Planning and decision-making in complex, partially observed environments is a significant challenge in embodied AI. Traditionally, embodied agents rely on physical exploration to gather more information, which can be time-consuming and impractical, especially in large-scale, dynamic environments. For instance, autonomous driving or navigation in urban settings often demands the agent to make quick decisions based… →
Using large language models (LLMs) has revolutionized artificial intelligence applications, enabling breakthroughs in natural language processing tasks like conversational AI, content generation, and automated code completion. Often with billions of parameters, these models rely on massive memory resources to store intermediate computation states and large key-value caches during inference. These models’ computational intensity and growing… →
Log-based anomaly detection has become essential for improving software system reliability by identifying issues from log data. However, traditional deep learning methods often struggle to interpret the semantic details in log data, typically in natural language. LLMs, like GPT-4 and Llama 3, have shown promise in handling such tasks due to their advanced language comprehension.… →
Effective lesson structuring remains a critical challenge in educational settings, particularly when conversations and tutoring sessions need to address predefined topics or worksheet problems. Educators face the complex task of optimally allocating time across different problems while accommodating diverse student learning needs. This challenge is especially pronounced for novice teachers and those managing large student… →
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,… →