To overcome these challenges, researchers and practitioners are developing new techniques and architectures for multimodal AI training, such as:
- Multimodal fusion: combining data from different modalities using techniques such as concatenation, attention, and fusion
- Multimodal translation: translating data from one modality to another, such as translating text to speech
- Multimodal embeddings: learning shared representations across different modalities
- Multimodal attention: focusing on specific modalities or features when processing multimodal data
Overall, multimodal AI training has the potential to revolutionize many areas of AI research and application, enabling more accurate, robust, and interpretable decision-making in a wide range of domains.