Artificial Intelligence Design Research Articles

Towards holistic insect monitoring: species discovery, description, identification and traits for all insects.

Holistic insect monitoring needs scalable techniques to overcome taxon biases, determine species abundances, and gather functional traits for all species. This requires that we address taxonomic impediments and the paucity of data on abundance, biomass and functional traits. We here outline how these data deficiencies could be addressed at scale. The workflow starts with large-scale barcoding (megabarcoding) of all specimens from mass samples obtained at biomonitoring sites. The barcodes are then used to group the specimens into molecular operational taxonomic units that are subsequently tested/validated as species with a second data source (e.g. morphology). New species are described using barcodes, images and short diagnoses, and abundance data are collected for both new and described species. The specimen images used for species discovery then become the raw material for training artificial intelligence identification algorithms and collecting trait data such as body size, biomass and feeding modes. Additional trait data can be obtained from vouchers by using genomic tools developed by molecular ecologists. Applying this pipeline to a few samples per site will lead to greatly improved insect monitoring regardless of whether the species composition of a sample is determined with images, metabarcoding or megabarcoding. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Achieving health equity through conversational AI: A roadmap for design and implementation of inclusive chatbots in healthcare.

The rapid evolution of conversational and generative artificial intelligence (AI) has led to the increased deployment of AI tools in healthcare settings. While these conversational AI tools promise efficiency and expanded access to healthcare services, there are growing concerns ethically, practically and in terms of inclusivity. This study aimed to identify activities which reduce bias in conversational AI and make their designs and implementation more equitable. A qualitative research approach was employed to develop an analytical framework based on the content analysis of 17 guidelines about AI use in clinical settings. A stakeholder consultation was subsequently conducted with a total of 33 ethnically diverse community members, AI designers, industry experts and relevant health professionals to further develop a roadmap for equitable design and implementation of conversational AI in healthcare. Framework analysis was conducted on the interview data. A 10-stage roadmap was developed to outline activities relevant to equitable conversational AI design and implementation phases: 1) Conception and planning, 2) Diversity and collaboration, 3) Preliminary research, 4) Co-production, 5) Safety measures, 6) Preliminary testing, 7) Healthcare integration, 8) Service evaluation and auditing, 9) Maintenance, and 10) Termination. We have made specific recommendations to increase conversational AI's equity as part of healthcare services. These emphasise the importance of a collaborative approach and the involvement of patient groups in navigating the rapid evolution of conversational AI technologies. Further research must assess the impact of recommended activities on chatbots' fairness and their ability to reduce health inequalities.

The Future Role of Artificial Intelligence (AI) Design's Integration into Architectural and Interior Design Education is to Improve Efficiency, Sustainability, and Creativity

The Future Role of Artificial Intelligence (AI) Design's Integration into Architectural and Interior Design Education is to Improve Efficiency, Sustainability, and Creativity

Developing a conceptual technical framework for ethical AI in procurement with emphasis on legal oversight

This study presents a conceptual technical framework aimed at promoting ethical AI deployment within the procurement domain, with a particular focus on legal oversight. As the integration of artificial intelligence (AI) technologies in procurement processes becomes increasingly prevalent, concerns surrounding ethical considerations and legal compliance have come to the forefront. The framework outlined in this study offers a structured approach to addressing these challenges, emphasizing the importance of legal oversight in ensuring ethical AI practices. Drawing on existing literature and best practices, the framework outlines key components and principles for guiding the development, implementation, and monitoring of AI systems in procurement contexts. Central to the framework is the recognition of legal requirements and regulatory frameworks governing AI deployment, including data protection laws, liability provisions, and procurement regulations. By incorporating these legal considerations into the design and operation of AI systems, organizations can mitigate risks and ensure compliance with applicable laws. Additionally, the framework emphasizes the need for transparency and accountability in AI procurement processes, advocating for clear documentation, audit trails, and stakeholder engagement mechanisms. Furthermore, the framework outlines strategies for ethical AI design, including the identification and mitigation of algorithmic bias, the promotion of fairness and equity, and the protection of privacy rights. By embedding ethical principles into the development lifecycle of AI systems, organizations can foster trust and confidence among stakeholders while minimizing the potential for harm or discrimination. Overall, the conceptual technical framework presented in this study provides a comprehensive approach to promoting ethical AI in procurement, with a specific emphasis on legal oversight. By integrating legal requirements, ethical principles, and technical considerations, organizations can ensure that AI deployment in procurement processes is conducted responsibly, transparently, and in accordance with legal and ethical standards.

Artificial Intelligence: Opportunities and Challenges for the Social Education and Profession

Artificial intelligence (AI) is rapidly advancing and holds both opportunities and risks for the social work profession. This review article provides an analysis of the potential applications and implications of AI technologies for social work practice, education, research, and ethics. The article highlights promising uses of AI tools like predictive risk models, virtual simulations, chatbots, and data analytics to enhance social work services and training. However, it also critically examines the ethical considerations and social justice concerns related to the integration of AI in social work, including issues of privacy, transparency, accountability, consent, bias, displacement of roles, and threats to human dignity. To responsibly leverage AI's benefits while mitigating its risks, the article offers recommendations for social workers to collaborate on AI design, follow ethical protocols, pursue education on AI, and conduct further research. Key areas for future research identified include studying client perspectives on AI, testing the efficacy of AI systems for social work tasks, auditing algorithms for bias, and analyzing AI's impact on social work employment. In conclusion, while acknowledging AI's significant potential, the article emphasizes that purposeful oversight, participatory design, and adherence to social work values must remain priorities to ensure AI promotes dignity, autonomy, and justice. This review provides a balanced, evidence-based analysis to inform discussions on optimizing AI's integration in the social work profession.

Artificial intelligence and smile design: An e-Delphi consensus statement of ethical challenges.

Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues. An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e-Delphi protocol was used to seek the agreement of the ITU-WHO group on well-established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design. On the first round of the e-Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design. AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.

Development of a novel packer fluid for high-temperature and high-pressure oil and gas wells with using design of experiments and artificial intelligence

Development of a novel packer fluid for high-temperature and high-pressure oil and gas wells with using design of experiments and artificial intelligence

Review of Personalized Medicine and Pharmacogenomics of Anti-Cancer Compounds and Natural Products.

In recent years, the FDA has approved numerous anti-cancer drugs that are mutation-based for clinical use. These drugs have improved the precision of treatment and reduced adverse effects and side effects. Personalized therapy is a prominent and hot topic of current medicine and also represents the future direction of development. With the continuous advancements in gene sequencing and high-throughput screening, research and development strategies for personalized clinical drugs have developed rapidly. This review elaborates the recent personalized treatment strategies, which include artificial intelligence, multi-omics analysis, chemical proteomics, and computation-aided drug design. These technologies rely on the molecular classification of diseases, the global signaling network within organisms, and new models for all targets, which significantly support the development of personalized medicine. Meanwhile, we summarize chemical drugs, such as lorlatinib, osimertinib, and other natural products, that deliver personalized therapeutic effects based on genetic mutations. This review also highlights potential challenges in interpreting genetic mutations and combining drugs, while providing new ideas for the development of personalized medicine and pharmacogenomics in cancer study.

Learner Experiences of Mobile Apps and Artificial Intelligence to Support Additional Language Learning in Education

This study examines learners’ experiences and the use of language learning applications (“apps”) as a primary source of second or additional language learning (“L2”) instruction and assessment in higher education. It purviews the integration of artificial intelligence (AI)-powered features that support technology-enhanced language learning experiences. Principles of pedagogy, heutagogy, and self-determination theory are used to inform the appropriate design and application of AI to support language learning. We examine the congruence between learner's goals with perceived outcomes following a 4-week language learning intervention using an app. A survey of n = 151 adult learners across two Canadian universities revealed: (a) apps are perceived as an engaging, convenient, and structured approach to early stages of L2 learning and (b) the integration of AI for conversation-based simulations or speech recognition would enable more adaptive, personalized L2 learning experiences. The authors discuss implications for future developments and AI uptake for language learning apps.

Overview of Memristor-Based Design for Analog Applications.

Memristor-based design has gained significant attention in recent years due to its potential to revolutionize various fields such as artificial intelligence, neuromorphic computing, non-volatile memory, signal processing, filtering, and radio frequency design. These emerging devices offer unique advantages such as non-volatile memory, low power consumption, and a high integration density. Their scalability and compatibility with existing fabrication processes make them an attractive option for industry adoption, paving the way for faster and more efficient architecture design. Researchers are actively exploring ways to optimize memristor technology for practical applications to harness its full potential. This includes developing novel materials and structures as well as improving the reliability and performance of memristors in various applications. This paper provides a comprehensive overview of the current advancements in memristor technology and their potential impact on the design of future electronic systems, focusing on its applications in the analog domain. By exploring the latest research and development in this field, researchers can gain valuable insights into how analog memristors can be integrated into their designs to achieve enhanced performance and efficiency. The paper delves into the fundamental principles of memristor technology, exploring its unique characteristics and advantages over traditional electronic components. It discusses the potential impact of memristors and challenges in the analog field of electronics, and highlights the progress made in their integration with existing circuitry, enabling novel functionalities and improved performance. Furthermore, it highlights ongoing research efforts to improve the performance and reliability of memristors, as well as the potential limitations and challenges that need to be addressed for widespread adoption, including variability in performance and reliability.

Examining the Influence of Artificial Intelligence Implementation in HRM Practices Using T-O-E Model

Given the swift progress of artificial intelligence in contemporary economies, HR managers are also adopting artificial intelligence tools to perform many HR tasks, from human resource planning to employee quitting. This research employs the TOE (Technology–Organization–Environment) model and incorporates the trust factor to propose a methodology for investigating artificial intelligence (AI) adoption in HRM. A structured questionnaire was used to survey 615 ITeS companies. Data analysis was performed using partial least squares structural equation modelling. The findings of this research indicate that factors such as cost-effectiveness, relative advantage, organization size, top management support, HR readiness, competitive pressure, vendor support, and the trust of HR managers are considered to have a positive impact on AI adoption in managing human resources. The utilization of AI in HRM has been cumbersome by conflicting results stemming from security and privacy concerns and the complexity of the technology involved. It is also found that factors such as reliability and credibility have a positive effect on the trust of HR managers. This article aims to provide valuable insights to senior executives, HR managers, experts, researchers, AI designers, developers, and marketers by strengthening their understanding of the impact of AI in managing human resources.

An experimental hybrid customized AI and generative AI chatbot human machine interface to improve a factory troubleshooting downtime in the context of Industry 5.0

Throughout industrial revolutions, equipment downtime mitigations have been one of the ultimate goals of most factories. Several tools, such as human machine interface (HMI) alarming systems or predictive maintenance schedules, assist in reducing system downtime but still depend on the operators’ ability to swiftly retrieve, understand, and efficiently act upon reported failures. We propose the design of a hybrid experimental artificial intelligence (AI) and generative AI chatbot HMI that effectively extracts factory equipment conditions that are useful for troubleshooting and predictive maintenance analysis. We achieve these functions by feeding experimental factory-monitored data to the customized chatbot application tool running in its back-end, a Langchain agent linked to the OpenAI GPT-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}3.5 language model (LM) via OpenAI APIs. We design our chatbot front-end with Streamlit, an open-source web app. In the context of I5.0, our chatbot HMI uses personalized natural language, English, to interact with the operator, making the information extraction more understandable. We also integrate the generative AI capability of the GPT 3.5 LM that augments the factory data based on the loaded format to create a larger dataset for additional tasks like machine learning modelling. The experimental results show the accuracy of our customized chatbot HMI when retrieving data based on specific prompts and the advantages of a reduced troubleshooting time compared to operations in traditional factories, which are highly dependent on supervisors’ interventions. Our study provides a valuable example of upgrading standard factory HMIs to I5.0-capable ones by implementing customized AI and generative AI chatbots within operational industrial environments.

Artificial Intelligence-Powered Pulse Sequences in Nuclear Magnetic Resonance and Magnetic Resonance Imaging: Historical Trends, Current Innovations and Perspectives

This review article explores the historical background and recent advances in the application of artificial intelligence (AI) in the development of radiofrequency pulses and pulse sequences in nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI). The introduction of AI into this field, which traces back to the late 1970s, has recently witnessed remarkable progress, leading to the design of specialized frameworks and software solutions such as DeepRF, MRzero, and GENETICS-AI. Through an analysis of literature and case studies, this review tracks the transformation of AI-driven pulse design from initial proof-of-concept studies to comprehensive scientific programs, shedding light on the potential implications for the broader NMR and MRI communities. The fusion of artificial intelligence and magnetic resonance pulse design stands as a promising frontier in spectroscopy and imaging, offering innovative enhancements in data acquisition, analysis, and interpretation across diverse scientific domains.

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON FMCG DISTRIBUTION

Economic Impact-Many examinations have investigated the monetary ramifications of man-made intelligence and ML reception. Research frequently centres around efficiency gains, work market elements, and the potential for work uprooting and creation. A few examinations propose that while Artificial intelligence and ML can prompt expanded effectiveness and development, they may likewise disturb customary business designs, requiring labour force transformation and re-skilling. Business and Industry: Writing in this space looks at how Artificial intelligence and ML advances are changing business tasks, including promoting, finance, store network the board, and client care. Reads up feature the potential for artificial intelligence and ML to improve dynamic cycles, enhance asset designation, customize client encounters, and drive upper hand. Medical care: Artificial intelligence and ML have huge ramifications for medical care, including sickness determination, therapy arranging, drug disclosure, and customized medication. Research in this space investigates the potential for Artificial intelligence and ML to work on clinical results, diminish clinical blunders, lower medical services expenses, and upgrade patient consideration through prescient examination, picture acknowledgment, and normal language handling. Education and Learning: The effect of Artificial intelligence and ML on schooling and learning is likewise a subject of interest. Writing in this field looks at how artificial intelligence fueled devices and stages can work with customized growth opportunities, versatile coaching, robotized reviewing, and instructive substance creation. Research additionally investigates the difficulties and moral contemplations related with man-made intelligence driven instructive innovations. Ethical and social implications-Researchers have raised worries about the moral and cultural ramifications of Artificial intelligence and ML, including issues connected with protection, predisposition, decency, straightforwardness, responsibility, and algorithmic administration. Research in this space looks to foster structures, rules, and guidelines to alleviate likely dangers and guarantee capable man-made intelligence advancement and arrangement. Environmental Impact few examinations explore how simulated intelligence and ML can be utilized to address natural difficulties, for example, environmental change, asset preservation, contamination control, and manageable turn of events. Research in this area investigates uses of computer based intelligence and ML in energy the board, savvy horticulture, natural checking, and preservation endeavours’ lawful and administrative parts of simulated intelligence and ML are likewise a subject of insightful request. Writing in this field looks at licensed innovation privileges, risk issues, information assurance regulations, and the moral and lawful obligations of artificial intelligence designers and clients. Research intends to lay out legitimate systems and rules to successfully administer artificial intelligence and ML advancements.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations. These risks include safety control risks, increased traffic flow, extreme weather events, and movement of tectonic plates. The proposed system is based on the Internet of Things and artificial intelligence identification technology. The monitoring system will cover various aspects of tunnel operations, such as the slope of the entrance, the structural safety of the cave body, toxic and harmful gases that may appear during operation, excessively high and low-temperature humidity, poor illumination, water leakage or road water accumulation caused by extreme weather, combustion and smoke caused by fires, and more. The system will enable comprehensive monitoring and early warning of fire protection systems, accident vehicles, and overheating vehicles. This will effectively improve safety during tunnel operation.

Manifestations of xenophobia in AI systems

AbstractXenophobia is one of the key drivers of marginalisation, discrimination, and conflict, yet many prominent machine learning fairness frameworks fail to comprehensively measure or mitigate the resulting xenophobic harms. Here we aim to bridge this conceptual gap and help facilitate safe and ethical design of artificial intelligence (AI) solutions. We ground our analysis of the impact of xenophobia by first identifying distinct types of xenophobic harms, and then applying this framework across a number of prominent AI application domains, reviewing the potential interplay between AI and xenophobia on social media and recommendation systems, healthcare, immigration, employment, as well as biases in large pre-trained models. These help inform our recommendations towards an inclusive, xenophilic design of future AI systems.

From service design thinking to the third generation of activity theory: a new model for designing AI-based decision-support systems.

The rise of Artificial Intelligence (AI), particularly machine learning, has brought a significant transformation in decision-making (DM) processes within organizations, with AI gradually assuming responsibilities that were traditionally performed by humans. However, as shown by recent findings, the acceptance of AI-based solutions in DM remains a concern as individuals still strongly prefer human intervention. This resistance can be attributed to psychological factors and other trust-related issues. To address these challenges, recent studies show that practical guidelines for user-centered design of AI are needed to promote justified trust in AI-based systems. To this aim, our study bridges Service Design Thinking and the third generation of Activity Theory to create a model which serves as a set of practical guidelines for the user centered design of Multi-Actor AI-based DSS. This model is created through the qualitative study of human activity as a unit of analysis. Nevertheless, it holds the potential for further enhancement through the application of quantitative methods to explore its diverse dimensions more extensively. As an illustrative example, we used a case study in the field of human capital investments, with a particular focus on organizational development, which involves managers, professionals, coaches and other significant actors. As a result, the qualitative methodology employed in our study can be characterized as a "pre-quantitative" investigation. This framework aims at locating the contribution of AI in complex human activity and identifying the potential role of quantitative data in it.

Predicting Stock Market Trends Employing Machine Learning: A Survey

The stock prices depend both on time as well as associated variables and finding patterns in among the variables aid forecasting future stock prices which is often termed as stock market forecasting. Stock market prediction extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. For real life applications utilizing stock market data, it is necessary to predict stock market data with low errors and high accuracy. This needs design of appropriate artificial intelligence (AI) and machine learning (ML) based techniques which can analyze large and complex data sets pertaining to stock markets and forecast future prices and trends in stock prices with relatively high accuracy. This paper presents a comprehensive review on the various techniques used in recent contemporary papers for stock market forecasting. Keywords: Time Series Models, Stock Market Forecasting, Artificial Intelligence, Artificial Neural Networks, Forecasting accuracy.

An artificial intelligent well trajectory design method combining both geological and engineering objectives

Conventional directional well trajectory is designed to connect the wellhead and target point or section with multiple engineering objectives including deflecting capacity, torsional strength, total length, etc. With the growing need of directional wells for complex geological conditions, only the target point or section provided by geologists and reservoir engineers may not be satisfied as the geological objective for well trajectory design. The whole geological profile along the well trajectory can be taken as the geological objective instead of the target point or section. Furthermore, the state-of-the-art of the artificial intelligence algorithm can be applied automatically evaluate the geological profile and form a new geology objective for well trajectory design. In this study, an algorithm is proposed to evaluate the geological profile and a new index, formation evaluation score (FES), is obtained as the geological target for well trajectory design together with conventional multiple engineering objectives. With the combined multiple geological and engineering target, a directional well trajectory is optimized. The results show that the hydrocarbon formation contact effectively increases under comparable engineering conditions, which confirms the benefit of the proposed method in this work. In addition, the proposed method can globally optimize well trajectory with the help of the geological profile, as opposed to locally satisfy the target point or section. The automation of geological formation analysis can also reduce the workload for geologist and reservoir engineers. The outcome of this work may pave the way to comprehensive combine the geological and engineering objectives for directional well design under higher automation levels.

Defining acceptable data collection and reuse standards for queer artificial intelligence research in mental health: protocol for the online PARQAIR-MH Delphi study

IntroductionFor artificial intelligence (AI) to help improve mental healthcare, the design of data-driven technologies needs to be fair, safe, and inclusive. Participatory design can play a critical role in empowering...