What Is Multimodal AI?

Multimodal AI is the art of artificial intelligence (AI) that collects, understands, and processes information from multiple different modes.

It combines different sources of information, including text, audio, images, and video, to enable richer understanding and interaction.

In this article, I would like to explain the mechanism and features of multimodal AI.

• Those interested in multimodal AI
• Those who want to incorporate AI into their company’s services
• Those who are short of IT personnel within the company

This article is recommended for those who fit this description. By reading it, you will understand what it is and how it is used.

How Multimodal AI Works

Multimodal AI combines and processes multiple data types, including text, audio, images, and video.
For example, it combines and processes these information sources rather than a language model taking text as input and an image model taking images as input.
Integrating input from multiple sources provides an AI system with much more information.
For example, when given a picture of a specific animal, it can process not only the image’s content but also textual information about the animal’s name and characteristics, as well as audio information about the animal’s sound, allowing for more comprehensive and detailed understanding.
To process various types of data, integrating input from multiple sources necessitates the use of specialized algorithms and models.

Multimodal AI, which accepts input from multiple sources, has a wide range of applications.
Examples include image caption generation, which combines natural language processing and image processing; dialogue systems, which combine speech recognition and natural language understanding; and video search, which combines video and text.
These applications will allow for more efficient integration of input from multiple sources, resulting in more advanced information processing and interaction.

Multimodal Features

Complex problem solving of Multimodal AI

One of its defining characteristics is the ability to solve complex problems by integrating input from multiple sources.

For example, multimodal AI is used in the medical field for medical diagnosis and disease prediction by combining various information, such as a patient’s voice, image data, medical records, and symptom descriptions, to diagnose diseases and suggest appropriate treatments.

This enables doctors to make more accurate and faster diagnoses, improving the efficiency and success of patient treatment.

Multimodal AI will also be used in automation technologies such as self-driving cars and in the robotics field.

Complex problem-solving capabilities are essential for vehicles to comprehensively grasp the surrounding situation—audio, images, sensor data, etc.—and drive safely and efficiently.

Multimodal AI can adapt to complex traffic conditions and environmental changes and select the optimal action.

Contextual Understanding

Multimodal AI understands context from multiple sources and processes the information appropriately. This ability allows it to integrate inputs from various sources to address more complex problems.

Multimodal AI combines information from multiple modes to provide deeper understanding and solutions when a single source of information is not enough.

For example, generating image captions by combining images and text requires understanding the content of an image and generating relevant text.

In this case, it is necessary to go beyond simple image recognition and natural language processing to combine multiple sources of information to understand the relationships and meaning between images and text.

Similarly, voice-to-text dialogue systems combine speech recognition and natural language understanding to understand what a user is saying and generate appropriate responses, enabling them to handle complex interactions and tasks.

Interactive Applications

Multimodal AI can also be used in interactive applications, which refers to the ability to integrate input from multiple sources and solve problems through a two-way dialogue with the user.

For example, interactive AI can combine information from different modes, such as voice, images, and text, to enable users to communicate naturally, enabling applications such as voice assistants and conversational robots.

It can also recognize non-verbal information such as user gestures and facial expressions and generate appropriate responses accordingly.

For example, facial expressions and gestures can be analyzed to infer a user’s emotions and intentions and provide more personalized services.

Interactive multimodal AI applications are being used in a variety of fields, including education, entertainment, customer support, and health management.

By enabling closer communication with users, we can provide more effective services and experiences, improving user satisfaction and engagement.

In Summary

Multimodal AI is capable of collecting, understanding, and processing information from multiple different modes, enabling it to achieve a complex understanding and be used for interactive dialogue with users and more complex problem-solving.

Why not give multimodal AI a try?