Artificial intelligence-based material discovery for clean energy future

Artificial intelligence (AI)-assisted materials design and discovery methods can come to the aid of global concerns for introducing new efficient materials in different applications. Also, a sustainable clean future requires a transition to a low-carbon economy that is material-intensive. AI-assisted methods advent as inexpensive and accelerated methods in the design of new materials for clean energies. Herein, the emerging research area of AI-assisted material discovery with a focus on developing clean energies is discussed. The applications, advantages, and challenges of using AI in material discovery are discussed and the future perspective of using AI in clean energy is studied. This perspective paves the way for a better understanding of the future of AI applications in clean energies

Delegating Agency in the Public Sector: A Case Study on Current Human-Technology Practices and Visions for AI

Human-technology collaboration is currently receiving a surge of attention in Information Systems (IS) due to attempts to introduce Artificial Intelligence (AI) in private and public organizations. In Scandinavia, governments are introducing AI-infused services to support decision-making and enhance efficiency in case processing within healthcare, education, and welfare. However, there is a need to shed more light on the conditions that precede AI implementation and the path that leads organizations to envision AI as a solution to a problem. We ask: How do humans and technology cooperate in the public sector? How is AI visioned to be part of this in the future? We report from an ongoing qualitative case study of work practices to assess sick leave cases at a Scandinavian welfare agency in which AI gradually emerged as a means to achieve more efficient resource distribution at the agency. Inspired by the concept of delegation drawn from Actor-Network Theory, we trace the distribution of work across technical and human agents in the sick leave department and illustrate how the agency is starting to envision a way to delegate tasks to AI-based tools in the future

Current and Future Artificial Intelligence (AI) Curriculum in Business School: A Text Mining Analysis

As artificial intelligence (AI) becomes one of the most important driving forces in industrial innovations, more business schools, mostly in graduate programs, are introducing AI in their curricula, particularly in information systems (IS) curricula. However, there appears to be a paucity of research on the AI curriculum. This study examines the current status of the AI curriculum in both undergraduate and graduate business schools and provides recommendations for future AI curriculum development. The study develops a technical competency model for AI curriculum based on both MSIS2016 - Global Competency Model for Graduate Degree Programs in Information Systems and IS2020 - A Competency Model for Undergraduate Programs in Information Systems and the AI technical competencies. Using text mining analysis, we collected and analyzed AI courses from the top 46 business schools at both undergraduate and graduate levels, ranked by US News in 2020. The findings indicate that machine learning is at the core of the AI curriculum in business, and most AI curricula are a hybrid of AI and data analytics. This acknowledges that the AI curriculum is still at its early stage, and business schools are closely adhering to the industrial development trend. The proposed technical competency model for AI curriculum can serve as a guideline for future AI curriculum development in business schools. We hope this study provides systematic insight into AI curriculum and offers recommendations for business education, in IS programs specifically

A high-level overview of AI ethics

Artificial intelligence (AI) ethics is a field that has emerged as a response to the growing concern regarding the impact of AI. It can be read as a nascent field and as a subset of the wider field of digital ethics, which addresses concerns raised by the development and deployment of new digital technologies, such as AI, big data analytics, and blockchain technologies. The principle aim of this article is to provide a high-level conceptual discussion of the field by way of introducing basic concepts and sketching approaches and central themes in AI ethics. The first part introduces concepts by noting what is being referred to by “AI” and “ethics”, etc.; the second part explores some predecessors to AI ethics, namely engineering ethics, philosophy of technology, and science and technology studies; the third part discusses three current approaches to AI ethics namely, principles, processes, and ethical consciousness; and finally, the fourth part discusses central themes in translating ethics in to engineering practice. We conclude by summarizing and noting the inherent interdisciplinary future directions and debates in AI ethics

Towards Responsible AI in the Era of ChatGPT: A Reference Architecture for Designing Foundation Model-based AI Systems

The release of ChatGPT, Bard, and other large language model (LLM)-based chatbots has drawn huge attention on foundations models worldwide. There is a growing trend that foundation models will serve as the fundamental building blocks for most of the future AI systems. However, incorporating foundation models in AI systems raises significant concerns about responsible AI due to their black box nature and rapidly advancing super-intelligence. Additionally, the foundation model's growing capabilities can eventually absorb the other components of AI systems, introducing the moving boundary and interface evolution challenges in architecture design. To address these challenges, this paper proposes a pattern-oriented responsible-AI-by-design reference architecture for designing foundation model-based AI systems. Specially, the paper first presents an architecture evolution of AI systems in the era of foundation models, from "foundation-model-as-a-connector" to "foundation-model-as-a-monolithic architecture". The paper then identifies the key design decision points and proposes a pattern-oriented reference architecture to provide reusable responsible-AI-by-design architectural solutions to address the new architecture evolution and responsible AI challenges. The patterns can be embedded as product features of foundation model-based AI systems and can enable organisations to capitalise on the potential of foundation models while minimising associated risks

Engaging children and young people on the potential role of artificial intelligence in medicine

INTRODUCTION: There is increasing interest in Artificial Intelligence (AI) and its application to medicine. Perceptions of AI are less well-known, notably amongst children and young people (CYP). This workshop investigates attitudes towards AI and its future applications in medicine and healthcare at a specialised paediatric hospital using practical design scenarios. METHOD: Twenty-one members of a Young Persons Advisory Group for research contributed to an engagement workshop to ascertain potential opportunities, apprehensions, and priorities. RESULTS: When presented as a selection of practical design scenarios, we found that CYP were more open to some applications of AI in healthcare than others. Human-centeredness, governance and trust emerged as early themes, with empathy and safety considered as important when introducing AI to healthcare. Educational workshops with practical examples using AI to help, but not replace humans were suggested to address issues, build trust, and effectively communicate about AI. CONCLUSION: Whilst policy guidelines acknowledge the need to include children and young people to develop AI, this requires an enabling environment for human-centred AI involving children and young people with lived experiences of healthcare. Future research should focus on building consensus on enablers for an intelligent healthcare system designed for the next generation, which fundamentally, allows co-creation. IMPACT: Children and young people (CYP) want to be included to share their insights about the development of research on the potential role of Artificial Intelligence (AI) in medicine and healthcare and are more open to some applications of AI than others. Whilst it is acknowledged that a research gap on involving and engaging CYP in developing AI policies exists, there is little in the way of pragmatic and practical guidance for healthcare staff on this topic. This requires research on enabling environments for ongoing digital cooperation to identify and prioritise unmet needs in the application and development of AI

Trust in Construction AI-Powered Collaborative Robots: A Qualitative Empirical Analysis

Construction technology researchers and forward-thinking companies are experimenting with collaborative robots (aka cobots), powered by artificial intelligence (AI), to explore various automation scenarios as part of the digital transformation of the industry. Intelligent cobots are expected to be the dominant type of robots in the future of work in construction. However, the black-box nature of AI-powered cobots and unknown technical and psychological aspects of introducing them to job sites are precursors to trust challenges. By analyzing the results of semi-structured interviews with construction practitioners using grounded theory, this paper investigates the characteristics of trustworthy AI-powered cobots in construction. The study found that while the key trust factors identified in a systematic literature review -- conducted previously by the authors -- resonated with the field experts and end users, other factors such as financial considerations and the uncertainty associated with change were also significant barriers against trusting AI-powered cobots in construction.Comment: 2023 ASCE International Conference on Computing in Civil Engineering (I3CE

The Imitation Game: Detecting Human and AI-Generated Texts in the Era of Large Language Models

The potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionizing education, research, and practice. However, distinguishing between human-written and AI-generated text has become a significant task. This paper presents a comparative study, introducing a novel dataset of human-written and LLM-generated texts in different genres: essays, stories, poetry, and Python code. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text, despite the dataset's limited sample size. However, the task becomes more challenging when classifying GPT-generated text, particularly in story writing. The results indicate that the models exhibit superior performance in binary classification tasks, such as distinguishing human-generated text from a specific LLM, compared to the more complex multiclass tasks that involve discerning among human-generated and multiple LLMs. Our findings provide insightful implications for AI text detection while our dataset paves the way for future research in this evolving area

Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines

The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very much at the forefront of current scientific, governmental and public debate. While these might seem like very modern concerns, they have a long history that is often disregarded in contemporary discourse. Insofar as current debates do acknowledge the history of these ideas, they rarely look back further than the origin of the modern digital computer age in the 1940s-50s. In this paper we explore the earlier history of these concepts. We focus in particular on the idea of self-reproducing and evolving machines, and potential implications for our own species. We show that discussion of these topics arose in the 1860s, within a decade of the publication of Darwin's The Origin of Species, and attracted increasing interest from scientists, novelists and the general public in the early 1900s. After introducing the relevant work from this period, we categorise the various visions presented by these authors of the future implications of evolving machines for humanity. We suggest that current debates on the co-evolution of society and technology can be enriched by a proper appreciation of the long history of the ideas involved.Comment: To appear in Proceedings of the Artificial Life Conference 2018 (ALIFE 2018), MIT Pres