What is Multimodal AI? How LLMs Process Text, Images, and More
Multimodal AI models have evolved from using modular feature-level fusion, which combines separate models for different data types, to native multimodality, where a shared vector space processes multiple modalities like text, images, and audio simultaneously.
MAIN POINTS FROM TRANSCRIPT
- Multimodal AI involves models that handle multiple data modalities like text, images, and audio.
- Early multimodal systems used modular feature-level fusion, combining separate models for each modality.
- Feature-level fusion may lead to information loss as data is summarized for processing.
- Native multimodality uses a shared vector space for simultaneous processing of all modalities.
TAKEAWAYS
- Multimodal AI has shifted towards native multimodality for more integrated data processing.
- Shared vector spaces allow for seamless integration of various data types in AI models.
- Modular systems remain in use for enterprise tasks due to cost-effectiveness and flexibility.
- Native multimodal AI represents the current gold standard for handling diverse data inputs.