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 introduce a pioneering method for urban economic competitiveness analysis in China, addressing the limitations of traditional approaches. Leveraging convolutional neural networks (CNN) and a rich urban feature dataset, augmented using deep convolutional Generative Adversarial Networks (DCGAN), the model offers a comprehensive understanding of urban development, overcoming data scarcity challenges and outperforming traditional methods.
A recent publication delves into the evolving landscape of utilizing machine learning to simulate the complexities of the human brain. Tracing the historical journey from simplified neural network models to contemporary connectome-driven approaches, the article emphasizes the potential of machine learning in replicating neural activities.
The UK and the US have taken significant steps toward governmental oversight of artificial intelligence (AI), addressing various aspects of AI technology. The US President signed an executive order outlining directives for federal agencies, emphasizing the need for standards, security, and preventing misuse of AI.
Researchers introduced Relay Learning, a novel deep-learning framework designed to ensure the physical isolation of clinical data from external intruders. This secure multi-site deep learning approach, Relay Learning, significantly enhances data privacy and security while demonstrating superior performance in various multi-site clinical settings, setting a new standard for AI-aided medical solutions and cross-site data sharing in the healthcare domain.
This research revealed that art attributed to AI is often devalued in terms of monetary worth, skill, and likability, but these effects can be mitigated when direct comparisons with human-made art are avoided. The findings suggest that AI's role in art, while presenting challenges, may lead to new forms of artistic expression and creativity, similar to the impact of inventions like the camera on the art world.
Researchers introduce LLaVA-Interactive, an innovative multimodal human and artificial intelligence (AI) interaction prototype. This system enables users to engage in multi-turn dialogues and interact through visual and language prompts, offering diverse applications, from content creation to culinary guidance, and inspiring future research in multimodal interactive systems.
Researchers have explored the use of hierarchical generative modeling to mimic human motor control, enabling autonomous task completion in a humanoid robot. Through extensive physics simulations, they demonstrated the feasibility and effectiveness of this approach, showcasing its potential for complex tasks involving locomotion, manipulation, and grasping, even under challenging conditions.
This study presents an innovative system for business purchase prediction that combines Long Short-Term Memory (LSTM) neural networks with Explainable Artificial Intelligence (XAI). The system is designed to predict future purchases in a medical drug company, offering transparent explanations for its predictions, fostering user trust, and providing valuable insights for business decision-making.
This research article underscores the importance of aligning AI outputs with human expectations in decision support systems and introduces the concept of Explainable AI (XAI). A systematic review results in a taxonomy of interaction patterns, emphasizing the need for more interactive functionality in AI systems.
This research paper compared various computational models to predict ground vibration from mining blasts. The study found that a blackhole-optimized LSTM model provided the highest predictive accuracy, outperforming conventional and advanced methods, offering a robust foundation for AI-powered solutions in vibration forecasting and design optimization in the mining industry.
Researchers outlined six principles for the ethical use of AI and machine learning in Earth and environmental sciences. These principles emphasize transparency, intentionality, risk mitigation, inclusivity, outreach, and ongoing commitment. The study also highlights the importance of addressing biases, data disparities, and the need for transparency initiatives like explainable AI (XAI) to ensure responsible and equitable AI-driven research in these fields.
Researchers present a detailed case study on the integration of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) for inspecting residential buildings. The study outlines a four-step inspection process, including preliminary preparations, data acquisition, AI defect detection, and 3D reconstruction with defect extraction, and provides insights into challenges, lessons learned, and future prospects for AI-UAV-based building inspections.
This paper explores the integration of IoT with drone technology to enhance data communication and security across various industries, including agriculture and smart cities. The study focuses on the use of machine learning and deep learning techniques to detect cyberattacks within drone networks and presents a comprehensive framework for intrusion detection.
This article explores the impact of industrial robot adoption on corporate green innovation in China. The study uses data from Chinese manufacturing companies and analyzes the role of industrial robots in improving green innovation by promoting environmental management and enhancing productivity.
This article explores the challenges and approaches to imparting human values and ethical decision-making in AI systems, with a focus on large language models like ChatGPT. It discusses techniques such as supervised fine-tuning, auxiliary models, and reinforcement learning from human feedback to imbue AI systems with desired moral stances, emphasizing the need for interdisciplinary perspectives from fields like cognitive science to align AI with human ethics.
Researchers highlight the increasing role of artificial intelligence (AI) in biodiversity preservation and monitoring. AI is shown to be a powerful tool for efficiently processing vast datasets, identifying species through audio recordings, and enhancing conservation efforts, though concerns about its environmental impact must be addressed.
This paper delves into the extensive use of artificial intelligence (AI) models for assessing food security indicators across the globe, with a notable focus on sub-Saharan Africa. The study emphasizes the importance of stakeholder involvement in AI modeling for food security, highlighting three key approaches to integrating AI into food security research.
Researchers leveraged artificial intelligence, including machine learning and natural language processing, to analyze legal documents and predict intimate partner femicide, showcasing the potential for AI to enhance crime prevention and detection in this specific context.
Researchers delve into the realm of mobile robot path planning. Covering single-agent and multi-agent scenarios, the study explores environmental modeling, path planning algorithms, and the latest advancements in artificial intelligence for optimizing navigation. It also introduces open-source map datasets and evaluation metrics.
Researchers have introduced a cutting-edge Driver Monitoring System (DMS) that employs facial landmark estimation to monitor and recognize driver behavior in real-time. The system, using an infrared (IR) camera, efficiently detects inattention through head pose analysis and identifies drowsiness through eye-closure recognition, contributing to improved driver safety and accident prevention.
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