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 analyzed 3.8 million tweets to uncover how users engage with ChatGPT for tasks like coding and content creation, highlighting its versatile applications. The study underscores ChatGPT's potential to revolutionize business processes and services across multiple domains.
A review in Data & Knowledge Engineering investigates how AI enhances digital twins, highlighting improved functionalities and key research gaps. The integration of these technologies shows promise across various sectors, from healthcare to smart cities.
The Laplacian correlation graph (LOG) significantly improves stock trend prediction by modeling price correlations. Experimental results show superior accuracy and returns, highlighting LOG's potential in real-world investment strategies.
Researchers in a PNAS article emphasized the transformative impact of AI on science and the need for robust oversight to maintain scientific integrity. They proposed principles and the establishment of a Strategic Council to guide the responsible use of AI in research, ensuring transparency, accountability, and equity.
Researchers introduced a novel AI-based, three-tier scheme for identifying and assessing carbon-neutral mobility hubs, focusing on optimal site selection, suitability analysis, and traffic impact evaluation. The study demonstrated significant reductions in vehicle travel and substantial cost savings, promoting sustainable urban transportation planning.
Researchers reviewed the integration of NLP in software requirements engineering (SRE) from 1991 to 2023, highlighting advancements in machine learning and deep learning. The study found that AI technologies significantly enhance the accuracy and efficiency of SRE tasks, despite challenges in integrating these technologies into existing workflows.
Researchers employed AI and computational vision techniques to improve pedestrian monitoring in crowded train stations. Utilizing YOLOv7 for object detection and AlphaPose for activity recognition, the study successfully tracked passenger movements and activities, providing critical insights for enhancing station safety and efficiency.
Researchers in Nature Communications introduced SchNet4AIM, a model integrating SchNet for molecule interpretation with local quantum descriptors. This approach accurately predicts atomic charges and interaction energies while maintaining computational efficiency and interpretability, offering insights into complex chemical phenomena.
Researchers compared human and large language model (LLM) performance on theory of mind tasks, finding that while GPT-4 excelled in identifying indirect requests and false beliefs, it struggled with faux pas detection, where LLaMA2 appeared better, though further analysis questioned this advantage.
A recent article in Education Sciences addresses the impact of generative AI on higher education assessments, highlighting academic integrity concerns. Researchers propose the "against, avoid, and adopt" (AAA) principle for assessment redesign to balance AI's potential with maintaining academic standards.
A recent review in the Journal of Materials Research and Technology explores machine learning's transformative potential in designing and optimizing magnesium (Mg) alloys. By leveraging ML, researchers can efficiently enhance Mg alloy properties, expediting their development and broadening industrial applications.
Researchers compare AI's efficiency in extracting ecological data to human review, highlighting speed and accuracy advantages but noting challenges with quantitative information.
Researchers use MLPs in ONIOM schemes to refine drug-protein structures efficiently and accurately, highlighting potential applications in drug development.
Integrating blockchain with the Internet of Drones (IoD) promises enhanced security, connectivity, and efficiency in drone applications like delivery, surveillance, and rescue operations.
Researchers highlight wearable optical sensors as an emerging technology for sweat monitoring. These sensors utilize advancements in materials and structural design to convert sweat chemical data into optical signals, employing methods like colorimetry and SERS to provide non-invasive, continuous health monitoring.
Researchers analyzed the Cambridge Structural Database (CSD) to understand lanthanide coordination chemistry, providing insights for designing better ligands for rare-earth element (REE) separations. The study focused on trends in coordination numbers, first shell distances, and ligand types, which will guide future data-driven ligand design for efficient REE separation.
This study evaluated the proficiency of ChatGPT, Google's Bard, and Anthropic's Claude in answering neurophysiology questions. Despite facing challenges with complex integrative topics, the models performed moderately well overall, highlighting both their potential and the need for targeted training to enhance their capabilities in specialized domains.
This study demonstrated the potential of T5 large language models (LLMs) to translate between drug molecules and their indications, aiming to streamline drug discovery and enhance treatment options. Using datasets from ChEMBL and DrugBank, the research showcased initial success, particularly with larger models, while identifying areas for future improvement to optimize AI's role in medicine.
A recent article in "Artificial Intelligence in Agriculture" reviewed machine learning (ML) techniques for detecting plant diseases in apple, cassava, cotton, and potato crops. The study highlighted the superior accuracy of convolutional neural networks (CNNs) and emphasized ML's potential to enhance crop yield and quality, despite challenges related to data quality and ethical considerations.
Researchers demonstrated a novel approach to structural health monitoring (SHM) in seismic contexts, combining self-sensing concrete beams, vision-based crack assessment, and AI-based prediction models. The study showed that electrical impedance measurements and the AI-based Prophet model significantly improved the accuracy of load and crack predictions, offering a robust solution for real-time SHM and early warning systems.
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