Hasil untuk "artificial intelligence"

R. Luckin, Wayne Holmes

I. Arel, Derek C. Rose, T. Karnowski

D. Mocanu, Elena Mocanu, P. Stone et al.

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős–Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible. Artificial neural networks are artificial intelligence computing methods which are inspired by biological neural networks. Here the authors propose a method to design neural networks as sparse scale-free networks, which leads to a reduction in computational time required for training and inference.

Trevor J. M. Bench-Capon, P. Dunne

N. Nilsson

J. McCarthy

S. Shapiro

C. Hewitt, P. Bishop, R. Steiger

Dominik Dellermann, P. Ebel, Matthias Söllner et al.

Research has a long history of discussing what is superior in predicting certain outcomes: statistical methods or the human brain. This debate has repeatedly been sparked off by the remarkable technological advances in the field of artificial intelligence (AI), such as solving tasks like object and speech recognition, achieving significant improvements in accuracy through deep-learning algorithms (Goodfellow et al. 2016), or combining various methods of computational intelligence, such as fuzzy logic, genetic algorithms, and case-based reasoning (Medsker 2012). One of the implicit promises that underlie these advancements is that machines will 1 day be capable of performing complex tasks or may even supersede humans in performing these tasks. This triggers new heated debates of when machines will ultimately replace humans (McAfee and Brynjolfsson 2017). While previous research has proved that AI performs well in some clearly defined tasks such as playing chess, playing Go or identifying objects on images, it is doubted that the development of an artificial general intelligence (AGI) which is able to solve multiple tasks at the same time can be achieved in the near future (e.g., Russell and Norvig 2016). Moreover, the use of AI to solve complex business problems in organizational contexts occurs scarcely, and applications for AI that solve complex problems remain mainly in laboratory settings instead of being implemented in practice. Since the road to AGI is still a long one, we argue that the most likely paradigm for the division of labor between humans and machines in the next decades is Hybrid Intelligence. This concept aims at using the complementary strengths of human intelligence and AI, so that they can perform better than each of the two could separately (e.g., Kamar 2016).

Keith Frankish, W. Ramsey

Martino Maggetti

Policy makers, scientists, and the public are increasingly confronted with thorny questions about the regulation of artificial intelligence (AI) systems. A key common thread concerns whether AI can be trusted and the factors that can make it more trustworthy in front of stakeholders and users. This is indeed crucial, as the trustworthiness of AI systems is fundamental for both democratic governance and for the development and deployment of AI. This article advances the discussion by arguing that AI systems should also be recognized, as least to some extent, as artifacts capable of exercising a form of agency, thereby enabling them to engage in relationships of trust or distrust with humans. It further examines the implications of these reciprocal trust dynamics for regulators tasked with overseeing AI systems. The article concludes by identifying key tensions and unresolved dilemmas that these dynamics pose for the future of AI regulation and governance.

Yifei Zhang

With the rapid development of artificial intelligence and the Internet of Things technology, the automatic music composition system has become a hot topic of research. This paper presents the TransVAE-Music composition system to achieve efficient multimodal data perception and fusion. Through the introduction of the Internet of Things technology, the system can collect and process audio, video and other data in real time, and improve the diversity and artistry of music generation. At the same time, the Bayesian optimization mechanism is used to finely adjust the hyperparameters in the system to further improve the model performance. Experimental results show that TransVAE-Music has 1.10 and 1.12 reconstruction errors on the POP909 and FMA datasets, respectively, which significantly outperforms other mainstream automatic music generation models. In addition, the model reached 4.8 and 4.9 in perceived quality score (PQS), and 4.4 and 4.5 in user satisfaction score (USS), respectively. These results demonstrate that the proposed system has significant advantages in terms of the accuracy of music generation and the user experience. This study not only provides an effective method for automatic music generation, but also provides important references for future studies on multimodal data fusion and high-quality music generation.

Jing Zhao, Kun Cheng

This study investigates the synchronization of real-time music generation with visual elements in Virtual Reality (VR) environments, leveraging Artificial Intelligence (AI) to create immersive, interactive music experiences for performance, education, and therapy. By leveraging deep learning techniques, specifically Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, the study investigates real-time music generation and synchronization with VR environments. The optimization of music-visual alignment was achieved through Genetic Algorithms (GAs), enhancing the overall coherence and responsiveness of the system. Key findings include significant improvements in user engagement, learning outcomes, and audience satisfaction in educational and performance contexts. The system achieved a high degree of musical coherence, with sequence prediction accuracy of 92.3% and sub-50ms latency, providing a seamless VR music experience. Case studies focused on interactive music education, immersive performance, and personalized music therapy demonstrated the system’s potential in diverse settings, with improvements in user retention, stress reduction, and overall satisfaction. This study highlights the transformative potential of combining VR and AI in music, paving the way for innovative applications in music education, performance, and therapy. The findings offer a foundation for future research and development in immersive music technologies.

Lindai Xie, Yingying Jiang, Chi-Ning Chang et al.

This multi-informant mixed-methods study uses a concurrent parallel sampling approach to investigate undergraduate students' and faculty's perceptions of utilizing AI in teaching and learning at U.S. universities. A survey developed based on the Technology Acceptance Model, Social Influence Theory, and existing literature was implemented to collect undergraduate students' data regarding students' perceived AI learning environment, perceived others' attitudes toward AI, and personal attitudes toward AI. Faculty's opinions were collected through semi-structured interviews in accordance with the survey variables. Quantitative findings indicated that the effect of the AI learning environment on students' personal attitudes toward AI was fully mediated by their perceptions of others' attitudes. This finding highlights the critical role of perceived others' attitudes towards AI since students tend to adapt to the AI learning environment by mirroring the attitudes they perceive from others. The qualitative findings explored faculty's use of AI tools, their attitudes toward AI and students' usage, the challenges they experienced, and the need for clear guidance and support to facilitate better incorporation of AI into their professional practices. The integration of quantitative and qualitative phases compares students' and faculty's usage and attitudes toward AI and brings important insights that focus on improving the AI-using environment, ensuring sufficient financial support, and offering professional training for both faculty and students. Based on the findings, students can be guided in developing informed attitudes about AI utilization through faculty's demonstration of appropriate AI usage, fostering meaningful conversations about AI integration, and experiential learning opportunities to practice AI-assisted learning.

Chia-Jung Liu, Yueh-Chun Liu, Yu-Hsuan Chen et al.

Abstract Objectives Differentiating between nontuberculous mycobacteria (NTM) pulmonary disease (NTM-PD) and colonization (NTM-PC) is clinically important but difficult. It remains unknown whether artificial intelligence utilizing clinical data and chest CT images could address this clinical problem. Materials and methods Patients were retrospectively recruited with NTM isolation from respiratory specimens in two hospitals. Their disease or colonization status was determined by three NTM experts. We developed a multimodal deep learning model named NTMNet, which integrates chest CT scans and clinical data (including age, sex, acid-fast smear [AFS] results, and mycobacterial species) to predict NTM disease status. The performance of NTMNet was evaluated on both internal and external test sets. Results A total of 324 NTM-PC patients and 285 NTM-PD patients were included. Among the internal and external test sets, the area under the receiver operating characteristic curve (AUC) for predicting NTM disease status using CT imaging was 0.73 (95% CI: 0.62–0.82) and 0.78 (95% CI: 0.75–0.83), respectively. When imaging data were integrated with clinical information, our NTMNet model achieved AUC values of 0.85 (95% CI: 0.80–0.93) and 0.82 (95% CI: 0.78–0.89), respectively. Furthermore, our NTMNet model demonstrated comparable accuracy to that of three experienced pulmonologists in determining NTM disease status in the reader study. Conclusion Our multimodal NTMNet exhibited satisfactory performance in distinguishing disease status among patients with respiratory NTM isolates. This deep learning-based model has the potential to assist physicians in clinical management, achieving diagnostic accuracy comparable to that of pulmonologists. Critical relevance statement A deep learning model leveraging chest computed tomography images and clinical data effectively differentiated NTM disease status, achieving a classification accuracy comparable to that of pulmonologists and demonstrating its potential to support accurate NTM diagnosis in clinical settings. Key Points Accurately distinguishing nontuberculous mycobacteria (NTM) disease status is clinically important but challenging. The NTMNet model effectively differentiated the NTM disease status and matched the performance of the pulmonologists. The NTMNet model could be a potential diagnostic tool for patients with respiratory NTM isolates. Graphical Abstract

Abdullah Addas, Abdullah Addas, Muhammad Nasir Khan et al.

IntroductionThe regional disparity in higher education access can only be met when there are strategies for sustainable development and diversification of the economy, as envisioned in Saudi Vision 2030. Currently, 70% of universities are concentrated in the Central and Eastern regions, leaving the Northern and Southern parts of the country with limited opportunities.MethodsThe study created a framework with sensors and generative adversarial networks (GANs) that optimize the distribution of medical universities, supporting equity in access to education and balanced regional development. The research applies an artificial intelligence (AI)-driven framework that combines sensor data with GAN-based models to perform real-time geographic and demographic data analyses on the placement of higher education institutions throughout Saudi Arabia. This framework analyzes multisensory data by examining strategic university placement impacts on regional economies, social mobility, and the environment. Scenario modeling was used to simulate potential outcomes due to changes in university distribution.ResultsThe findings indicated that areas with a higher density of universities experience up to 20% more job opportunities and a higher GDP growth of up to 15%. The GAN-based simulations reveal that redistributive educational institutions in underrepresented regions could decrease environmental impacts by about 30% and enhance access. More specifically, strategic placement in underserved areas is associated with a reduction of approximately 10% in unemployment.DiscussionThe research accentuates the need to include AI and sensor technology to develop educational infrastructures. The proposed framework can be used for continuous monitoring and dynamic adaptation of university strategies to align them with evolving economic and environmental objectives. The study explains the transformative potential of AI-enabled solutions to further equal access to education for sustainable regional development throughout Saudi Arabia.

Banović Jovana M., Radisavljević Ivana M.

The Constitution of the Republic of Serbia safeguards the right to privacy through several aspects, as does the European Convention on Human Rights and Fundamental Freedoms. The cornerstone in this area is the Law on Personal Data Protection from 2018. In line with the ultima ratio principle of criminal law, the Criminal Code protects these data when the most severe violations occur, pursuant to the Criminal Procedure Code. However, with the daily expansion of science, technology, and innovative means of communication and recording, this takes on a different, "digital" dimension. Naturally, this trend calls for certain adjustments in regulations, as well as in their interpretation and application. In this paper, the authors aim to highlight key provisions of the aforementioned regulations and their current and future interpretation within the context of digital society, with a particular focus on criminal law aspects. This complexity is further amplified by the development of artificial intelligence, which inherently relies on the use of vast amounts of data. The aim of this paper is to identify some of the critical elements in the protection of privacy rights, particularly those related to personal data most closely linked to individuals, and to raise the question of potential legislative amendments.

Mohammed N. Alenezi

As digital infrastructure continues to expand, networks, web services, and Internet of Things (IoT) devices become increasingly vulnerable to distributed denial of service (DDoS) attacks. Remarkably, IoT devices have become attracted to DDoS attacks due to their common deployment and limited applied security measures. Therefore, attackers take advantage of the growing number of unsecured IoT devices to reflect massive traffic that overwhelms networks and disrupts necessary services, making protection of IoT devices against DDoS attacks a major concern for organizations and administrators. In this paper, the effectiveness of supervised machine learning (ML) classification and deep learning (DL) algorithms in detecting DDoS attacks on IoT networks was investigated by conducting an extensive analysis of network traffic dataset (legitimate and malicious). The performance of the models and data quality improved when emphasizing the impact of feature selection and data pre-processing approaches. Five machine learning models were evaluated by utilizing the Edge-IIoTset dataset: Random Forest (RF), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and K-Nearest Neighbors (KNN) with multiple K values, and Convolutional Neural Network (CNN). Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.

Seif Hashem Al-Azzam, Mohammad Al-Oudat

Background/purpose. University students in Jordan face numerous challenges that affect their lifestyle on campus and academic performance. The most common challenges can be summarized into two important categories: psychological and academic factors. Psychological factors, such as anxiety levels and daily sleep duration, and academic factors such as GPA and study hours, it is worth mentioning that these phenomena may have related influences on each other and along with such interactions may heighten negative effects. Furthermore, there is no solid research on the topic that can provide solutions to both dimensions in one study. This paper provides a novel analysis-based framework to help target students who face these challenges in the early stages to provide quality service and consultation. Materials/methods. The framework was developed based on a questionnaire that was built based on consultation of psychological and academic expertise to extract features that are related to the important factors. The questionnaire was distributed to 1020 students from several Jordanian universities. The evaluation of data collected through questionnaires included three major sections about demographic, academic, and psychological factors using the SPSS statistical analysis tool to ensure validity and reliability. After that, the Framework categorizes each student's challenges using the Large Language Model (LLM) into academic difficulties, academic and psychological challenges, psychological distress, and normal students. Finally, multiple classifiers are applied to obtain the status of the students. Results. The results show that the collected features from questionnaires work well with all classifiers with high accuracy. The contributions of this study include analyzing both academic and psychological factors and exploring their correlation through a case study conducted in Jordan. Also, using LLM for categorization along with classifiers provides an early intervention for students who suffer from academic, and psychological challenges or both. Conclusion. These findings suggest that early interventions targeting both academic and psychological factors are critical for improving student well-being and academic success, providing valuable insights for university support services.