Understanding Multimodal AI

2025-01-31

Various forms of information surround us in our daily lives. From text and images to audio, data comes in many formats, allowing us to choose the most convenient way to access it. However, from a data processing perspective, challenges arise when attempting to analyse these diverse formats in a unified way. This is where multimodal AI comes into play.

What Is Multimodal AI?

In essence, multimodal intelligence is an artificial intelligence system capable of integrating and processing different types of data, such as text, images, audio, videos, and more. It differs from generative AI, which typically creates new content from a single type of input.

Like other AI models, multisensory AI is trained on predefined datasets and then continues to learn, interpret, and generate results from them.

The functionality of this system can be explained through three key components:

Input Module. Multimodal AI consists of multiple homogeneous neural networks that process specific types of data. These networks form the input module, enabling the system to accept data in various formats. At this stage, the data is converted into numerical representations that are easier for the system to process, capturing relationships and context.
Synthesis Module. This module analyses and integrates the different data types into a unified dataset, creating a cohesive representation.
Output Module. After data processing, the system generates and delivers a response. During the development phase, these outputs are fine-tuned to improve performance and minimize hallucinations, bias, and other inaccuracies.

Several companies have already developed and implemented multimodal AI models:

Google Gemini. A multimodal model combining text and image data. For example, it can write a recipe in text form based on a provided image.
GPT-4 Vision. An updated version where text interacts seamlessly with images.
ImageBind. This model processes six data modalities, including text, images, audio, video, thermal, and depth data.
Google Multimodal Transformer. It generates captions and descriptive video summaries by analysing sound, text, and images.

Why Is Multimodal AI Valuable?

One of the primary advantages of multimodal AI is its ability to offer enhanced reasoning, problem-solving, and generative capabilities for both developers and users. By supporting multiple data modalities and generating results in various formats, these systems deliver higher-quality, more precise, and contextually rich outcomes.

Applications powered by multimodal AI are also more efficient and functional. Additionally, this type of AI demonstrates how advanced technologies can mimic human-like approaches to understanding the environment, creating a sense of realism.

Applications of Multimodal AI

The functionality of multisensory AI is already being enabled in various sectors, while in others, it is seen as a promising tool for the future.

1. Chatbots. By integrating multimodal models, chatbots can respond more effectively to customer queries and provide high-quality solutions. For instance, sending an image can result in text-based recommendations, links, and explanations.

2. Robotics Industry. Multimodal AI plays a vital role in comprehensive environmental identification, relying on the understanding of diverse forms of information. For example, robots process visual data from cameras, auditory data from microphones, and sensory data from sensors.

3. Autonomous Vehicles. Like robotics, multimodal intelligence ensures the seamless operation of autonomous cars by processing visual, auditory, and other environmental data.

Final Word

Multimodal AI offers a transformative experience for both creators of AI-driven tools and systems and end users. The capabilities of multimodal models are reshaping AI’s potential, and the future will see these systems being increasingly adopted across industries.

If you are interested in this topic, we suggest you check our articles:

Sources: BuiltIn, Google Cloud, Splunk, TechTarget