The rapid expansion of data in today’s era has brought with it both possibilities and difficulties. Businesses handle and use this data to their advantage with the help of some techniques. With their own unique architecture, capabilities, and optimum use cases, data warehouses and big data systems are two popular solutions. The differences between data… →

Foundation models (FMs) and large language models (LLMs) are revolutionizing AI applications by enabling tasks such as text summarization, real-time translation, and software development. These technologies have powered the development of autonomous agents that can perform complex decision-making and iterative processes with minimal human intervention. However, as these systems tackle increasingly multifaceted tasks, they require… →

Large Language Models (LLMs) have advanced exponentially since the last decade. However, LLMs still need to improve regarding deployment and utilization, particularly in the areas of computational cost, latency, and output accuracy. This limits the accessibility of LLMs to smaller organizations, degrades the user experience in real-time applications, and risks misinformation or errors in critical… →

Drug discovery is a costly, lengthy process with high failure rates, as only one viable drug typically emerges from a million screened compounds. Advanced high-throughput (HTS) and ultra-high-throughput screening (uHTS) technologies allow rapid testing of large compound libraries, enabling Pharma and Biotech companies to explore more chemical compounds and novel biological targets. Despite these technologies,… →

Machine learning (ML) has revolutionized wireless communication systems, enhancing applications like modulation recognition, resource allocation, and signal detection. However, the growing reliance on ML models has increased the risk of adversarial attacks, which threaten the integrity and reliability of these systems by exploiting model vulnerabilities to manipulate predictions and performance. The increasing complexity of wireless… →

In the evolving field of artificial intelligence, a major challenge has been building models that excel in specific tasks while also being capable of understanding and reasoning across multiple data types, such as text, images, and audio. Traditional large language models have been successful in natural language processing (NLP) tasks, but they often struggle to… →

In today’s increasingly interconnected world, effective communication across languages is essential. However, many natural language processing (NLP) models still struggle with less common languages. This challenge is particularly evident for low-resource languages such as Thai, Mongolian, and Khmer, which lack the data and processing infrastructure available for languages like English or Chinese. Traditional NLP models… →

The development of vision-language models (VLMs) has faced challenges in handling complex visual question-answering tasks. Despite substantial advances in reasoning capabilities by large language models like OpenAI’s GPT-o1, VLMs still struggle with systematic and structured reasoning. Current models often lack the ability to organize information and engage in logical, sequential reasoning, limiting their effectiveness for… →