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.
Researchers presented a traffic-predicting model, utilizing deep learning techniques, to identify and prevent congestion from large flow sizes (elephant flows) in software-defined networks (SDN). The model, evaluated with an SDN dataset, demonstrated high accuracy in distinguishing elephant flows, and the SHapley Additive exPlanations (SHAP) technique provided detailed insights into feature importance, contributing to potential applications in real-time adaptive traffic management for improved Quality of Service (QoS) in various domains.
Researchers present an innovative approach to dyslexia identification using a multi-source dataset incorporating eye movement, demographic, and non-verbal intelligence data. Experimenting with various AI models, including MLP, RF, GB, and KNN, the study demonstrates the efficacy of a fusion of demographic and fixation data in accurate dyslexia prediction. The insights gained, including the significance of IQ, age, and gender, pave the way for enhanced dyslexia detection, while challenges like data imbalance prompt considerations for future improvements.
The paper explores recent advancements and future applications in robotics and artificial intelligence (AI), emphasizing spatial and visual perception enhancement alongside reasoning. Noteworthy studies include the development of a knowledge distillation framework for improved glioma segmentation, a parallel platform for robotic control, a method for discriminating neutron and gamma-ray pulse shapes, HDRFormer for high dynamic range (HDR) image quality improvement, a unique binocular endoscope calibration algorithm, and a tensor sparse dictionary learning-based dose image reconstruction method.
In this study, the authors delve into psychological factors influencing consumer attitudes toward AI systems. They categorize barriers into opacity, emotionlessness, rigidity, autonomy, and non-humanness, examining how each relates to human cognition. The study emphasizes the need for targeted, context-specific interventions based on system capabilities and user inclinations, providing insights for improving acceptance and mitigating risks in the adoption of AI technologies.
Researchers unveil a groundbreaking virtual reality (VR) system utilizing child avatars for immersive investigative interview training. The AI-driven prototype, featuring a lifelike 6-year-old avatar, outperforms 2D alternatives, showcasing superior realism, engagement, and training efficacy. The system's AI capabilities, including automatic performance evaluation and tailored feedback, present a promising avenue for scalable and personalized training, potentially transforming competencies in handling child abuse cases globally.
Researchers propose a novel deep learning (DL) method utilizing convolutional neural networks (CNNs) for automatic sediment core analysis. The DL-based approach employs semantic segmentation on digital images of sediment cores, demonstrating high accuracy in interpreting sedimentary facies, offering a precise, efficient tool for subsurface stratigraphic modeling in geoscience applications.
This study conducts a systematic literature review to categorize critiques and challenges of the proposed European Artificial Intelligence Act (AIA). As AI governance becomes crucial, the AIA aims to regulate AI development and deployment, considering potential harms. The interdisciplinary Information Systems (IS) field's attention to societal AI dimensions highlights the need for a thorough analysis of the AIA, guiding responsible innovation amidst rapid advancements.
This paper explores the profound impact of artificial intelligence (AI) on art history, showcasing how algorithms decode intricate details in art compositions. The study reveals AI's role in analyzing poses, color palettes, brushwork, and perspectives, contributing to the understanding of artists' use of optical science. Additionally, AI aids in art restoration, uncovering hidden layers, reconstructing missing elements, and disproving theories.
This study investigates the impact of expertise framing on user behavior in distinguishing between human and algorithmic advisors. Experiments on regression tasks revealed a significant increase in advice utilization when human advisors were framed as experts, while a comparable effect was not observed for algorithmic advisors.
This study, published in AISeL, explores the user experience of integrating AI technologies like ChatGPT into knowledge work. Through interviews with 31 users, distinct phases were identified, ranging from pre-use curiosity and anxiety to the establishment of a tight intertwinement with ChatGPT as a collaborative assistant. The findings emphasize the emotional dimensions of AI adoption and raise important considerations for individuals, organizations, and society regarding potential dependencies, deskilling, and the evolving role of AI in the workplace.
The AI-driven transformation of the labor market demands a nuanced approach to skill measurement. Researchers, in a recent AISeL publication, illuminate the dynamic nature of AI skills and propose a novel dynamic co-occurrence method, addressing limitations in static approaches. Leveraging empirical analysis, they reveal the evolving landscape of AI skills and demonstrate the superior performance of the dynamic method in capturing emergence and evolution.
This paper explores the pivotal role of generative AI in providing automated feedback to foster human creativity in innovation. The researchers conducted a series of experiments, utilizing generative AI to offer visual and numeric feedback in real-time. Preliminary insights indicate that visual feedback enhances perceived originality, imagination, and task competence, shedding light on the potential of AI-driven feedback in augmenting creative endeavors.
Researchers delve into the challenges of lifelong learning in AI, proposing specialized hardware accelerators for edge platforms. The study explores intricacies in design, outlines crucial features, and suggests metrics for evaluating these accelerators, emphasizing the co-evolution of models and hardware. The future vision involves reconfigurable architectures, innovative memory designs, and advancements in on-chip communication, calling for a holistic hardware-software co-design approach to enable efficient, adaptable, and robust lifelong learning systems in edge AI.
This study critically evaluates the Cigna StressWaves Test (CSWT), an AI-based tool integrated into Cigna's stress management toolkit, claiming 'clinical grade' assessment. The research, conducted with 60 participants, reveals significant concerns about CSWT's reliability and validity, challenging its efficacy. The study underscores the importance of stringent validation processes for AI-driven health tools, particularly in mental health assessment, and highlights challenges associated with speech-based health measures.
This article presents an ensemble learning approach utilizing convolutional neural networks (CNNs) for precise identification of medicinal plant species based solely on leaf images. The research addresses the challenges of manual identification by taxonomic experts and demonstrates how advanced AI techniques can significantly enhance the efficiency, reliability, and accessibility of plant recognition systems, showcasing potential applications in cataloging and utilizing medicinal plant biodiversity.
This research proposes a novel approach to continual learning in artificial neural networks, addressing the challenge of balancing memory stability and learning plasticity. Inspired by the biological active forgetting mechanism observed in the Drosophila mushroom body’s γMB subset, the study introduces a synaptic expansion-renormalization framework, employing multiple learning modules to actively regulate forgetting.
This article explores the expanding role of artificial intelligence (AI) in scientific research, focusing on its creative ability in hypothesis generation and collaborative efforts with human researchers. AI, particularly large language models (LLMs), aids in proposing hypotheses, identifying blind spots, and collaborating on broad hypotheses, showcasing its potential in various fields like chemistry, biology, and materials science.
This article discusses bioRxiv's collaboration with ScienceCast, an AI startup, to use large language models for multi-level summaries of scientific preprints. While aiming to enhance accessibility, the pilot reveals challenges in accurately summarizing complex technical content, with scientists noting inaccuracies. The future outlook suggests potential benefits as AI capabilities advance, but concerns around precision and the need for a balance between automation and human oversight persist.
DeepMind's GraphCast model, featured in Nature, emerges as a groundbreaking innovation in weather forecasting. Outperforming traditional and AI-based methods, GraphCast provides highly accurate global weather predictions within minutes, showcasing the potential of machine learning to transform and enhance the efficiency of this critical scientific field.
This study examined how people perceive advice from generative AI, exemplified by ChatGPT, on societal and personal challenges. The research, involving 3308 participants, revealed that while AI advisors were perceived as less competent when their identity was transparent, positive experiences mitigated this aversion, highlighting the potential value of clear and understandable AI recommendations for addressing real-world challenges.
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