Multimodal Evolution
Vision-language models and multimodal capabilities.
Vision-Language Models: Connecting Sight and Language
Vision-language models learn to connect visual perception with language understanding, evolving from contrastive image-text matching (CLIP) to full visual reasoning capabilities integrated into large language models.
Native Multimodal Training
Native multimodal training jointly trains a single model on text, images, audio, and video from the ground up, producing cross-modal understanding that adapter-based approaches cannot achieve.
Audio and Speech Models
Audio and speech capabilities in LLMs evolved from specialized speech recognition systems to native audio understanding and generation, culminating in models that can hold real-time spoken conversations with emotional nuance.
Video Understanding
Video understanding in LLMs extends visual reasoning from static images to temporal sequences, enabling models to comprehend narratives, track objects, and answer questions about events unfolding over minutes to hours.
The Convergence Toward Omni-Models
The AI field is converging from separate specialized models for each modality toward unified “omni-models” that perceive, reason about, and generate text, images, audio, video, and code within a single architecture.