Introduction- Multimodal Thinking Ability of AI
The next frontier of AI is here -the brain that can think, see, understand, read, and even reason. Multimodal AI is a paradigm shift in how a Machine can think and process information, enabling it to understand and generate multiple types of data simultaneously, just like a Human does.
The days are gone when we needed different models to perform various tasks like understanding images, text, video or audio. With the rise of models like Gemini 2.5, GPT4, Claude and more, these AI models now have the power to do all the tasks at once. They now possess a unified way of processing the world, much like humans do. But how exactly does this work? How do they generate/understand as humans do? And why are they better than traditional models? All these questions will be answered in this post.
In this blog, we will explore the capabilities of Multimodal models, how they work, their architecture, and why they are better than traditional models.
What is a Multimodal AI?
Defining Multimodal AI
Multimodal AI refers to those models that can process and understand different information from multiple data types or “modalities” -such as text, image, video, audio and others- to produce enriched and better output and informed results.
These models mimic human-like behaviour and fuse different types of data and their output using advanced techniques like Transformers or graph-based models. This fusion enables AI to establish relationships across different data types and deliver more accurate results.
Why does it matter?
Previously, traditional AI models handled a single data type at a time. This was a very slow process, and it didn’t satisfy the users’ needs. We needed an AI which could understand and give accurate results using different data types at once. This was accomplished by providing the AI models the ability of Multimodal think multimodally. Now, AI can understand, work, and give accurate output on different data types.
How does Multimodal AI work?
Multimodal AI works by integrating and processing different types of data(image, text, audio, video, others) and then by fusing them into a single cohesive(single meaning full-output) using a specialised architecture. Here’s the breakdown:
- Data Integration: Firstly, multimodal AI are designed to understand any type of Data, so it should be able to input data like images, text, audio, and video.
- Unimodal Encoders: Each type of data has its own dedicated Neural Network or “Encoders” which is used to parse the data and understand it. For example, image data will be sent to a Convolutional Neural Network(CNN), and text data will be sent to a Recurrent Neural Network(RNN).
- Feature Extraction: After passing the input data to the encoders, they parse it and extract the key features and characteristics.
- Fusion: Then comes the fusion model, which combines all the features from different modalities to create a comprehensive representation of the output.
- Output Generation: Lastly, the system then uses this fused representation to generate an output, which could be another image, text, audio or even a combination of these.
Let’s dive a bit into the architecture of a multimodal AI.
Multimodal AI Architechture
At its foundation, multimodal AI typically relies on sophisticated architecture, which is specifically designed to process and integrate diverse data types. Multimodal AI incorporate specialised neural networks and frameworks which are capable of handling different data types.
Here’s the basic Architecture of Multimodal AI:
- Data Fusion: The main ability and heart of multimodal AI lies in how they can handle multiple data types using different data fusion techniques. A few of the data fusion techniques commonly used are:
- Early Fusion: Combines Raw data from different data types or modalities before feeding into the shared neural network.
- Late Fusion: Each data type or modality is processed by a separate model, and the results of these models are merged to form the output.
- Hybrid/Joint Models: Uses Transformers or Encoders(like BERT, ViT, Whisper) to process each data type or modality, then fuse using the Attention mechanism layer or contrastive learning.
The Shift from Traditional Models to Multimodal
Comparison table between Traditional models and Multimodal
| Feature/Aspect | Traditional AI Models | Multimodal AI Models |
|---|---|---|
| Data Format Support | Single format (text/image) | Multiple formats (text, image, audio, video) |
| Understanding of Context | Limited to one modality | Cross-modal reasoning |
| Model Architecture | Modality-specific pipelines | Unified/shared architecture |
| Training Data Requirements | Narrow scope, format-specific | Broad, diverse multimodal datasets |
| User Interaction | Command or input specific | Natural interaction (e.g., “Explain this diagram”) |
| Real-World Applications | Supervised on single task | Integrated (e.g., AI tutors, digital assistants) |
| Scalability | Hard to extend to new formats | Easier to scale with new modalities |
| Learning Paradigm | Supervised on a single task | Self-supervised cross-modal learning |
| Fusion Capability | None or late-stage merging | Early, late, or hybrid fusion |
This transition of traditional models to multimodal helped machines to act more human-like, increasing both utility and interactivity.
Real-World Application Of Multimodal AIs
- Multimodal Assistants
- Many AI/LLM tools have adapted the ability of Multimodal to work with different kinds of data, like PDF, CSV, images or web apps to understand them and answer questions about the data uploaded.
- HealthCare
- Multimodal AI can read through different medical records simultaneously and provide an accurate analysis of the records. It becomes easier for everyone to know what the report says about the person.
- Education
- Students can use it to gain an in-depth understanding of topics or to clear up confusion through visual and interactive lessons.
- Multimodal AI can explain topics with diagrams, flow charts or even prepare a study guide.
- E-Commerce
- Many of the world’s biggest companies use this Multimodal AI feature to suggest good products according to the customer’s search history and their requirements.
What are the Challenges Associated with Multimodal AI?
Even though Multimodal AI are a big step towards meeting the future, it still faces quite some issues in some areas:
- Technical Complexity in Data Integration: Many AI struggle to integrate diverse data types into a format that can be processed accurately.
- Data-Related Hurdles: Scarcity of high-quality data for training these AI models to make it better. Over-reliance on text-based data skews the model’s performance, limiting the effectiveness on images, audio and other forms of data/modalities.
- Ethical and Practical Concerns: Training data reflects human biases, and privacy risks can be a major issue for any Multimodal AI.
- Computational Cost: Real-time processing of multiple modalities demands significant resources, limiting deployment in low-network areas.
Final Thoughts
Multimodal AI represents more than an AI tool—it signifies a conceptual leap in how machines interact and perceive our world. By integrating multiple data sources and understanding them, it has become more human-like. It can now mimic human behaviour and provide answers more accurately.
Multimodal AI can work with different data types, which increases its usability for tasks beyond just questions and answers. Multimodal AI can generate improved and enriched content. As the field continues to evolve, we can expect increases in models which will be able to perform more complex tasks and provide accurate results.
The future isn’t about creating powerful models which will be able to perform complex tasks- it is about creating models which will mimic human behaviour more accurately. Multimodal AI represents a crucial step toward that future, bringing us closer to machines that truly understand our complex, nuanced world.