Multimodal AI

Multimodal AI focuses on creating systems that can understand and integrate information from multiple sources like text, images, audio, and video, aiming to achieve more comprehensive and human-like intelligence. Current research emphasizes developing robust model architectures, such as Mixture-of-Experts (MoE) and transformer-based models, often pre-trained on massive datasets and fine-tuned for specific tasks, including visual question answering and multimodal generation. This field is significant because it pushes the boundaries of AI capabilities, leading to advancements in various applications, from assistive robotics and medical diagnosis to improved search and information retrieval systems. However, challenges remain in addressing biases present in training data and ensuring the reliability and explainability of these complex systems.