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Understanding Multimodal AI: Benefits, Working, and Applications/ai-insights/understanding-multimodal-ai-benefits-working-and-applications

Understanding Multimodal AI: Benefits, Working, and Applications

April 03, 2024

Understanding Multimodal AI: Benefits, Working, and Applications

You may have heard quite often recently about how Artificial intelligence has been transforming industries across the world. This technology has now become so advanced that it can automate several processes, not just stocking in warehouses but also creating highly effective marketing campaigns with just a single prompt.

However, most of the AI models can work with only a single type of data. For example, the GPT3 is a text-based LLM model i.e., it can help the users with only the processing of text data. You insert text prompts and you get text output, that’s it. Similarly, in other Generative AI models, such as Pictory, you will get output based on the type and data those AI models have been trained for.

But the world of AI is changing. Now, the development and applications of Multimodal AI are on the rise. But what is it actually?

Understanding Multimodal AI

Multimodal AI refers to the AI systems capable of processing multiple types of data or data from multiple modalities including text, speech, images, videos, and sensor data. It helps such AI models to understand the various kinds of information presented to them.

While a customer service chatbot can help customers with text queries only, it might struggle to understand a happy or irate customer using emojis specific to their emotions. But a multimodal AI system can. It can analyze the text, tone of voice, and facial expressions (if the video is included), and provide a more definite output resonating with customer emotions.

According to Markets and Markets, the multimodal AI market is projected to grow up to $4.5 billion by 2028 exhibiting a CAGR of 35% between 2023-2028. This huge growth rate can be attributed to various factors including the wide adoption of such multimodal AI models that offer several benefits.

Let’s have a look at some of them.

Benefits of Multimodal AI systems

  • Improves accuracy and enhances decision-making

    These types of AI models combine information from various sources and therefore, they can achieve higher accuracy in operations like object recognition, sentiment analysis, fraud detection, etc.

  • Better Human-Computer Interaction

    Since they pose very user-friendly interfaces, they help with more natural and intuitive interactions with machines. For example, today’s smartphones can easily understand gestures and voice commands instead of just relying on text-based instructions.

  • Better understanding of the world

    Multimodal AI models can process real-world data from varied sources and gain a better understanding of different types of situations.

How does Multimodal AI work?

AI Professionals have reached greater heights in devising advanced technologies and these kinds of AI systems are true examples of human achievements. Here are the various stages of how they work:

  • Step 1: Data from various sources are gathered, cleaned, formatted, and prepared for processing.

  • Step 2: Each AI model analyzes each type of data, such as Natural Language Processing (NLP) algorithms used for texts, and computer vision for images, to extract relevant features

  • Step 3: The features extracted in the above stage are then combined using different techniques including early fusion, late fusion, etc.

  • Step 4: Finally, based on fused data, the Generative AI model provides output such as classification, prediction, response, and others.

Applications of Multimodal AI

Multimodal AI applications are vast and varied. Here are some examples:

  • Healthcare

    Multimodal Generative AI can be used to analyze medical images and patient records to predict disease outbreaks, or even personalize treatment plans.

  • Customer Service

    Multimodal chatbots can be trained to respond to various types of customer inputs such as text, voice, and sentiment analysis, and provide more efficient and personalized support.

  • Education

    AI applications in education are huge. With multimodal AI, AI tutors can combine speech recognition, facial recognition, student performance data, and other elements to personalize the learning experience.

  • Retail

    Recommendation systems can leverage the power of multimodal AI and analyze customers’ purchase history, browsing behavior, facial recognition, etc. to suggest better.

The most popular and widely used multimodal AI systems

  • Google Gemini
  • GPT-4V
  • Inworld AI
  • Meta ImageBind
  • Runway Gen-2

Challenges of Building and Using Multimodal AI

Though multimodal AI systems offer a ton of features, they are more challenging to create than unimodal AI.

  • Integration of data: since multimodal combines and synchronizes different types of data, it can be challenging because data is collected from various sources and does not have similar formats.
  • Representation of features: it becomes challenging to combine and represent different features in a structured way because each modality has its own characteristics like images requiring CNN and texts requiring LLM feature extraction techniques.
  • Dimensionality and scalability: multimodal AI systems consist of high dimensions having no measures to reduce them. This further enhances its problem to compute, and scale.

Future of Multimodal AI

As AI research advances, multimodal AI is expected to become even more sophisticated. Here are some potential future developments:

  • Advancements in Deep Learning: Improved deep learning models will enable more efficient and accurate processing of multimodal data.
  • Integration with the Internet of Things (IoT): The rise of IoT devices will generate even more data streams for multimodal AI systems to analyze, leading to even richer insights.
  • Wider Adoption Across Industries: Multimodal AI has the potential to revolutionize various sectors, transforming how we interact with machines and experience the world around us.


Multimodal AI systems are still in the growing phase. Proper implementation and wide adoption need to go through various challenges such as complex data, addressing privacy concerns, implementing explainability and bias, and more. However, since their applications can be game-changers in various industries, the future of such models looks quite promising.

As we move towards the future, we can see advancements in deep learning and integration with IoT making them more powerful and purposeful. There’s no doubt as this technology gains adoption, it will have more potential to revolutionize different sectors as we discussed above. So, we can expect more seamless interaction between humans and machines.