There is a need for flexible and efficient adaptation of large language models (LLMs) to various tasks. Existing approaches, such as mixture-of-experts (MoE) and model arithmetic, struggle with requiring substantial tuning data, inflexible model composition, or strong assumptions about how models should be used. These limitations call for a methodology that can adapt LLMs efficiently… →
Artificial Intelligence is evolving significantly, and Large Language Models have shown a remarkable capacity to comprehend human-text inputs. Going beyond simple text to analyzing and generating code, LLMs have shown promising results in software development. However, with increased complexity, providing a quality assessment of the code becomes challenging. This paper aims to present CodeJudge, which… →
Mobile Vehicle-to-Microgrid (V2M) services enable electric vehicles to supply or store energy for localized power grids, enhancing grid stability and flexibility. AI is crucial in optimizing energy distribution, forecasting demand, and managing real-time interactions between vehicles and the microgrid. However, adversarial attacks on AI algorithms can manipulate energy flows, disrupting the balance between vehicles and… →
Widely growing sectors, like Healthcare, logistics, and smart cities, are interconnected on devices that require task reasoning capabilities in the Internet of Things (IoT) systems. This requirement has prompted researchers to find effective ways to integrate real-time data and contextual understanding into Large Language Models (LLMs), which have difficulty interpreting real-world tasks. LLMs process IoT… →
Large Language Models (LLMs) have demonstrated remarkable progress in natural language processing tasks, inspiring researchers to explore similar approaches for text-to-image synthesis. At the same time, diffusion models have become the dominant approach in visual generation. However, the operational differences between the two approaches present a significant challenge in developing a unified methodology for language… →
Current generative AI models face challenges related to robustness, accuracy, efficiency, cost, and handling nuanced human-like responses. There is a need for more scalable and efficient solutions that can deliver precise outputs while being practical for diverse AI applications. Nvidia introduces the Nemotron 70B Model, built to offer a new benchmark in the realm of… →
Photovoltaic energy, which uses solar panels to turn sunlight into electricity, is an important part of the shift to renewable energy. Deep learning-based prediction is critical for optimizing output, anticipating weather fluctuations, and improving solar system efficiency, allowing for more intelligent energy network management. There are numerous techniques for predicting PV power generation. Traditional approaches… →
Machine learning focuses on developing models that can learn from large datasets to improve their predictions and decision-making abilities. One of the core areas of development within machine learning is neural networks, which are especially critical for tasks such as image recognition, language processing, and autonomous decision-making. These models are governed by scaling laws, suggesting… →