Data & AI

A transformative approach in artificial intelligence (AI), emphasizing their ability to autonomously perceive, plan, adapt, and act in complex environments. These systems represent a significant evolution from traditional AI models by integrating dynamic reasoning, memory management, and goal-oriented behavior. Read More

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 Large Language Models (LLMs) as advanced artificial intelligence systems capable of performing general tasks, such as translation or coding, through simple prompting.  These models have garnered significant attention due to their ability to understand and generate human-like text, marking a notable advancement in AI applications. Read More

In machine learning systems, runtime intelligence refers to the capability of managing and serving features in real-time during model inference. This involves utilizing an online feature store, a low-latency, high-availability data store that provides real-time lookup of features, typically used for machine learning model serving. Read More​

enabling machines to interpret complex visual data. Their work includes multi-image visual question answering (MIQA), the BDD100K autonomous driving dataset, depth sensing for robotics, and Open Long-Tailed Recognition (OLTR) to improve model generalization across diverse data. These innovations support real-world applications like medical imaging, autonomous vehicles, and robotic manipulation. Read More

enables real-time monitoring, debugging, and quality assessment of model behavior in production. It includes tracking metrics like latency, token usage, and response accuracy, while evaluation frameworks test for correctness, safety, and relevance using automated and human-in-the-loop methods. Together, they ensure LLMs are reliable, efficient, and continuously improving. Read More​