Human Intelligence Research Articles

AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.

Artificial intelligence (AI) is one of the fastest-growing fields in various industries, including engineering, architecture, medical and clinical research, aerospace, and others. AI, which is a combination of machine learning (ML), deep learning (DL), and human intelligence (HI), is revolutionizing drug discovery and development by making it more cost-effective and efficient. It is also being used in fields such as medicinal chemistry, molecular and cell biology, pharmacology, pharmacokinetics, formulation development, and toxicology. AI plays a crucial role in clinical testing by enhancing patient stratification, patient sample evaluation, and trial design, assisting in the identification of biomarkers, determining efficacy criteria, dose selection, trial length, and target patient population selection. The primary objective of this study is to emphasize the importance of AI in clinical trials and drug development, while also exploring the existing challenges and potential advancements in AI within the healthcare industry. A comprehensive literature review was conducted, covering the period from 1998 to 2023. The Science Direct, PubMed, and Google Scholar databases were searched for relevant information. A variety of publications, including Research Gate, Nature, MDPI, and Springer Link, provided pertinent data. This study aimed to gain a deeper understanding of the use of AI in clinical research and drug development, as well as its potential and limitations. We also discuss the benefits and main data limitations of the traditional trial and drug development approach. AI approaches are currently being used to overcome research obstacles and eliminate conceptual or methodological limitations. After discussing possible obstacles and coping mechanisms, we provide several recommendations to help individuals understand the challenges and difficulties associated with clinical research and drug development. It is essential for pharmaceutical companies to have a cutting-edge AI strategy if AI is to become a routine tool for clinical research and drug development.

9.Y.2. Leveraging GenAI and Deep Learning for the Analysis of Public Health Data: A Global Perspective

Abstract The evolution from Artificial Intelligence (AI) to Generative Artificial Intelligence (Gen AI) represents a significant advancement in technological capabilities and applications. Advancements in computing power, algorithmic complexity, and data availability have enabled Gen AI to exhibit broader cognitive abilities such as reasoning, problem-solving, and learning across diverse domains. Private sector investment and innovation is advancing capability in this space at a rapid pace, trending toward a complete paradigm shift towards machines capable of autonomously adapting and learning in dynamic environments, promising profound impacts across industries from education and finance to healthcare and beyond. Yet harnessing the power of GenAI remains out of reach for most public health entities. Questions like “How to start” and “What to do”, let alone concerns regarding cost, security, and ethics, often thwart an agency from endeavoring to even begin their journey to the program improvement they visualize through the lens and promise of AI. Conduent’s longstanding presence and pioneering work in AI across various sectors, has led to a simple, underlying philosophy: we leverage Gen AI technologies to assist and enable humans to improve business processes. Instead of developing Gen AI technologies ourselves, we focus our efforts on making advanced technologies accessible to our clients and enabling them to achieve their outcomes. This session focuses on accessibility and enablement for public health agencies looking to jump start their journey to AI and evolve their public health programs with the use of Gen AI, covering topics such as: • The evolution of GenAI in the private sector; • Visualizing objectives in public health outcomes and improved business processes; • What to do about data; • Using human intelligence to drive machine learning; • Managing security, ethics, and equity; • Roadmap your Journey to Gen AI. For Conduent, Gen AI is not intended to replace humans but to harness its potential to support and empower humans in creating innovative, additive opportunities that enhance business process outcomes. In the public health space, its impact is immense and far reaching. Let’s shift from “How do I start” to tackling the questions that improve programs and their outcomes. So how will you optimize your program with Gen AI? Let’s get started. Key messages • Private sector Gen AI capability is expanding at a rapid pace yet many public health agencies are struggling just to start. Conduent offers an informed perspective to jump start your Gen AI journey. • Accessibility and enablement for public health agencies looking to jump start their journey to AI and evolve their public health programs with the use of GenAI. Speakers/Panelists Mark Marostica Conduent Public Health Solutions, Florham Park, USA Dianna Lydiard Conduent, Florham Park, USA Tarun Khatri Conduent, Burlington, Canada

Sztuczna inteligencja. Konteksty i interpretacje

Artificial intelligence (AI) has been developing dynamically, and becomes a core issue in the public debate. The related contemporary achievements, such as generative language models, change the way we work, especially in creative fields. The article analyses the historical development of AI, from the Dartmouth workshops in 1956, through John McCarthy’s and Alan Turing’s symbolic approach to artificial intelligence, to the effect of cybernetics on contemporary technologies. It draws attention to Richard Dreyfuss’ and John Searle’s critique of AI, emphasising their meaning in redefining the differences between human and artificial intelligence. In the context of the AI revolution, the article poses a question about the future of design and technology, considering if the process of human adaptation keeps up with the development of machines. It also refers to ethical aspects of automation, and the growing importance of creative thinking in the face of technological progress. Keywords: artificial intelligence (AI), cybernetics, philosophy, history, design

Teachers’ Perception Towards Integration of Artificial Intelligence Tutoring-Based System in the School Curriculum: A Survey

Artificial Intelligence (AI) is the theory and development of computer systems capable of performing tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. This approach has been adopted by many organizations, including schools, to facilitate the teaching and learning process. This development has raised varying opinions on the future of employment within all sectors, including the educational sector. The purpose of this research was to evaluate the perception of teachers toward the integration of AI tutoring-based systems into the school curriculum. A descriptive analysis of the quantitative design was used, and 320 teachers were purposely sampled from public secondary schools in Southwest, Nigeria. The data were collected using a questionnaire and analyzed using SPSS. The findings of the study revealed that although teachers in public secondary schools in the southwest of Nigeria are aware of the positive impact of AI tutoring, 77% of the participants have a negative disposition towards the integration of an AI tutoring-based system into the school curriculum. The factors responsible for this high level of negative disposition include age, low level of education, fear of replacement and professional specialties of the teachers. Based on these findings, it was therefore recommended that teachers be sensitized on the importance of AI tutoring and that stakeholders should propagate the message of how it would promote the standard of education in the state. The study also recommended that educational policymakers integrate the use of artificial intelligence into the school curriculum by allocating the required resources to schools and organizing staff training on the use of AI without neglecting a befitting remuneration. Keywords: Artificial Intelligence, Teachers, Learning, Disposition, Perception

Advancing AI Data Ethics in Nursing: Future Directions for Nursing Practice, Research, and Education.

The ethics of artificial intelligence (AI) are increasingly recognized due to concerns such as algorithmic bias, opacity, trust issues, data security, and fairness. Specifically, machine learning algorithms, central to AI technologies, are essential in striving for ethically sound systems that mimic human intelligence. These technologies rely heavily on data, which often remain obscured within complex systems and must be prioritized for ethical collection, processing, and usage. The significance of data ethics in achieving responsible AI was first highlighted in the broader context of health care and subsequently in nursing. This viewpoint explores the principles of data ethics, drawing on relevant frameworks and strategies identified through a formal literature review. These principles apply to real-world and synthetic data in AI and machine-learning contexts. Additionally, the data-centric AI paradigm is briefly examined, emphasizing its focus on data quality and the ethical development of AI solutions that integrate human-centered domain expertise. The ethical considerations specific to nursing are addressed, including 4 recommendations for future directions in nursing practice, research, and education and 2 hypothetical nurse-focused ethical case studies. The primary objectives are to position nurses to actively participate in AI and data ethics, thereby contributing to creating high-quality and relevant data for machine learning applications.

Rak technologiczny w realnie zautomatyzowanym świecie

The article offers a combination of critical philosophical reflection with design science in the context of the implications of popularising generative AI systems. Challenging the validity of comparing AI to human intelligence, the text describes such consequences in reference to the “technological cancer” hypothesis stated by an astrophysicist, Aurélien Barrau, as well as drafts possible ways of fighting such a cancer. Keywords: artificial intelligence (AI), social crisis, environmental crisis, critical design, critical thinking

The associations between gut microbiota and fecal metabolites with intelligence quotient in preschoolers

BackgroundThe awareness of the association between the gut microbiota and human intelligence levels is increasing, but the findings are inconsistent. Furthermore, few research have explored the potential role of gut microbial metabolites in this association. This study aimed to investigate the associations of the gut microbiota and fecal metabolome with intelligence quotient (IQ) in preschoolers.MethodsThe 16 S rRNA sequencing and widely targeted metabolomics were applied to analyze the gut microbiota and fecal metabolites of 150 children aged 3–6 years. The Wechsler Preschool and Primary Scale of Intelligence, Fourth Edition (WPPSI-IV) was used to assess the cognitive competence.ResultsThe observed species index, gut microbiome health index, and microbial dysbiosis index presented significant differences between children with full-scale IQ (FSIQ) below the borderline (G1) and those with average or above-average (all P < 0.05). The abundance of Acinetobacter, Blautia, Faecalibacterium, Prevotella_9, Subdoligranulum, Collinsella, Dialister, Holdemanella, and Methanobrevibacter was significantly associated with preschooler’s WPPSI-IV scores (P < 0.05). In all, 87 differential metabolites were identified, mainly including amino acid and its metabolites, fatty acyl, and benzene and substituted derivatives. The differential fecal metabolites carnitine C20:1-OH, 4-hydroxydebrisoquine, pantothenol, creatine, N,N-bis(2-hydroxyethyl) dodecanamide, FFA(20:5), zerumbone, (R)-(-)-2-phenylpropionic acid, M-toluene acetic acid, trans-cinnamaldehyde, isonicotinic acid, val-arg, traumatin, and 3-methyl-4-hydroxybenzaldehyde were significantly associated with the preschooler’s WPPSI-IV scores (P < 0.05). The combination of Acinetobacter, Isonicotinic acid, and 3-methyl-4-hydroxybenzaldehydenine may demonstrate increased discriminatory power for preschoolers in G1.ConclusionThis study reveals a potential association between gut microbiome and metabolites with IQ in preschoolers, providing new directions for future research and practical applications. However, due to limitations such as the small sample size, unclear causality, and the complexity of metabolites, more validation studies are still needed to further elucidate the mechanisms and stability of these associations.

ChatGPT in Higher Education: A Comprehensive Study of the Literature. Friend or Foe?

This paper presents a qualitative, high-quality State-of-the-Art (SotA) extended literature review on integrating ChatGPT, an advanced and contemporary natural language processing model, a sophisticated AI-powered chatbot that OpenAI developed in higher education. Snyder (2019, p.1) emphasised that "a literature review as a research method is more relevant than ever”. According to Barry et al (2022, p. 663), "the fundamental purpose of SotA literature review is to create a 3-part argument about the state of knowledge for a specific phenomenon: This is where we are now. This is how we got here. This is where we could go next”. This 3-part argument will underpin this study. With the rapid advancement of artificial intelligence (AI) and its potential to transform various sectors, including education, integrating AI-powered tools like ChatGPT to enhance teaching and learning has become a topic of significant interest. This study explores the diverse applications of ChatGPT in higher education, including pedagogical practices, student engagement, intelligent tutoring systems, personalised learning, language learning support, academic research, feedback, grading, and administrative support. Additionally, it identifies the challenges and ethical concerns this technology poses, focusing on bias, privacy, data security, reliability, accountability, accuracy and the balance between human interaction and artificial intelligence. Analysing the literature on ChatGPT’s role in higher education presents future directions for implementing it in higher education settings, ushering in an era of exciting possibilities. The research concludes that higher education institutions should provide clear and transparent guidelines for their staff and students on using ChatGPT responsibly for academic purposes to mitigate any ethical concerns.

A Comprehensive Study on Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies reshaping industries and society. This paper explores the foundational concepts of AI and ML, emphasizing their definitions, methodologies, and applications. AI encompasses a broad spectrum of techniques aimed at mimicking human intelligence, while ML, a subset of AI, focuses on the development of algorithms that enable machines to learn from data. We examine the historical evolution of these fields, the current state of research, and key technologies such as neural networks, natural language processing, and computer vision. The implications of AI and ML are vast, impacting sectors including healthcare, finance, and transportation, and raising ethical considerations regarding privacy, bias, and employment. By analyzing case studies and recent advancements, this paper aims to highlight the potential and challenges of integrating AI and ML into everyday life, paving the way for future research and development in this dynamic domain

The moderating role of emotional intelligence and HR digitalization on the relationship between compensation and employee job performance in Johor manufacturing sector

This research examines the moderating role of emotional intelligence and HR digitalization on the relationship between compensation and employee job performance in Johor manufacturing sector. The manufacturing sector, which has contributed significantly to the Malaysian national GDP, was among the most affected sectors during the remote work movement caused by several Covid-19. One of the most critical aspects was managing human resources (HR) in remote work and the challenges faced by the employees working online. Drawing upon the Theory of Social Exchange and Ability model of Emotional Intelligence, this practical study investigated the influence of compensation (COM) on employee job performance (EJP) with the moderation of human resource digitalization (HRDi) and emotional intelligence (EI). A quantitative methodology based on online and hard-copy questionnaires was adopted. The population of this study consisted of management employees, such as directors, managers, executives, and administrators working in Johor manufacturing companies. A total of 318 responses was used in this study. Utilising SPSS and Smart PLS, the researcher analysed preliminary and pilot studies, data screening and cleaning procedures, exploratory factor analysis, as well as partial least squares structural equation modeling to assess the relationships among variables and test the hypotheses. The research findings demonstrated that compensation directly affects employee job performance. Meanwhile, human resource digitalization moderates the relationships between compensation and job performance of employees. In addition, emotional intelligence moderates the relationships between compensation and employee job performance. Policymakers are urged to encourage HR digitalization implementation and training on enhancing emotional intelligence. Besides, organizations should have plans on how to enhance their implementation of HR digitalization and improve emotional intelligence.

Wittgenstein, Contexts, and Artificial Intelligence An Engineer Among Philosophers, a Philosopher Among Engineers

Could we take Wittgenstein’s philosophy as antagonistic or compatible with AI? Interpretations go in opposite directions. In this paper, I stand with compatibilists and claim that Wittgenstein’s discussion on contexts has deep connections with the early stages of AI at different levels. Furthermore, his remarks on context aids in the comprehension of the recent advancement in machine learning based AI, although they embed a warning against the oversimplyfied association of artificial and human intelligence.

Comparative Analysis of Human and Artificial Intelligence Planning in Production Processes

Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.

Boundary Between Art and Pornography: Differences in Image Identification Between Artificial Intelligence and Human Intelligence

This study examined whether Microsoft Azure and human participants differed significantly in their judgment of controversial image content (e.g., adult and violent content). Significant differences were observed between the Azure- and participant-awarded scores on all four indexes—Adult, Racy, Gore, and Event Description. The artificial intelligence made stricter judgments than did the participants. The introduction of artificial intelligence may facilitate the efficient judgment of images by website administrators. However, the standards upheld by artificial intelligence appear to be unique and stricter than those upheld by people. In the short term, the boundary between art and pornography may remain difficult to identify. Assessing the difference in judgments between artificial and human intelligence should be a topic of continual concern.

Research on the Knowledge Structure and Sustainable Development Pathways of Artificial Intelligence from the Perspective of Technological Science

Achieving significant breakthroughs in both the fundamental theories and technological applications of artificial intelligence is essential for fostering its long-term development. Under the guidance of Professor Qian Xuesen’s theory of technological science, exploring the internal mechanisms of knowledge evolution in artificial intelligence holds profound theoretical and practical significance for promoting sustainable technological advancement. This study draws on literature from the Web of Science (WOS) database and employs methods such as knowledge mapping, natural language processing, clustering analysis, and citation analysis to outline the knowledge structure of the field, clarify the trajectory of sustainable development, and trace the technological genealogy of VR/AR technologies.This study divides the knowledge structure within the field of technological science into “basic theoretical knowledge—applied basic knowledge—applied knowledge”, enriching Qian’s theory of technological science from within and providing strong intellectual support and technological pathways for sustainable technological development in practice. Artificial intelligence encompasses 10 distinct knowledge domains, among which machine learning and deep learning constitute the basic theoretical knowledge, data intelligence, computer vision, and swarm intelligence are the applied basic knowledge, and image processing and human-computer intelligence are the applied knowledge. The development of VR/AR technology has formed two main sustainable development paths: “machine learning—data intelligence—intelligent systems—human computer intelligence”, and “deep learning—computer vision—image processing”.

Awareness About Artificial Intelligence Among Dental Practitioners And Dental Students

BACKGROUND Artificial intelligence (AI) is defined as acquisition of intelligence by computers or machines to perform complex tasks that generally require human intelligence. Nowadays usage of AI in various fields is appreciated because of its time effectiveness and less laborious.In dentistry, AI has been trending, specifically in diagnostic imaging and early detection of diseases,benefiting both dental graduates and practitioners. AIM The main aim is to assess the awareness of Artificial intelligence as an effective tool among dental trainees and dental practitioners. MATERIALS AND METHOD An online cross-sectional survey was conducted among dental trainees and dental practitioners in various institutions. A Google questionnaire form was developed and circulated to assess participant’s awareness about Artificial intelligence in dentistry. Data were collected in Microsoft excel and statistical analysis were performed. RESULTS More than 60% of the participants showed confidence about their familiarity towards AI in dentistry (p&lt;0.01197). Among those, 60.5% answered that they are well aware of ChatGPT and 28% of them to Microsoft Bing (p&lt;0.04395). 53% answered that machine learning is an important form of AI (p&lt;0.00216). Over the advantages of AI, 43% believed AI can improve diagnostics, access to disease screening, cost effectiveness, reduced treatment time (p&lt;0.0001) and 53% agreed that AI can be useful in day-to-day dental practices (p&lt;0.0001). CONCLUSION The present study shows that awareness of AI among the dental fraternity is satisfactory. The field of AI is emerging rapidly and various other new applications in AI are being utilised. AI could act as a valuable tool in supporting clinicians delivering effective dental care and supplementing education for dental trainees. To enhance the future of AI in dentistry, the present curriculum needs to be approachable enough and various hands-on-training are essential. KEYWORDS:artificial intelligence, chatbots, dental caries, radiographic diagnosis, teledentistry.

Contactless Service: Artificial Intelligence Applications of Airports

Airports are an essential part of the airline value chains. Artificial intelligence (AI) technologies are widely used at airports; they utilize two technologies: face detection and recognition. The study examines how passengers' performance expectancy and emotions affect satisfaction and willingness to adopt the AI service of passengers via familiarity. Based on the AI device, it uses acceptance and socio-technical systems perspectives; participants who adopted the AI service in airports in Beijing, Taipei, and Singapore who have the potential to use AI service usage experience more than three times; 387 completed questionnaires. A satisfaction model was analyzed using the partial least squares approach; this study used purposed sampling to collect data based on the passenger volume analysis by those airports, and 387 completed questionnaires were analyzed. With the rise of AI innovation services in airports, passengers can quickly complete boarding procedures through AI innovative services. The research findings point out that passengers' performance expectancy and emotions positively affect familiarity with AI services for passengers. AI innovations provide a service change driver for and have the potential to replace human tasks and intelligence and analytics applications at airports. AI services are a powerful tool for airports to serve their passengers efficiently, and airports will collaborate with airlines to serve passengers with AI.

Students’ Perceptions and Use of AI in MLIS Courses

Artificial intelligence (AI) has been a buzzword on the Internet due to the development of ChatGPT. AI, especially generative AI, can create original text, audio, and images in response to users’ questions and needs. These programs have the potential to enhance students’ learning and creativity if used ethically. Especially for graduate students in the library and information science (LIS), AI can be used to help students outline essays or produce creative content (e.g., videos, slides, visual presentations) that mimic human intelligence and creativity. However, considering this is yet new technology, research needs to be done to understand how students perceive and use AI ethically in their coursework and research in higher education settings. Specifically, this study aims to explore students’ views on the ethical implications of AI in LIS research and class assignments in a course in the Master of Library and Information Studies (MLIS) program at a university and gather students’ perspectives on its responsible or irresponsible utilization. Content analysis of students’ online discussions revealed that students perceived that AI could enhance educational efficiency, foster creativity, and provide technical support. Students found AI helpful in personalized learning experiences, resource access, streamlining tasks, and critical thinking and problem-solving. However, they had concerns about incorrect citation, copyright, plagiarism, bias, misinformation, privacy, and the risk of diminished creativity. This inquiry will contribute to gaining insights into how to help MLIS students prepare with ethical understanding and use of AI for their professional activities.

Unifying Principles of Generalization: Past, Present, and Future.

Generalization, defined as applying limited experiences to novel situations, represents a cornerstone of human intelligence. Our review traces the evolution and continuity of psychological theories of generalization, from its origins in concept learning (categorizing stimuli) and function learning (learning continuous input-output relationships) to domains such as reinforcement learning and latent structure learning. Historically, there have been fierce debates between approaches based on rule-based mechanisms, which rely on explicit hypotheses about environmental structure, and approaches based on similarity-based mechanisms, which leverage comparisons to prior instances. Each approach has unique advantages: Rules support rapid knowledge transfer, while similarity is computationally simple and flexible. Today, these debates have culminated in the development of hybrid models grounded in Bayesian principles, effectively marrying the precision of rules with the flexibility of similarity. The ongoing success of hybrid models not only bridges past dichotomies but also underscores the importance of integrating both rules and similarity for a comprehensive understanding of human generalization.

Professional Judgment and Skepticism Amidst the Interaction of Artificial Intelligence and Human Intelligence

Professional Judgment and Skepticism Amidst the Interaction of Artificial Intelligence and Human Intelligence

AI in plastic surgery: customizing care for each patient

Artificial intelligence (AI) and machine learning (ML) involve the usage of complex algorithms to identify patterns, predict future outcomes, generate new data, and perform other tasks that typically require human intelligence. AI tools have been progressively adopted by multiple disciplines of surgery, enabling increasingly patient-specific care, as well as more precise surgical modeling and assessment. For instance, AI tools such as ChatGPT have been applied to enhance both patient educational materials and patient-surgeon communication. Additionally, AI tools have helped support pre- and postoperative assessment in a diverse set of procedures, including breast reconstructions, facial surgeries, hand surgeries, wound healing operations, and burn surgeries. Further, ML-supported 3D modeling has now been utilized for patient-specific surgical planning and may also be combined with 3D printing technologies to generate patient-customized, implantable constructs. Ultimately, the advent of AI and its intersection with surgical practice have demonstrated immense potential to transform patient care by making multiple facets of the surgical process more efficient, precise, and patient-specific.