What is a modality in AI? Give three examples of different modalities.

Modality, in artificial intelligence, refers to a type or format of data that can be taken in as input and processed, or generated as output. Some of the common modalities present in AI include text, image, and audio.

What is multimodal learning?

In the context of artificial intelligence, multimodal learning points to a form of machine learning where a model processes and integrates multiple modalities, and may result in an output consisting of various modalities, too.

Where is multimodal learning used? Can you find one real application and identify the types of information it combines?

Multimodal learning is used for tasks which require an amalgamation of multiple data formats, or modalities, in order to achieve a consistent and accurate output. One significant application of multimodal learning is in healthcare systems, where a wide plethora of modalities are sent in, such as medical scans (image), lab results (text), recordings (video), and voice biomarkers (audio).

What is embodied AI?

Embodied AI is a type of artificial intelligence which uses a physical ‘body’ to interact with its environment. Unlike chatbots which process data through digital servers, embodied AI systems utilise sensors, cameras, and motors to actively take in information from their surroundings.

How are multimodal learning and embodied AI connected?

Embodied AI systems deeply depend on multimodal learning; to move beyond processing mere datasets, an embodied AI model requires the ability to take in various aspects of its environment such as visual, auditory, and sensory components. In order to achieve this, embodied AI uses multimodal learning to simultaneously operate on different processes of varying formats and combine them into a unified, reliable result.

Get to know more about these fascinating aspects of AI through these links!

Modalities and their role in AI
An intricate briefing of Multimodal AI
Embodied AI systems and their significance