Contemporary Artificial Intelligence Research Articles

Augmented or stunted hospitality workers

ABSTRACT Hospitality management scholars are optimistic that digital technologies will augment hospitality workers. They assess the effect of digital technologies depending on the strict separation of workers and digital technologies. Moreover, they mainly study how service robots can work alongside hospitality workers, the anxieties of workers’ replacement by digital technologies, and how digital technologies can augment hospitality workers. Most studies neglect that digital technologies can worsen hospitality work. This study criticises the lure of digital technologies in the field of hospitality management and argues that the use of contemporary artificial intelligence will reduce hospitality workers’ autonomy, skills, and social interactions.

Investigating the rheological characteristics of alkali-activated concrete using contemporary artificial intelligence approaches

Abstract Using artificial intelligence-based tools, this research aims to establish a direct correlation between the alkali-activated concrete (AAC) mix design factors and their performances. More specifically, the machine learning system was fed new property data obtained from AAC mixes used in laboratory experiments. The rheological parameters (yield stress [static/dynamic] and plastic viscosity) of AAC were predicted using the multilayer perceptron neural network (MLPNN) and bagging ensemble (BE) models. In addition, the R 2 values, k-fold analyses, statistical checks, and the dissimilarity between the experimental and predicted compressive strength were employed to assess the performance of the created models. Also, the SHapley additive exPlanation (SHAP) approach was used for examining the relevance of influencing parameters. The BE approach was found to be significantly accurate in all prediction models, with R 2 greater than 0.90, and MLPNN models were found to be moderately precise, with R 2 slightly below 0.90. However, the error assessment through statistical checks and k-fold analysis also validated the higher precision of BE models over the MLPNN models. Building models that can calculate rheological properties of AAC for different values of input parameters could save a lot of time and money compared to doing the tests in a laboratory. In order to ascertain the required amounts of raw materials of AAC, investigators, as well as businesses, may find the SHAP study helpful.

FGeo-TP: A Language Model-Enhanced Solver for Euclidean Geometry Problems

The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proofs has always been a grand challenge to the interdisciplinary field of mathematics and artificial intelligence. This is the fourth article in a series of our works, in our previous work, we established a geometric formalized system known as FormalGeo. Moreover, we annotated approximately 7000 geometric problems, forming the FormalGeo7k dataset. Despite the fact that FGPS (Formal Geometry Problem Solver) can achieve interpretable algebraic equation solving and human-like deductive reasoning, it often experiences timeouts due to the complexity of the search strategy. In this paper, we introduced FGeo-TP (theorem predictor), which utilizes the language model to predict the theorem sequences for solving geometry problems. The encoder and decoder components in the transformer architecture naturally establish a mapping between the sequences and embedding vectors, exhibiting inherent symmetry. We compare the effectiveness of various transformer architectures, such as BART or T5, in theorem prediction, and implement pruning in the search process of FGPS, thereby improving its performance when solving geometry problems. Our results demonstrate a significant increase in the problem-solving rate of the language model-enhanced FGeo-TP on the FormalGeo7k dataset, rising from 39.7% to 80.86%. Furthermore, FGeo-TP exhibits notable reductions in solution times and search steps across problems of varying difficulty levels.

Can Robots Be Smarter than Human Beings?

The rapid advancements in artificial intelligence (AI) and robotics have fuelled speculation about the possibility of machines surpassing human intelligence. This abstract explores the question of whether robots can become smarter than human beings. By analyzing the capabilities of contemporary AI technologies and understanding the fundamental characteristics of human intelligence, we assess the potential for machines to outpace human cognitive abilities. To address this question, we examine the current state of AI development, including machine learning, deep learning, and neural networks. We discuss how these technologies enable robots to process vast amounts of data, learn from it, and make autonomous decisions. Additionally, we explore the concept of "general intelligence" and its significance in comparing human intelligence to AI. While machines excel in narrow domains and specific tasks, achieving true human-like general intelligence remains an ongoing challenge. Furthermore, the abstract delves into the unique aspects of human intelligence that set it apart from current AI capabilities. Human cognition encompasses various complex abilities such as creativity, emotional intelligence, moral reasoning, and common sense understanding, which have proven difficult to replicate in machines. We examine the limitations and challenges faced by AI in these areas and explore the philosophical and ethical implications of creating machines that surpass human cognitive capacities. Ultimately, the abstract concludes that while robots have shown remarkable progress in specific domains, surpassing human intelligence in its entirety is a complex and elusive goal. Human intelligence encompasses a multitude of intricacies that extend beyond pure computational power. However, the abstract acknowledges the potential for future advancements in AI and robotics to challenge the boundaries of human cognitive abilities, warranting continued research and thoughtful consideration of their societal impact.

Visual Questioner using Bert Transformer

The Visual Questioner, an innovative machine learning endeavor, represents a cutting-edge project designed to process images and facilitate user interaction through AI-generated responses to inquiries. Focused on the fusion of visual recognition and natural language understanding, the overarching objective of this project is to cultivate a robust model capable of discerning intricate details within an image and, subsequently, formulating coherent textual responses to user-generated questions. At the core of the Visual Questioner lies the imperative to bridge the semantic gap between visual content and linguistic expression, enhancing the depth of comprehension and interaction within the artificial intelligence paradigm. By harnessing advanced algorithms and neural network architectures, this initiative aspires to elevate the sophistication of image-based question-answering systems, presenting a pivotal advancement in the realm of human-machine communication. This research not only delves into the technical intricacies of image understanding but also underscores the imperative role of natural language generation, thereby contributing substantively to the evolving landscape of AI applications. As we navigate through the complexities of this project, its potential implications span diverse domains, ranging from human-computer interaction to autonomous systems and beyond, underscoring the multifaceted significance of the Visual Questioner in contemporary artificial intelligence research.

APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN THE EARLY DIAGNOSIS OF CHRONIC LOW BACK PAIN

In the last decades, the use of artificial intelligence (AI) has been increasingly investigated in intervertebral disc degeneration (IDD) and chronic low back pain (LBP) research. To date, several AI-based cutting-edge technologies, such as computer vision, computer-assisted diagnosis, decision support system and natural language processing have been utilized to optimize LBP prevention, diagnosis, and treatment. This talk will provide an outline on contemporary AI applications to IDD and LBP research, with a particular attention towards actual knowledge gaps and promising innovative tools.

Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis

The rapid evolution of digital technologies and computer sciences is ushering society into a technologically driven future where machines continually advance to meet human needs and enhance their own intelligence. Among these groundbreaking innovations, Artificial Intelligence (AI) is a cornerstone technology with far-reaching implications. This study undertakes a bibliometric review to investigate contemporary AI and assessment topics in education, aiming to delineate its evolving scope. The Web of Science Databases provided the articles for analysis, spanning from 1994 to September 2023. The study seeks to address research questions about prominent publication years, authors, countries, universities, journals, citation topics, and highly cited articles. The study’s findings illuminate the dynamic nature of AI in educational assessment research, with AI firmly establishing itself as a vital component of education. The study underscores global collaboration, anticipates emerging technologies, and highlights pedagogical implications. Prominent trends emphasize machine learning, Chat GPT, and their application in higher education and medical education, affirming AI's transformative potential. Nevertheless, it is essential to acknowledge the limitations of this study, including data currency and the evolving nature of AI in education. Nonetheless, AI applications are poised to remain a prominent concern in educational technology for the foreseeable future, promising innovative solutions and insights.

Academics' perceptions of ChatGPT-generated written outputs: A practical application of Turing’s Imitation Game

Artificial intelligence (AI) technology, such as Chat Generative Pre-trained Transformer (ChatGPT), is evolving quickly and having a significant impact on the higher education sector. Although the impact of ChatGPT on academic integrity processes is a key concern, little is known about whether academics can reliably recognise texts that have been generated by AI. This qualitative study applies Turing’s Imitation Game to investigate 16 education academics’ perceptions of two pairs of texts written by either ChatGPT or a human. Pairs of texts, written in response to the same task, were used as the stimulus for interviews that probed academics’ perceptions of text authorship and the textual features that were important in their decision-making. Results indicated academics were only able to identify AI-generated texts half of the time, highlighting the sophistication of contemporary generative AI technology. Academics perceived the following categories as important for their decision-making: voice, word usage, structure, task achievement and flow. All five categories of decision-making were variously used to rationalise both accurate and inaccurate decisions about text authorship. The implications of these results are discussed with a particular focus on what strategies can be applied to support academics more effectively as they manage the ongoing challenge of AI in higher education. Implications for practice or policy: Experienced academics may be unable to distinguish between texts written by contemporary generative AI technology and humans. Academics are uncertain about the current capabilities of generative AI and need support in redesigning assessments that succeed in providing robust evidence of student achievement of learning outcomes. Institutions must assess the adequacy of their assessment designs, AI use policies, and AI-related procedures to enhance students’ capacity for effective and ethical use of generative AI technology.

Improving the Effectiveness of E-learning Videos by leveraging Eye-gaze Data

Recent advances in technology strengthen remote and lifelong learning by integrating e-videos into teaching-learning pedagogy. Therefore, educational content developers are tasked with creating engaging and qualitative e-content. The shift in paradigm from offline to online teaching brings forth several issues regarding the quality of online learning materials and the missing dynamic interaction between instructors and learners. Leveraging contemporary artificial intelligence techniques to provide insights into methods for developing quality e-content is the need of the hour. This study showed that the pattern and duration of the eye gaze of the learner on the text, image, or instructor in the video reveal valuable insights, not only regarding the comprehension of the learner but also giving suggestions to improve video lectures. The results show that learners perform better when they spend more time looking at the instructor compared to the image and text on a frame. Therefore, just like classroom teaching, the presence of the instructor in the video is vital, as looking directly at the instructor while they are delivering the lecture encourages comprehension. Furthermore, by applying classification techniques to learner eye gaze data, it was possible to predict with 97% confidence whether the learner would answer the post-quiz correctly or not.

Utilizing Computational Music Analysis and AI for Enhanced Music Composition: Exploring Pre- and Post-Analysis

This research paper investigates the transformative potential of computational music analysis and artificial intelligence (AI) in advancing the field of music composition. Specifically, it explores the synergistic roles of pre-analysis and post-analysis techniques in leveraging AI-driven tools to enhance the creative process and quality of musical compositions. The study encompasses a historical overview of music composition, the evolution of computational music analysis, and contemporary AI applications. It delves into pre-analysis, focusing on its role in informing composition, and post-analysis, which evaluates and augments compositions. The paper underscores the significance of these technologies in fostering creativity while addressing challenges and ethical considerations. Through case studies, evaluations, and discussions, this research offers insights into the profound impact of computational music analysis and AI on music composition, paving the way for innovative and inclusive musical expressions.
 

The “algorithmic as if”: Computational resurrection and the animation of the dead in Deep Nostalgia

Contemporary artificial intelligence and algorithmic processes address deep-seated existential challenges and modes of desire. In so doing, they produce computational systems of imagination, an “algorithmic as if” that enables the expression, transformation, and seeming overcoming of existential limitations via technological means. This article elaborates the character of the “algorithmic as if” by focusing on Deep Nostalgia, an online tool that turns personal photographs of the deceased into looped animations which smile, blink, and move, promising to overcome mortality by technologically “resurrecting the dead.” Performing a close-reading of Deep Nostalgia’s technological processes and the public discourse around its 2021 launch, the article highlights its combination of computational learning, forms of visual representation (photography, video, and animation), and distinctive realignments of temporal experience. Together, these frame the “algorithmic as if” as a magical and affective space for realizing impossible longings that are also reflexive encounters with the “limit-situation” of human mortality.

An Innovative Exploration of Psychological Research Methods Driven by Artificial Intelligence: A Case Study Using Facial Expression Technique to Improve Teenagers’ Self Awareness

Incorporating artificial intelligence (AI) into psychological research can significantly advance AI methodologies and concurrently address inherent biases and inaccuracies in traditional psychological research reliant on manual data collection and analysis. This study introduces an innovative fusion of traditional facial expression recognition, a method aimed at alleviating the self-awareness of teenager students, with contemporary AI facial recognition technology. Through the integrated facial expression recognition system, the project aims to motivate students to identify their emotion, subsequently enhancing their psychological well-being and alleviating self-care.

ChatGPT Isn't Magic

ChatGPT Isn't Magic

Automated diagnosis of pancreatic mucinous and serous cystic neoplasms with modality-fusion deep neural network using multi-modality MRIs.

Pancreatic cystic neoplasms are increasingly diagnosed with the development of medical imaging technology and people's self-care awareness. However, two of their sub-types, serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN), are often misclassified from each other. Because SCN is primarily benign and MCN has a high rate of malignant transformation. Distinguishing SCN and MCN is challenging and essential. MRIs have many different modalities, complete with SCN and MCN diagnosis information. With the help of an artificial intelligence-based algorithm, we aimed to propose a multi-modal hybrid deep learning network that can efficiently diagnose SCN and MCN using multi-modality MRIs. A cross-modal feature fusion structure was innovatively designed, combining features of seven modalities to realize the classification of SCN and MCN. 69 Patients with multi-modalities of MRIs were included, and experiments showed performances of every modality. The proposed method with the optimized settings outperformed all other techniques and human radiologists with high accuracy of 75.07% and an AUC of 82.77%. Besides, the proposed disentanglement method outperformed other fusion methods, and delayed contrast-enhanced T1-weighted MRIs proved most valuable in diagnosing SCN and MCN. Through the use of a contemporary artificial intelligence algorithm, physicians can attain high performance in the complex challenge of diagnosing SCN and MCN, surpassing human radiologists to a significant degree.

Contemporary challenges and AI solutions in port operations: applying Gale–Shapley algorithm to find best matches

Artificial intelligence (AI) developments enable human capability to deliver the same outcome at a lower cost. This research performs a high-level matching between AI solutions and challenges within the port area by developing a novel academic approach. This way, the matching is carried out more structured than when one (manager, developer, challenge owner, etc.) fulfils it based on their opinion without following any structured approach. Therefore, the study defines first a comprehensive typology of port stakeholders' challenges, which can be solved via AI solutions. This typology presents challenges, including their main issues, widespread impact, and potential solutions. A state-of-the-art review of AI solutions that can address these challenges is carried out in parallel. Secondly, this review clearly distinguishes between AI solutions based on their technology and functionality. Thirdly, this research selects an appropriate AI solution for addressing each identified challenge in the port operation by upgrading the Gale–Shapley algorithm. Finally, it shows that the most critical presented AI solutions in this study use various machine learning (ML) techniques. Besides, concerning the AI solution's reusability feature and the result of high-level matching, this research shows that the implementation phase effort can be drastically reduced by using the recently developed matching algorithm.

Knowledge Graphs and Their Applications in Drug Discovery.

Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.

A holistic approach to remote patient monitoring, fueled by ChatGPT and Metaverse technology: The future of nursing education

ChatGPT and Metaverse are contemporary artificial intelligence tools that are increasingly being used in healthcare professional training, particularly for remote patient monitoring. These technologies offer immersive and personalized learning experiences for nurses, improving their skills and confidence in managing remote patient care. ChatGPT can create simulated patient interactions that mimic real-life scenarios, while the Metaverse can provide virtual reality simulations and scenarios for nurses to practice and learn in a safe and controlled environment. The unification of ChatGPT and Metaverse technology in nursing education can enrich the learning experience and equip nurses with the necessary skills for remote patient monitoring, ultimately leading to improved patient outcomes and quality of care.

Air pollution forecasting based on wireless communications: review.

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field.

Artificial intelligence research in hospitality: a state-of-the-art review and future directions

Purpose The purpose of this study is to analyze state-of-the-art knowledge of artificial intelligence (AI) research in hospitality. Design/methodology/approach This study adopts the theory-context-methods framework to systematically review 100 AI-related articles recently published (i.e. from 2021 to April 2023) in three top-tier hospitality journals, namely, the International Journal of Contemporary Hospitality Management, International Journal of Hospitality Management and Journal of Hospitality Marketing and Management. Findings Findings suggest that studies of AI applications in hospitality are mostly theory-driven, whereas most AI methods research adopts a data-driven approach. State-of-the-art AI applications research exhibits the most interest in service robots. In AI methods research, little attention was paid to the amid-service/experience. Research limitations/implications This study reveals inadequacies in theory, context and methods in contemporary AI research. More research from hospitality suppliers’ perspectives and research on generative AI applications are advocated in response to the unveiled research gaps and recent AI developments. Originality/value This study classifies the most recent AI research in hospitality into two main streams – AI applications research and AI methods research – and discusses the gaps in each research stream and latest AI developments. The paper then suggests future research directions to guide researchers in advancing AI research in hospitality.

Thinking Like a Machine

As part of ongoing research bridging ethnomethodology and computer science, in this article we offer an alternate reading of Alan Turing’s 1936 paper, “On Computable Numbers”. Following through Turing’s machinic respecification of computation, we hope to contribute to a deflationary position on AI by showing that the activities attributed to AIs are achieved in the course of methodic hands-on work with computational systems and not in isolation by them. Turing’s major innovation was a demonstration that mathematical and logical operations could be broken down into elementary, mechanically executable operations, devoid of intellectual content. Drawing out lessons from a re-enactment of Turing’s methods as a means of reflecting on contemporary artificial intelligence (AI), including the way those methods disappear into the technology, we will suggest the interesting question raised in “On Computable Numbers” is less about the possibilities of designing machines that “can think” (cf. Turing, 1950), but the practical work we do, and which is made possible, when we ourselves set out to think like machines.