기계학습 및 비전 연구실

A superintelligence is a hypothetical agent that possesses intelligence far surpassing that of the brightest and most gifted human minds. This hypothetical ability can also be referred to as Artificial General Intelligence (AGI). There have been many milestones towards such a goal, including GPT-4, ChatGPT, CLIP, and Flamingo. They have shown marvelous performance on diverse tasks compared to task-specific weak AI models, even without specific training. Nowadays, these AGI/foundation models have acquired multi-modality (images, videos, knowledge graphs, etc.), achieving a deeper understanding of the world. Our overarching goal is to develop a general-purpose learning system capable of learning and performing unseen tasks using every modality it can utilize. In modern data analysis, highly structured data frequently occur and they can be viewed as data on non-Euclidean spaces (e.g., graphs, Riemannian manifolds, data manifolds, and functional spaces). We focus on AI for Science, applying graph neural networks (GNNs) to areas such as molecule language models, molecule optimization for drug discovery, and weather forecasting. Our goal is to leverage these advanced techniques to model complex scientific data and drive innovation in scientific research. High-level computer vision enables a deeper understanding of the visual world. Object recognition systems detect objects in images and videos. They offer basic information on whether certain objects are in the scene and how many instances are in the scene. But the information may not be sufficient for building personalized and automated systems for smart city: smart home, smart offices, and hospitals. Without a deep understanding of the interaction between humans and objects, it is hard to understand the context of the scene