As artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) become central to innovation across industries, they also bring challenges that cannot be ignored. These workloads demand powerful computing resources, efficient memory management, and well-optimized software to make the most of the hardware. For developers, migrating legacy code to GPU-based frameworks can feel like… →

Large language models have made impressive strides in understanding natural language, solving programming tasks, and tackling reasoning challenges. However, their high computational costs and dependence on large-scale datasets bring their own set of problems. Many of these datasets lack the variety and depth needed for complex reasoning, while issues like data contamination can compromise evaluation… →

Diffusion models are closely linked to imitation learning because they generate samples by gradually refining random noise into meaningful data. This process is guided by behavioral cloning, a common imitation learning approach where the model learns to copy an expert’s actions step by step. For diffusion models, the predefined process transforms noise into a final… →

Drug-induced toxicity is a major challenge in drug development, contributing significantly to the failure of clinical trials. While efficacy issues account for most failures, safety concerns are the second leading cause, at 24%. Toxicities can affect various organ systems, including the heart, liver, kidneys, and lungs, and even approved drugs may face withdrawal due to… →

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… →