Multimodal AI refers to artificial intelligence systems that can process, understand, and generate information across multiple modes or types of data. In the context of AI, “modes” typically refer to different types of data inputs and outputs, such as text, images, audio, and video. A multimodal AI system can handle more than one of these data types, either separately or in combination.
Here are some key points about Multimodal AI.
Unlike unimodal systems that handle only one type of data (e.g., text-only or image-only), multimodal systems can work with a combination of data types. For instance, it might process both text and images simultaneously.
The Hypercontext is an advanced multimodal AI concept that refers to the ability of handling and integrating both “Human-friendly” and “Machine-friendly” data simultaneously. The integration will encompass various data formats such as images, texts, audio, tabular data, and time series.
This innovative approach aims to extend the context humans have to make decisions by integrating information that humans cannot process in an efficient and precise way. This information amplifies precision and accuracy, particularly in repetitive and data-intensive tasks that require fast and accurate decisions.
A prime example of the Hypercontext’s application would be the understanding of complex and diverse health data, where human-interpretable image data can be combined with large volumes of machine-readable numerical values, thereby revolutionizing the way we approach data-intensive fields.