In order to create more accurate predictions, draw insightful conclusions and draw more precise conclusions about real-world problems, multimodal AI combines multiple types or modes of data to create more reliable determinations, conclusions or predictions based on real-world data.
There is a wide range of data types used in multimodal AI systems, including audio, video, speech, images, and text, as well as a range of more traditional numerical data sets. In the case of multimodal AI, a wide variety of data types are used at once to aid artificial intelligence in establishing content and better understanding context, something which was lacking in earlier versions of the technology.
As an alternative to defining Multimodal AI as a type of artificial intelligence (AI) which is capable of processing, understanding, and/or generating outputs for more than one type of data, Multimodal AI can be described as follows. Modality is defined as the way something manifests itself, is perceived, or is expressed. It can also be said to mean the way it exists.
Specifically speaking, modality is a type of data that is used by machine learning (ML) and AI systems in order to perform machine learning functions. Text, images, audio, and video are a few examples of the types of data modalities that may be used.
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