Until recently, artificial intelligence operated in silos. One model analyzed text, another recognized images, and a third processed audio. This approach, however, does not reflect how humans perceive the world. We naturally combine sight, sound, and language to understand reality.
Today, a new generation of AI is revolutionizing this paradigm: multimodal models. These systems are designed to process and integrate different types of data simultaneously. Consequently, they can “reason” more holistically, paving the way for a more human-like artificial intelligence.
What are Multimodal Models and How Did They Start?
A multimodal model is not simply the sum of several specialized AIs. Instead, it’s an architecture that creates connections between different modalities. For example, such a system learns that the word “cat” refers not only to a sequence of letters but also to the image of a feline and the sound of a meow. This cross-modal approach allows AI to understand complex concepts more comprehensively.
Interest in multimodal models grew in recent years, particularly since 2018. Their emergence was driven by two main factors:
- Increased Computing Power: Multimodal models require enormous resources. The evolution of more powerful GPUs (Graphic Processing Units) has made training these complex systems possible.
- The Need for Greater Complexity: Researchers realized that monolingual models, however powerful, had limitations in understanding the world. To overcome these, there was a need for models that could mimic the way humans use multiple senses simultaneously to learn and solve problems.
Research and Implementation
Researchers have studied multimodal integration by following different paths:
- Concatenation Method: This was one of the first approaches. Researchers simply combined data from different modalities (e.g., text and an image) into a single input for the neural network. While a simple method, it often failed to capture the complex relationships between modalities.
- Early and Late Fusion Methods: Scientists then explored fusing data at different stages of the learning process. In “early” fusion, data is combined at the beginning, before it is analyzed. Conversely, in “late” fusion, data is analyzed separately and only combined at the end to make a decision.
- Attention-Based Architectures: This is the most significant innovation. Models with attention mechanisms, like the Transformer (fundamental to ChatGPT’s development), have allowed AI to focus on relevant parts of different modalities simultaneously. For example, a model can “look” at an image and “read” a question, relating visual details to the text to provide an accurate answer.
Examples of Multimodal Models in Action
This technology’s impact is already evident in several applications.
- Visual Question Answering (VQA) Systems: A user can ask an AI to analyze an image and answer a question about it. For instance, one can show a photo of a city and ask, “How many windows does the building in the foreground have?” The multimodal model analyzes both the image and the text of the question to provide a precise answer. Companies like Microsoft have developed VQA systems to assist people with visual disabilities.
- Text-to-Image Generation: Models like DALL-E and Midjourney have popularized multimodal generation. The user enters a text description (“A cat wearing an astronaut helmet on Mars”) and the AI creates a coherent image. This process requires the AI to not only understand the meaning of each word but also how to combine the concepts visually into a single scene.
- Advanced Voice Assistants: Next-generation voice assistants no longer just respond to commands. They can analyze a user’s tone of voice, ambient sounds, and even video to better understand the context. For example, if a car skids, the assistant might ask, “Do you need help?” This ability to interpret context multimodally makes interaction more natural and useful.
The Future of Artificial Intelligence
The development of multimodal models marks a crucial step toward artificial general intelligence (AGI). While current models excel at specific tasks, AGI aims to create systems that can learn and apply knowledge across a wide range of tasks, just like a human.
Multimodal models are the foundation of this evolution. By learning to integrate different sensory modalities, they develop a richer, more nuanced understanding of the world. This progress will pave the way for innovations in fields like robotics, virtual reality, and advanced medical diagnostics.