Why is it in the news?
- Multimodal artificial intelligence refers to AI systems that can understand and process multiple types of data inputs, such as images, sounds, videos, and text, allowing users to interact with AI in various ways.
- The move towards multimodal AI represents the next frontier in AI development, aiming to make AI systems more similar to human cognition by incorporating multiple modes of understanding and communication.
- Multimodal AI combines text and images (e.g., OpenAI’s DALL.E and CLIP) or processes audio (e.g., Whisper) alongside text.
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Significance of multimodal AI systems
- Leading AI companies like OpenAI and Google are actively working on multimodal AI systems.
- OpenAI has enabled its GPT-3.5 and GPT-4 models to analyze images and engage in full conversations via speech synthesis.
- OpenAI is actively hiring experts in multimodal AI and working on a new project called Gobi.
- Google’s Gemini, a multimodal large language model, is also in development, leveraging its vast image and video database from its search engine and YouTube.
- Multimodal AI systems can link different types of data during training, allowing them to generate content based on text prompts or perform tasks like automatic image caption generation.
Multimodal AI finds applications in various fields:
- Detecting hateful content on social media (e.g., Meta).
- Predicting dialogue in videos (e.g., Google).
- Integrating sensory data like touch, smell, and temperature for immersive experiences.
- Assisting in autonomous driving and robotics.
- Analyzing medical images and reports (e.g., Google Health AI).
- Speech translation (e.g., Google Translate and Meta’s SeamlessM4T).
- Future multimodal AI systems may incorporate additional sensory data such as touch, smell, and brain signals, enabling more immersive experiences and simulations.