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 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.
This paper presents a novel approach for automatically counting manatees within a region using deep learning, even when provided with low-quality images. Manatees, being slow-moving aquatic mammals often found in aggregations in shallow waters, pose challenges such as water surface reflections, occlusion, and camouflage.
This paper addresses the safety concerns associated with the increasing use of electric scooters by introducing a comprehensive safety system. The system includes a footrest with a force-sensitive sensor array, a data-collection module, and an accelerometer module to address common causes of accidents, such as overloading and collisions.
The paper addresses concerns about the accuracy of AI-driven chatbots, focusing on large language models (LLMs) like ChatGPT, in providing clinical advice. The researchers propose the Chatbot Assessment Reporting Tool (CHART) as a collaborative effort to establish structured reporting standards, involving a diverse group of stakeholders, from statisticians to patient partners.
This study introduces MetaQA, a groundbreaking data search model that combines artificial intelligence (AI) techniques with metadata search to enhance the discoverability and usability of scientific data, particularly in geospatial contexts. The MetaQA system, employing advanced natural language processing and spatial-temporal search logic, significantly outperforms traditional keyword-based approaches, offering a paradigm shift in scientific data search that can accelerate research across disciplines.
This article delves into the assessment of flood susceptibility in Australian tropical cyclone-prone regions, focusing on the impact of tropical cyclone Debbie in 2017. Researchers employ a Random Forest (RF) machine learning model, optimized by differential evolution, and satellite remote sensing data to create a flood hazard map for the Airlie Beach, Mackay, and Bowen regions in North Queensland.
Researchers introduce a groundbreaking Robotic AI Chemist designed for autonomous synthesis and optimization of catalysts for the oxygen evolution reaction (OER) using Martian meteorites. The study addresses the critical challenge of oxygen production for sustainable Mars exploration through in situ resource utilization, presenting an all-in-one system that combines robotic capabilities with artificial intelligence, outpacing traditional trial-and-error approaches by five orders of magnitude.
Researchers propose viewing large language models (LLMs) in conversational AI through the lens of roleplay and simulation. This metaphorical framework helps avoid anthropomorphic misattributions, enabling nuanced interpretations of LLM behavior and fostering responsible development within ethical constraints.
This study introduces an innovative framework for early plant disease diagnosis, leveraging fog computing, IoT sensor technology, and a novel GWO algorithm. The hybrid approach, incorporating deep learning models like AlexNet and GoogleNet, coupled with modified GWO for feature selection, demonstrates superior performance in plant disease identification.
This study investigates the widespread usage of AI tools, particularly ChatGPT and GPT-4, among German students across disciplines. With nearly two-thirds of students utilizing these tools, the research emphasizes the need for further exploration into usage patterns, perceptions, and potential implications for teaching and learning.
Researchers explored the application of artificial intelligence (AI), specifically long short-term memory (LSTM) and artificial neural networks (ANN), in assessing and predicting surface water quality. The study, conducted on the Ashwini River in Himachal Pradesh, India, showcased the effectiveness of LSTM models in accurate water quality prediction, emphasizing the potential of AI in resource management and environmental protection
The paper published in the journal Electronics explores the crucial role of Artificial Intelligence (AI) and Explainable AI (XAI) in Visual Quality Assurance (VQA) within manufacturing. While AI-based Visual Quality Control (VQC) systems are prevalent in defect detection, the study advocates for broader applications of VQA practices and increased utilization of XAI to enhance transparency and interpretability, ultimately improving decision-making and quality assurance in the industry.
This study introduces a model-independent approach to discern texts written by humans from those generated by AI, such as ChatGPT. Using a redundancy measure based on n-gram usage and Bayesian hypothesis testing, the researchers achieved successful discrimination between human and AI-authored texts, offering a robust solution for authorship attribution challenges in the era of advanced language models.
This study investigates how different robot occupations influence the perception of the robot-human border among participants, considering the effects of age and gender. The findings reveal that service robots, particularly those involved in direct human interactions, require a higher degree of human-like features to be recognized as humans.
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