Meta-Transformer is an innovative framework developed by the Multimedia Lab at The Chinese University of Hong Kong and the OpenGVLab at Shanghai AI Laboratory, designed to process multiple data modalities using a unified set of parameters. Built on the transformer architecture, it encodes data from various inputs, such as images, text, and audio, into semantic embeddings for diverse tasks. The framework includes components like a data-to-sequence tokenizer, a unified feature encoder, and task-specific heads, enabling efficient multimodal learning. Meta-Transformer demonstrates competitive performance across numerous tasks and datasets, often outperforming existing models, particularly in scenarios like image classification and point cloud understanding, despite using fewer trainable parameters. However, it has limitations in temporal and structural awareness, leading to challenges in tasks requiring such dependencies, and also faces computational overhead issues. The framework represents a significant step towards developing unified multimodal intelligence, highlighting the potential of integrating diverse neural networks to advance AI capabilities in processing and understanding information across different modalities.