What is multimodal AI?

• Multimodal AI is artificial intelligence that combines multiple types, or modes, of data to create more accurate determinations, draw insightful conclusions or make more precise predictions about real-world problems. 
• Multimodal AI systems train with and use video, audio, speech, images, text and a range of traditional numerical data sets to help AI establish content and better interpret context, something missing in earlier AI.

How does multimodal AI differ from other AI?

• The fundamental difference between multimodal AI and traditional single modal AI is the data. 
• A single modal AI is generally designed to work with a single source or type of data. For example, a financial AI uses business financial data, along with broader economic and industrial sector data, to perform analyses, make financial projections or spot potential financial problems for the business. That is, the single modal AI is tailored to a specific task.
• On the other hand, multimodal AI ingests and processes data from multiple sources, including video, images, speech, sound and text, allowing more detailed and nuanced perceptions of the particular environment or situation. 
• In doing this, multimodal AI more closely simulates human perception.

Applications of multimodal AI

• Manufacturing: It can be leveraged to improve quality control, predictive maintenance, and supply chain optimization. By incorporating audio visual data, manufacturers can identify defects in products and optimize manufacturing processes, leading to improved efficiency and reduced waste.

• Language processing: For example, a system identifies signs of stress in a user’s voice and combines that with signs of anger in the user’s facial expression to tailor or temper responses to the user’s needs. Similarly, combining text with the sound of speech can help an AI improve pronunciation and speech in other languages.

• Computer vision: Combining multiple data types helps the AI identify the context of an image and make more accurate determinations. For example, the image of a dog combined with the sounds of a dog are more likely to result in the accurate identification of the object as a dog.
• Agriculture: It can help monitor crop health, predict yields, and optimize farming practices. By integrating satellite imagery, weather data, and soil sensor data, farmers can gain a richer understanding of crop health and optimize irrigation and fertilizer application, resulting in improved crop yields and reduced costs.