Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
This article delves into the intricate relationship between causality and eXplainable Artificial Intelligence (XAI) from three perspectives. It examines the limitations of current XAI, explores how XAI can contribute to causal inquiry, and advocates for the integration of causality to enhance XAI.
AI and robotics practitioners share diverse visions of AI's impact, from utopian coexistence to dystopian conflict. This study, based on 35 interviews, explores three continuums of AI future scenarios and highlights critical questions about agency, societal equality, and power distribution in shaping our AI-driven future.
Researchers have developed an open-source Python tool that integrates explainable artificial intelligence (XAI) with Google Earth Engine to improve land cover mapping and monitoring. The tool provides feature importance metrics and supports land cover classification and change detection workflows, making it a valuable resource for remote sensing applications with transparent machine learning.
Researchers have developed the U-SMR network, a hybrid model combining ResNet and Swin Transformer, to enhance fabric defect detection in the textile industry. The model balances global and local features, significantly improving accuracy and edge detection while achieving competitive performance and generalization.
Researchers have introduced a novel decision support system utilizing fuzzy logic to improve collision avoidance in multi-vessel situations at sea. By integrating artificial intelligence and COLREG rules, the system identifies the most dangerous vessel and calculates collision avoidance maneuvers, demonstrating promise in two-ship scenarios but highlighting the need for further research in high-traffic areas.
A recent review explores the potential of artificial intelligence (AI) in revolutionizing the screening, diagnosis, and monitoring of body iron levels. The review reveals AI's promise in improving the management of iron deficiency and overload, although challenges such as data limitations and ethical concerns must be addressed for its full potential to be realized.
Researchers have introduced an innovative framework that combines system dynamics modeling, risk management, and resiliency concepts to assess the effectiveness of smartphone-based skin lesion screening applications. By analyzing various factors that affect these systems, the study provides valuable insights into improving skin health monitoring and risk management in healthcare, particularly in the context of skin cancer detection and prevention.
This study examines the public's reactions and sentiments towards ChatGPT's role in education through Twitter data analysis. It reveals a complex interplay of positive and negative sentiments, highlighting the need for comprehensive exploration of AI's integration into education and the importance of considering diverse perspectives.
ZairaChem, a groundbreaking AI and machine learning tool, is transforming drug discovery in resource-limited settings. This fully automated framework for quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) modeling accelerates the identification of lead compounds and offers a promising solution for efficient drug discovery.
This paper explores the integration of artificial intelligence (AI) and computer vision (CV) technologies in addressing urban expansion challenges, particularly in optimizing container movement within seaports. Through a systematic review, it highlights the significant role of AI and CV in sustainable parking ecosystems, offering valuable insights for enhancing seaport management and smart city development.
Researchers introduce the e3-skin, a versatile electronic skin created using semisolid extrusion 3D printing. This innovative technology combines various sensors for biomolecular data, vital signs, and behavioral responses, making it a powerful tool for real-time health monitoring. Machine learning enhances its capabilities, particularly in predicting behavioral responses to factors like alcohol consumption.
A study comparing the creativity of AI chatbots and human participants in the Alternate Uses Task (AUT) reveals that chatbots consistently produce creative responses, often surpassing humans. However, the study underscores the unique complexity of human creativity, highlighting that while AI can excel, it still struggles to fully replicate or surpass the best human ideas.
Researchers explore how AI chatbots can improve supply chain sustainability in small and medium manufacturing enterprises (SMEs) in India. The research shows that chatbots enhance supply chain visibility and innovation capability, leading to improved sustainability performance, and offers practical recommendations for SMEs to leverage this technology for sustainable practices.
Researchers have developed a cutting-edge ship detection and tracking model for inland waterways, addressing data scarcity issues. Leveraging few-shot learning and innovative transfer learning techniques, this model achieves remarkable accuracy, promising advancements in maritime safety and monitoring systems.
Researchers introduce an extended Total Product Lifecycle (TPLC) model for AI in healthcare. This model addresses the crucial issue of bias, aiming to achieve health equity by considering equity metrics and mitigation strategies across all phases of AI development and deployment, ultimately improving healthcare outcomes for all.
This study advocates for a closer collaboration between artificial intelligence (AI) and ecological research to address pressing challenges such as climate change. The authors highlight the potential for AI to learn from ecological systems and propose a convergence that can lead to groundbreaking discoveries and more resilient AI systems.
Researchers have harnessed the power of artificial intelligence to forecast oil demand in both exporting and importing nations, providing policymakers and energy stakeholders with precise tools for navigating the complex global oil market landscape. Their study compared AI techniques with traditional statistical models, revealing the superiority of AI in terms of prediction accuracy and stability.
Researchers have harnessed the power of artificial intelligence to predict chloride resistance in concrete compositions, a key factor in enhancing structural durability and preventing corrosion. By leveraging machine learning techniques, they created a reliable model that can forecast chloride migration coefficients, reducing the need for labor-intensive and time-consuming experimentation, and paving the way for more cost-effective and sustainable construction practices.
Researchers reveal that chatbots equipped with empathetic capabilities significantly impact tourists' satisfaction and their intention to visit a destination. Empathy emerged as the most crucial attribute, surpassing informativeness and interactivity, highlighting the importance of emotionally resonant interactions in the tourism sector.
Researchers have developed a novel approach that combines ResNet-based deep learning with Grad-CAM visualization to enhance the accuracy and interpretability of medical text processing. This innovative method provides valuable insights into AI model decision-making processes, making it a promising tool for improving healthcare diagnostics and decision support systems.
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