Artificial intelligence

  Large Language Models (LLMs) have revolutionized natural language processing, enabling AI systems to perform a wide range of tasks with remarkable proficiency. However, researchers face significant challenges in optimizing LLM performance, particularly in human-LLM interactions. A critical observation reveals that the quality of LLM responses tends to improve with repeated prompting and user feedback….

  Monocular depth estimation (MDE) plays an important role in various applications, including image and video editing, scene reconstruction, novel view synthesis, and robotic navigation. However, this task poses significant challenges due to the inherent scale distance ambiguity, making it ill-posed. Learning-based methods should utilize robust semantic knowledge to achieve accurate results and overcome this…

  Input space mode connectivity in deep neural networks builds upon research on excessive input invariance, blind spots, and connectivity between inputs yielding similar outputs. The phenomenon exists generally, even in untrained networks, as evidenced by empirical and theoretical findings. This research expands the scope of input space connectivity beyond out-of-distribution samples, considering all possible…

  Governments and humanitarian organizations need reliable data on building and infrastructure changes over time to manage urbanization, allocate resources, and respond to crises. However, many regions across the Global South need more access to timely and accurate data on buildings, making it difficult to track urban growth and infrastructure development. The absence of this…

  Tips for Effective Feature Selection in Machine Learning Image by Author | Created on Canva When training a machine learning model, you may sometimes work with datasets with a large number of features. However, only a small subset of these features will actually be important for the model to make predictions. Which is why…

  Early attempts in 3D generation focused on single-view reconstruction using category-specific models. Recent advancements utilize pre-trained image and video generators, particularly diffusion models, to enable open-domain generation. Fine-tuning on multi-view datasets improved results, but challenges persisted in generating complex compositions and interactions. Efforts to enhance compositionality in image generative models faced difficulties in transferring…

  Accurately transcribing spoken language into written text is becoming increasingly essential in speech recognition. This technology is crucial for accessibility services, language processing, and clinical assessments. However, the challenge lies in capturing the words and the intricate details of human speech, including pauses, filler words, and other disfluencies. These nuances provide valuable insights into…

  Integrating No-Code AI in Non-Technical Higher Education: Recent developments in ML underscore its ability to drive value across diverse sectors. Nevertheless, incorporating ML into non-technical academic programs, such as those in social sciences, presents challenges due to its usual ties with technical fields like computer science. To overcome this barrier, a case-based approach utilizing…

  Digital Twin (DT) technology is becoming more and more popular as a method that gives Internet of Things (IoT) devices dynamic topology mapping and real-time status updates. However, there are difficulties in deploying DT in industrial IoT networks, especially when significant and dispersed data support is required. This frequently results in the creation of…

  Enhancing Agricultural Resilience through Remote Sensing and AI: Modern agriculture faces significant challenges from climate change, limited water resources, rising production costs, and disruptions like the COVID-19 pandemic. These issues jeopardize the sustainability of food production systems, necessitating innovative solutions to meet the demands of a growing global population. Recent advancements in remote sensing…