Large language models (LLMs) have transformed AI applications, powering tasks like language translation, virtual assistants, and code generation. These models rely on resource-intensive infrastructure, particularly GPUs with high-bandwidth memory, to manage their computational demands. However, delivering high-quality service to numerous users simultaneously introduces significant challenges. Efficiently allocating these limited resources is critical to meet service… →

Large Language Models (LLMs) have demonstrated remarkable potential in performing complex tasks by building intelligent agents. As individuals increasingly engage with the digital world, these models serve as virtual embodied interfaces for a wide range of daily activities. The emerging field of GUI automation aims to develop intelligent agents that can significantly streamline human workflows… →

Computer vision enables machines to analyze & interpret visual data, driving innovation across diverse applications such as autonomous vehicles, medical diagnostics, and industrial automation. Researchers aim to enhance computational models to process complex visual tasks more accurately and efficiently, leveraging techniques like neural networks to handle high-dimensional image data. As tasks become more demanding, striking… →

ReLU stands for Rectified Linear Unit. It is a simple mathematical function widely used in neural networks. The ReLU regression has been widely studied over the past decade. It involves learning a ReLU activation function but is computationally challenging without additional assumptions about the input data distribution. Most studies focus on scenarios where input data… →

Multimodal Large Language Models (MLLMs) have shown impressive capabilities in visual understanding. However, they face significant challenges in fine-grained perception tasks such as object detection, which is critical for applications like autonomous driving and robotic navigation. Current models fail to achieve precise detection, reflected in the low recall rates of even state-of-the-art systems like Qwen2-VL,… →

Generative AI systems transform how humans interact with technology, offering groundbreaking natural language processing and content generation capabilities. However, these systems pose significant risks, particularly in generating unsafe or policy-violating content. Addressing this challenge requires advanced moderation tools that ensure outputs are safe and adhere to ethical guidelines. Such tools must be effective and efficient,… →

Large language models (LLMs) have transformed the landscape of natural language processing, becoming indispensable tools across industries such as healthcare, education, and technology. These models perform complex tasks, including language translation, sentiment analysis, and code generation. However, their exponential growth in scale and adoption has introduced significant computational challenges. Each task often requires fine-tuned versions… →

Founded in 2022, Perplexity AI has quickly emerged as a significant player in artificial intelligence, particularly in AI-driven search technologies. With a strong focus on innovation and user-centric features, the company has introduced groundbreaking advancements while securing notable investments to expand its operations. Recent developments in Perplexity AI’s portfolio highlight its commitment to redefining how… →