Generalist multimodal AI: A review of architectures, challenges and opportunities

Key Points

• Multimodal AI encompasses various data types, offering enhanced model generalization capabilities and broader applications.
• Architectures are explored, detailing the integration of diverse data sources, which is crucial for successful multimodal performance.
• Challenges include data integration complexities and algorithmic efficiency, which hinder advanced applications across fields.
• Future opportunities may empower innovations in AI, indicating a need for robust frameworks and adaptive models.