Solving problems with combinatorial explosionplays an important role in decision-making, sincefeasible or optimal decisions often depend on anon-trivial combination of various factors. Gener-ally, an effective strategy for solving such problemsis merging different viewpoints adopted in differ-ent communities that try to solve similar prob-lems; such that algorithms developed in one re-search area are applicable to other problems, orcan be hybridised with techniques in other ar-eas. This is one of the aims of the RCRA (Ra-gionamento Automatico e Rappresentazione dellaConoscenza) group,1the interest group of the Ital-ian Association for Artificial Intelligence (AI*IA)on knowledge representation and automated rea-soning, which organises its annual meetings since1994

Finding differences in perspectives between designers and engineers to develop trustworthy AI for autonomous cars

In the context of designing and implementing ethical Artificial Intelligence (AI), varying perspectives exist regarding developing trustworthy AI for autonomous cars. This study sheds light on the differences in perspectives and provides recommendations to minimize such divergences. By exploring the diverse viewpoints, we identify key factors contributing to the differences and propose strategies to bridge the gaps. This study goes beyond the trolley problem to visualize the complex challenges of trustworthy and ethical AI. Three pillars of trustworthy AI have been defined: transparency, reliability, and safety. This research contributes to the field of trustworthy AI for autonomous cars, providing practical recommendations to enhance the development of AI systems that prioritize both technological advancement and ethical principles

Employee’s Attitude Towards Artificial Intelligence in the Indian Banking Sector

Purpose: This paper aims to examine employees' attitudes towards the utilization of Artificial Intelligence (AI) in the Indian Banking Sector. The study delves into understanding how employees perceive the integration of AI technologies and its impact on various aspects of banking operations.   Theoretical framework: Built upon the significance of AI as a transformative technology, this research seeks insights into employees' perspectives on AI adoption within the Indian Banking Sector. The study is framed within the context of technological implementation and its influence on organizational processes.   Design/Methodology/Approach: Utilizing a mixed-methods approach involving surveys and interviews, this study collects data on employees' attitudes towards AI in banking. A questionnaire captures diverse viewpoints, while interviews provide deeper insights into the reasons underlying these attitudes.   Findings: The study uncovers a range of employee attitudes towards AI integration in the Indian Banking Sector. Positive responses highlight AI's contributions in areas like accounting, sales, contracts, and cybersecurity. Conversely, some employees express concerns about job security and advocate for enhanced training and upskilling opportunities.   Research, Practical & Social implications: This research adds a unique perspective by presenting Indian banking employees' viewpoints on AI implementation. The findings hold practical implications for banking organizations aiming to effectively manage the incorporation of AI technologies while addressing employee concerns. Additionally, the study underscores the importance of facilitating adequate training to ensure a seamless transition.   Originality/Value: This study stands out as one of the limited research endeavors focused on Indian banking employees' attitudes towards AI. By concentrating on this aspect, the research offers valuable insights into the human dimension of technological advancement, contributing both academically and practically to the banking sector

An Industry-Specific Investigation on Artificial Intelligence Adoption: The Cases of Financial Services and Manufacturing

Artificial Intelligence (AI) has a lasting transformational effect on industries worldwide. Former research has primarily focused on AI adoption as a business phenomenon without considering different industries. Those are characterized by unique attributes that may influence how modern technologies are implemented. In order to initiate non-generalized research in that field, industry-specific drivers and barriers to firm-level AI adoption in the financial services and the manufacturing industry are analyzed. Drawing on the Technology-Organization-Environment (TOE) framework, it was possible to paint a holistic picture of use cases and unique, but also general drivers and barriers of AI adoption for each industry. Ultimately, by bringing these two viewpoints together, a theory of hard (generalizable) and soft (industry-specific) AI adoption factors was developed. Therefore, the findings serve as a basis for further industry-specific research and provide business stakeholders and executives with a transparent handbook about industry insights and AI knowledge

Viewpoint Development of Stochastic Hybrid Systems

Nowadays, due to the explosive spreading of networked and highly distributed systems, mastering system complexity becomes a critical issue. Two development and verification paradigms have become more popular: viewpoints and randomisation. The viewpoints offer large freedom and introduce concurrency and compositionality in the development process. Randomisation is now a traditional method for reducing complexity (comparing with deterministic models) and it offers finer analytical analysis tools (quantification over non-determinism, multi-valued logics, etc). In this paper, we propose a combination of these two paradigms introducing a viewpoint methodology for systems with stochastic behaviours

Ethics in AI through the Developer's View: A Grounded Theory Literature Review

The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems: the AI developers? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI developers' views on ethics in AI and analysed them to derive five categories - developer awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from developers' viewpoints to assist AI developers in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI developers when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for developers, managers, and organisations to help in their efforts to better consider and implement ethics in AI.Comment: 40 pages, 5 figures, 4 table

Naïve Realism, Cognitive Bias, and the Benefits and Risks of AI

In this short piece I comment on Orly Lobel\u27s book on artificial intelligence (AI) and society The Equality Machine. Here, I reflect on the complex topic of aI and its impact on society, and the importance of acknowledging both its positive and negative aspects. More broadly, I discuss the various cognitive biases, such as naïve realism, epistemic bubbles, negativity bias, extremity bias, and the availability heuristic, that influence individuals\u27 perceptions of AI, often leading to polarized viewpoints. Technology can both exacerbate and ameliorate these biases, and I commend Lobel\u27s balanced approach to AI analysis as an example to emulate. Although AI is changing at an unprecedented rate, as exemplified by recent advances in Large Language Model (LLM) technology such as ChatGPT/GPT4, humans are adaptable, and society can actively steer toward a desirable future. By acknowledging the potential benefits and risks of AI, and by striving to overcome inherent cognitive biases, individuals can achieve a more balanced understanding of the technology and its impact on society