Multimodal AI
Multimodal AI can process different types of input—such as text, images, and audio and generate various types of output, not just in the same format as the input.
In simple terms, Multimodal AI refers to machine learning models that can understand and combine information from multiple types of data, or modalities.
Unlike traditional AI models that usually work with just one type of data, multimodal AI uses and combines different kinds of data like text, images, and sound to understand things better and give more accurate results.
For example, if I provide an image of a vehicle, it will describe the vehicle. Or, if I describe a vehicle in words, it can create an image of that vehicle.
Multimodal AI systems can achieve higher accuracy and robustness in tasks such as image recognition, language translation, and speech recognition.
Multimodal artificial intelligence is trained to identify patterns between different types of data inputs.
These systems have three primary elements
- Input module – This part takes in different types of data, like text, images, or sound.
- Fusion module – This part mixes and processes the different data together to understand it better.
- Output module – This part gives the result, which can be different depending on the input.
Multimodal AI use cases
- Improving the performance of self-driving cars
- Developing new medical diagnostic tools
- Improving chatbot and virtual assistant experiences
- Analyzing social media data
Multimodal AI Examples
- Google Gemini
- Vertex AI
- OpenAI’s CLIP
Challenges of Multimodal AI
Multimodal AI faces several challenges. It requires large amounts of data from different sources like text, images, and audio, which can be hard to collect and may not always be available. Aligning these different types of data accurately is also difficult. In addition, multimodal AI systems need a lot of computing power, making them expensive to train and run. Finally, they can sometimes worsen existing problems in generative AI, such as bias or producing incorrect information.
Future of this AI
The future of multimodal AI is very exciting. Right now, most AI tools can only work with one type of data, like just text or just images. But with multimodal AI, tools can understand and create many types of data at once. Big companies like Google, Meta, and Open AI are working on this. New models called “unified models” can handle all these types of data in one system, making things faster and easier. One example is Google’s Gemini. Some experts believe that this kind of AI could one day lead to human-like intelligence, because it can learn and think using many kinds of information, just like people do.
