Apple’s New Multimodal AI Model

In this video, code_your_own_AI explores Apple’s newly released machine learning model, 4M-21, which stands for Massively Multimodal Masked Modeling. This model represents a significant step forward in integrating multiple modalities and tasks into a single neural network. The video highlights the capabilities of 4M-21, such as generating images from different inputs like human poses, polygons, and edges, and predicting various outputs like captions, bounding boxes, semantic segmentation, depth information, and more from a single RGB image.

The model’s versatility is demonstrated through examples where it performs tasks like creating realistic images based on input shapes, generating captions, and extracting detailed features from images. The core innovation lies in its ability to handle diverse inputs and outputs using specialized tokenizers for different data types, such as RGB images, text, and human poses. The model employs a combination of Vision Transformer-based tokenizers and traditional word piece tokenizers to process these inputs effectively.

The video also delves into the technical aspects of the model, including its pre-training on a wide array of modalities and the use of discrete tokenization methods. The model’s architecture, which includes separate encoder and decoder components, allows it to handle complex tasks efficiently. The potential applications of this technology are vast, ranging from advanced robotic systems to real-time data stream integration.

The video concludes by emphasizing the open-source nature of the model, available on GitHub, and its potential to revolutionize machine learning by enabling cross-modal retrieval and transfer learning. The presenter also provides a link to a detailed video by the research team behind 4M-21 for further insights.