Mainstream Artificial Intelligence Research Articles

Applying Artificial Intelligence for Emotional Cognition in Game Testing for Quality Assurance

Artificial intelligence (AI) is a branch of computer science that tries to develop computational tools and systems that can carry out tasks comparable to human decision-making and learning. The subject of AI is expanding quickly, and AI technology is becoming more and more significant in a variety of IT specialties. When more automated and intelligent solutions are employed instead of outdated techniques, the quantity of manpower and resources needed for game testing will be greatly decreased. The aim of this study is to find new models that will make game testing easier by utilising state-of-the-art AI techniques. In an attempt to determine the model base and algorithmic foundation for the new approach, the examination and comparison of existing theories, models, and algorithms is used to infer and identify the viability of certain current mainstream AI models and algorithms in game testing sessions. Standard software testing is not the same as game testing. Further consideration should be given to the whole gaming experience and entertainment value in addition to functional testing. User questionnaires and in-game testing are used in the current standard experience testing methodology. Artificial intelligence eliminates human aspects in emotional cognition. It may produce more objective test results while spending less money for both human and material resources by using massive data sets and its own learning and processing capabilities. This paper examines the state of AI in software testing, game creation, and emotion perception using expertise in using and applying AI approaches in game testing for quality assurance. It also demonstrates how well AI methods work for predicting player emotions during game testing. The AI testing reduces testing expenses related to labour and material resources while guaranteeing total game confidentiality before release. It also increases test accuracy and reduces the impact of subjectivity.

The great detectives: humans versus AI detectors in catching large language model-generated medical writing

BackgroundThe application of artificial intelligence (AI) in academic writing has raised concerns regarding accuracy, ethics, and scientific rigour. Some AI content detectors may not accurately identify AI-generated texts, especially those that have undergone paraphrasing. Therefore, there is a pressing need for efficacious approaches or guidelines to govern AI usage in specific disciplines.ObjectiveOur study aims to compare the accuracy of mainstream AI content detectors and human reviewers in detecting AI-generated rehabilitation-related articles with or without paraphrasing.Study designThis cross-sectional study purposively chose 50 rehabilitation-related articles from four peer-reviewed journals, and then fabricated another 50 articles using ChatGPT. Specifically, ChatGPT was used to generate the introduction, discussion, and conclusion sections based on the original titles, methods, and results. Wordtune was then used to rephrase the ChatGPT-generated articles. Six common AI content detectors (Originality.ai, Turnitin, ZeroGPT, GPTZero, Content at Scale, and GPT-2 Output Detector) were employed to identify AI content for the original, ChatGPT-generated and AI-rephrased articles. Four human reviewers (two student reviewers and two professorial reviewers) were recruited to differentiate between the original articles and AI-rephrased articles, which were expected to be more difficult to detect. They were instructed to give reasons for their judgements.ResultsOriginality.ai correctly detected 100% of ChatGPT-generated and AI-rephrased texts. ZeroGPT accurately detected 96% of ChatGPT-generated and 88% of AI-rephrased articles. The areas under the receiver operating characteristic curve (AUROC) of ZeroGPT were 0.98 for identifying human-written and AI articles. Turnitin showed a 0% misclassification rate for human-written articles, although it only identified 30% of AI-rephrased articles. Professorial reviewers accurately discriminated at least 96% of AI-rephrased articles, but they misclassified 12% of human-written articles as AI-generated. On average, students only identified 76% of AI-rephrased articles. Reviewers identified AI-rephrased articles based on ‘incoherent content’ (34.36%), followed by ‘grammatical errors’ (20.26%), and ‘insufficient evidence’ (16.15%).Conclusions and relevanceThis study directly compared the accuracy of advanced AI detectors and human reviewers in detecting AI-generated medical writing after paraphrasing. Our findings demonstrate that specific detectors and experienced reviewers can accurately identify articles generated by Large Language Models, even after paraphrasing. The rationale employed by our reviewers in their assessments can inform future evaluation strategies for monitoring AI usage in medical education or publications. AI content detectors may be incorporated as an additional screening tool in the peer-review process of academic journals.

Reflective Artificial Intelligence

As artificial intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today’s AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward.

Artificial-Intelligence integrated circuits: Comparison of GPU, FPGA and ASIC

In the recent years, the boom in technology industries has been greatly accelerated by the development of artificial intelligence (AI). AI, which is based on machine learning (ML), can only be developed rapidly because of the continuously increasing computational capacity of AI processors. Compared to general-purpose processors (GPPs), AI processors have specially designed architectures to accelerate the operations of AI applications, such as convolution, matrix, and massive parallel computing. The objectives of this paper are: (1) to illustrate the differences between general-purpose processors and AI processors; (2) to summarise the characteristic three mainstream AI processors: GPU, FPGA and ASIC, and draw a comparison among them. It shows that GPUs provide very competitive performance with high power consumption; FPGAs can offer high efficiency at low cost; and AISCs provide the highest performance with the lowest power consumption, but cost the most.

Establishment and Test Effect of Artificial Intelligence Optimization Model Based on Convolutional Neural Network

Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.

Artificial Intelligence-Based Ethical Hacking for Health Information Systems: Simulation Study.

Health information systems (HISs) are continuously targeted by hackers, who aim to bring down critical health infrastructure. This study was motivated by recent attacks on health care organizations that have resulted in the compromise of sensitive data held in HISs. Existing research on cybersecurity in the health care domain places an imbalanced focus on protecting medical devices and data. There is a lack of a systematic way to investigate how attackers may breach an HIS and access health care records. This study aimed to provide new insights into HIS cybersecurity protection. We propose a systematic, novel, and optimized (artificial intelligence-based) ethical hacking method tailored specifically for HISs, and we compared it with the traditional unoptimized ethical hacking method. This allows researchers and practitioners to identify the points and attack pathways of possible penetration attacks on the HIS more efficiently. In this study, we propose a novel methodological approach to ethical hacking in HISs. We implemented ethical hacking using both optimized and unoptimized methods in an experimental setting. Specifically, we set up an HIS simulation environment by implementing the open-source electronic medical record (OpenEMR) system and followed the National Institute of Standards and Technology's ethical hacking framework to launch the attacks. In the experiment, we launched 50 rounds of attacks using both unoptimized and optimized ethical hacking methods. Ethical hacking was successfully conducted using both optimized and unoptimized methods. The results show that the optimized ethical hacking method outperforms the unoptimized method in terms of average time used, the average success rate of exploit, the number of exploits launched, and the number of successful exploits. We were able to identify the successful attack paths and exploits that are related to remote code execution, cross-site request forgery, improper authentication, vulnerability in the Oracle Business Intelligence Publisher, an elevation of privilege vulnerability (in MediaTek), and remote access backdoor (in the web graphical user interface for the Linux Virtual Server). This research demonstrates systematic ethical hacking against an HIS using optimized and unoptimized methods, together with a set of penetration testing tools to identify exploits and combining them to perform ethical hacking. The findings contribute to the HIS literature, ethical hacking methodology, and mainstream artificial intelligence-based ethical hacking methods because they address some key weaknesses of these research fields. These findings also have great significance for the health care sector, as OpenEMR is widely adopted by health care organizations. Our findings offer novel insights for the protection of HISs and allow researchers to conduct further research in the HIS cybersecurity domain.

Monitoring of functionally graded material during laser directed energy deposition by acoustic emission and optical emission spectroscopy using artificial intelligence

ABSTRACT Laser directed energy deposition (L-DED) allows the production of multi-materials and functionally graded material (FGM) parts. But for commercialisation, process and quality monitoring of parts is required. For the first time, a novel monitoring method for chemical composition and process regimes of FGMs is proposed using a cost-effective acoustic emission (AE) (microphone) and optical emission spectroscopy (OES) sensors. Four chemical compositions (100%Ti, 58%Ti42%Nb, 37%Ti63%Nb, and 100% Nb) and two process parameters (475 W – 1'400 mm/min and 175 W – 2'000 mm/min) were selected, leading to four regimes/quality (conduction mode, partial, minor, medium, and severe lack of fusion pores). The signals were classified using seven mainstream artificial intelligence algorithms. The main conclusions are twofold. First, microphones are unsuitable candidates for monitoring the laser-material interaction during L-DED. The acoustic waves generated by the laser-material interaction are shielded by high gas flow surrounding it and so are either highly disturbed or does not reach the microphone. Conversely, OES are suitable candidates as the classification accuracies are higher than 90% for most category and machine learning algorithms, even after drastic feature reduction. Considering the wide range of chemical composition and quality, our proposed methods using OES have high industrialised potentials for them during L-DED FGM.

Editorial: Introduction to the 2020 Conference on Artificial Life Special Issue

This special issue highlights key selections from the 2020 Conference on Artificial Life, which is the primary international meeting organized yearly by the International Society for Artificial Life (www.alife.org). The conference themes broadly address the synthesis and simulation of living systems, welcoming scientific research that either deepens our understanding of life as we know it or broadens our conception of life as it could be (Langton, 1989).The 2020 conference, hosted by the University of Vermont and the Vermont Complex Systems Center, was originally intended to be held in Montréal, Québec, Canada. However, the global COVID-19 pandemic forced this event, like many others, to be held online. In truth, this challenge afforded a unique opportunity to hold a truly global conference, with 390 registered attendees from around the world.Of 183 submissions, 75 articles (41%) were accepted for full presentations at the conference and published in the proceedings (Bongard et al., 2020). An additional 11 submissions were presented as lighting talks and 24 as posters and were also included in the proceedings.Reflecting the highly interdisciplinary nature of the field, the topics covered in this special issue include evolutionary dynamics, artificial chemistry, agent-based modelling, game theory, genetic programming, neuroevolution, embodiment, and complex systems research: Ghouri, Barnes, and Lewis present a minimal version of the classic river crossing task, isolating the core task of building a bridge in a grid world for the purpose of increasing explainability of the original problem. Results with the minimal environment are consistent with results from the original version, highlighting the utility of the minimal environment for experiments on explainable evolutionary intelligence.Grove, Timbrell, Jolley, Polack, and Borg demonstrate that the mathematical color of noise in an environment has a significant impact on dynamics in evolving populations. In particular, their results call into question whether commonly employed Gaussian or white noise models should be the default.Lexicase selection in genetic programming is an alternative to traditional parent selection. Helmuth and Spector conduct an extensive benchmarking of a variant called down-sampled lexicase selection, showing that it outperforms standard selection, and investigate hypotheses about why it performs so well.Howison, Hugues, and Iida explore how morphology can be used to control and program interactions with the environment in their study of V-shaped falling papers. They also show how Bayesian optimization can be used to design functional constructs of nonliving materials in the real world.Hudcová and Mikolov provide a framework for classifying cellular automata complexity based on transients, which are parts of automata trajectories observed before entering into a loop. In particular, the presented classification is based on the asymptotic growth of the average transient length with increasing grid size. This framework is intended to aid the identification of interesting phenomena in evolving systems.Krellner and Han study the evolution of cooperation among agents playing a donation game. In particular, this work introduces a novel approach to information sharing to solve the problem of private information.Kruszewski and Mikolov develop an artificial chemistry based on combinatory logic, showing that complex structures emerge over time from a simple dynamical system. This work explicitly addresses the origins of open-ended evolution, which is a longstanding pursuit for the field of Artificial Life (and science in general).Miller re-envisions the artificial neuron model to better reflect natural evolution and development processes. Using this model, evolved programs can construct artificial neural networks that can be broken down into smaller networks that each solve distinct tasks.The 2020 conference theme “New frontiers in AI: What can ALife offer AI?” asked the community to consider how the unbridled and sometimes unconventional creativity of Artificial Life research might inspire innovation in mainstream Artificial Intelligence. In fact, the two fields share a deeply intertwined history, as some of the greatest pioneers in early Artificial Intelligence work also (or first) pursued what would now be called Artificial Life. As an example, Shannon (1940), often referred to as the father of information theory, wrote his doctoral dissertation An Algebra for Theoretical Genetics 16 years before he helped found the field of Artificial Intelligence at the Dartmouth Conference.At the same time, the Artificial Life community continues in its own myriad pursuits, recapitulating and reinventing nature often with computational tools, as evidenced by the works contained in this volume. Evolution on Earth gave rise to natural intelligence, and so evolution in silico (a mainstay of Artificial Life research) similarly bears the potential for creating Artificial Intelligence open-endedly; such is the foundational assumption of research on open-ended evolution. What ALife can offer AI is, among other things, an invitation to question what about life and intelligence might transcend substrates and, in doing so, to discover how what is might inspire what will be.

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.

Exploratory Insights on Artificial Intelligence for Government in Europe

There is great interest to use artificial intelligence (AI) technologies to improve government processes and public services. However, the adoption of technologies has often been challenging for public administrations. In this article, the adoption of AI in governmental organizations has been researched as a form of information and communication technologies (ICT)–enabled governance innovation in the public sector. Based on findings from three cases of AI adoption in public sector organizations, this article shows strong similarities between the antecedents identified in previous academic literature and the factors contributing to the use of AI in government. The adoption of AI in government does not solely rely on having high-quality data but is facilitated by numerous environmental, organizational, and other factors that are strictly intertwined among each other. To address the specific nature of AI in government and the complexity of its adoption in the public sector, we thus propose a framework to provide a comprehensive overview of the key factors contributing to the successful adoption of AI systems, going beyond the narrow focus on data, processing power, and algorithm development often highlighted in the mainstream AI literature and policy discourse.

Research on Application of Artificial Intelligence Technology in Electrical Automation Control

Among the evaluation indicators of national development level, the degree of power system development is one of the important indicators. Economic and social needs have gradually expanded the scale of the power grid, and many distributed renewable energy sources are continuously connected to the power grid system, making the power system more and more complex; the data generated during the application of the power system presents typical big data characteristics, such as presenting large, heterogeneous, and multi-source characteristics. Faced with the new characteristics of power data, the requirements for methods of analyzing and processing power data problems have increased. The current research on artificial intelligence (AI) in the computer field is getting deeper and deeper, and the application of AI in various industries has made greater research progress. It is the key to future scientific and technological progress. In my country’s development strategy, it is clearly required to promote the development of AI and regard the power system as one of the main application areas of AI. The rapid development and advancement of artificial intelligence can provide powerful tools in many aspects of the power system, including power system planning and design, coordinated control, simulation, prediction and estimation, diagnosis and identification. This article first analyzes the artificial intelligence technology, introduces the two mainstream artificial intelligence technologies in the current situation, and analyzes the power system, and then studies and analyzes the application of artificial intelligence technology in the electrical system.

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

Artificial Conscious Intelligence

The field of artificial consciousness (AC) has largely developed outside of mainstream artificial intelligence (AI), with separate goals and criteria for success and with only a minimal exchange of ideas. This is unfortunate as the two fields appear to be synergistic. For example, here we consider the question of how concepts developed in AC research might contribute to more effective future AI systems. We first briefly discuss several past hypotheses about the function(s) of human consciousness, and present our own hypothesis that short-term working memory and very rapid learning should be a central concern in such matters. In this context, we then present ideas about how integrating concepts from AC into AI systems to develop an artificial conscious intelligence (ACI) could both produce more effective AI technology and contribute to a deeper scientific understanding of the fundamental nature of consciousness and intelligence.

Elevators Fault Diagnosis Based on Artificial Intelligence

With the rapid development of cities, elevators have gradually been integrated into people’s daily life. Therefore, to reduce the occurrence of elevator failures by using the diagnosis methods of elevator failures is of significance to ensure people’s lives and property. As an emerging field in the 21st century, Artificial Intelligence (AI) techniques provide an effective elevator fault diagnosis technology. This paper describes the various AI algorithms for elevator fault diagnosis from two aspects: theoretical methods and practical applications. Firstly, several mainstream AI algorithms, including the following algorithms: Back Propagation (BP) Neural Network, Radial Basis Function (RBF), K-Means and Support Vector Machine (SVM) are introduced. Then, a broad literature review of the application of these AI algorithms in elevator fault diagnosis is given. Finally, the possible development trend of AI in elevator fault diagnosis in the future is discussed.

Natural Language Translation at the Intersection of AI and HCI

The fields of artificial intelligence (AI) and human-computer interaction (HCI) are influencing each other like never before. Widely used systems such as Google Translate, Facebook Graph Search, and RelateIQ hide the complexity of large-scale AI systems behind intuitive interfaces. But relations were not always so auspicious. The two fields emerged at different points in the history of computer science, with different influences, ambitions, and attendant biases. AI aimed to construct a rival, and perhaps a successor, to the human intellect. Early AI researchers such as McCarthy, Minsky, and Shannon were mathematicians by training, so theorem-proving and formal models were attractive research directions. In contrast, HCI focused more on empirical approaches to usability and human factors, both of which generally aim to make machines more useful to humans. Many of the attendees at the first CHI conference in 1983 were psychologists and engineers. Papers were presented with titles such as "Design principles for human-computer interfaces" and "Psychological issues in the use of icons in command menus," hardly appealing fare for most mainstream AI researchers.

Editorial Reflections and Planning for the Future

The newest editor in chief begins his tenure with IEEE Intelligent Systems by laying out a roadmap for the magazine going forward. Plans for IS's future include connecting with mainstream artificial intelligence communities, incorporating more from AI industry professionals, and increasing the magazine's presence on social media. The Web extra thanks IS's many reviewers for all their hard work in 2012.

Message from Editors-in-Chief

Message from Editors-in-Chief