MMMMDL
MMMMDL, short for Massive Multimodal Deep Learning, is a theoretical framework in artificial intelligence that aims to train models capable of learning from and integrating data across multiple modalities within a single architecture. The central idea is to create unified representations that capture the relationships between text, vision, sound, and other signals, improving performance and generalization across tasks with varying supervision levels. While there is no single official implementation, MMMMDL guides research into scalable, cross-modal modeling.
Core components typically include modality-specific encoders (such as text transformers, image encoders, and audio processors) whose
Training relies on large, diverse multimodal datasets and self-supervised objectives. Common techniques include masked representation modeling,
Applications span natural language understanding with grounded perception, visual question answering, audio-visual scene understanding, and robotics,
Critiques emphasize high computational cost, potential data biases, and issues of interpretability and privacy. Proponents argue