The ongoing advancement in artificial intelligence highlights a persistent challenge: balancing model size, efficiency, and performance. Larger models often deliver superior capabilities but require extensive computational resources, which can limit accessibility and practicality. For organizations and individuals without access to high-end infrastructure, deploying multimodal AI models that process diverse data types, such as text and…
Language models (LMs) are advancing as tools for solving problems and as creators of synthetic data, playing a crucial role in enhancing AI capabilities. Synthetic data complements or replaces traditional manual annotation, offering scalable solutions for training models in domains such as mathematics, coding, and instruction-following. The ability of LMs to generate high-quality datasets ensures…
Vision-Language Models (VLMs) allow machines to understand and reason about the visual world through natural language. These models have applications in image captioning, visual question answering, and multimodal reasoning. However, most models are designed and trained predominantly for high-resource languages, leaving substantial gaps in accessibility and usability for speakers of low-resource languages. This gap highlights…
Since the Industrial Revolution, burning fossil fuels and changes in land use, especially deforestation, have driven the rise in atmospheric carbon dioxide (CO2). While terrestrial vegetation and oceans serve as natural carbon sinks, absorbing some of this CO2, emissions have consistently outpaced their annual capacity. This imbalance has continuously increased atmospheric CO2 concentrations, fueling global…
Google AI Research introduces Gemini 2.0 Flash, the latest iteration of its Gemini AI model. This release focuses on performance improvements, notably a significant increase in speed and expanded multimodal functionality. A key development in Gemini 2.0 Flash is its enhanced processing speed. Google reports that the new model operates at twice the speed of…
LG AI Research has released bilingual models expertizing in English and Korean based on EXAONE 3.5 as open source following the success of its predecessor, EXAONE 3.0. The research team has expanded the EXAONE 3.5 models, including three types designed for specific use cases: The 2.4B model is an ultra-lightweight version optimized for on-device use.…
These days, large language models (LLMs) are getting integrated with multi-agent systems, where multiple intelligent agents collaborate to achieve a unified objective. Multi-agent frameworks are designed to improve problem-solving, enhance decision-making, and optimize the ability of AI systems to address diverse user needs. By distributing responsibilities among agents, these systems ensure better task execution and…
High-resolution, photorealistic image generation presents a multifaceted challenge in text-to-image synthesis, requiring models to achieve intricate scene creation, prompt adherence, and realistic detailing. Among current visual generation methodologies, scalability remains an issue for lowering computational costs and achieving accurate detail reconstructions, especially for the VAR models, which suffer further from quantization errors and suboptimal processing…
Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Modeled after the human brain, ANNs enable machines to learn from data, recognize patterns, and make decisions with remarkable accuracy. This article explores ANNs, from their origins to their functioning, and delves into their types and…
Transformer-based Detection models are gaining popularity due to their one-to-one matching strategy. Unlike familiar many-to-One Detection models like YOLO, which require Non-Maximum Suppression (NMS) to reduce redundancy, DETR models leverage Hungarian Algorithms and multi-head attention to establish a unique mapping between the detected object and ground truth, thus eliminating the need for intermediate NMS. While…