Multimodal models

Multimodal models are revolutionizing artificial intelligence technologies, enabling users to navigate AI models more intuitively and usably. 

Discover what multimodal models are and how they are being used, with Micron.

What are multimodal models?

Multimodal model definition: Multimodal models are a kind of artificial intelligence model based on ​deep learning.

Instead of learning from single datasets to perform a single action, multimodal models are trained with multiple datasets, making them adaptable to multiple uses.

This transformative approach to AI development has become feasible only in recent years. It integrates different aspects of other ​machine learning models to create a complex, adaptable system. 

Most AI models are designed to process a single type of data; this could be images, text or numerical data, but only one type. In contrast, multimodal models can handle a variety of data types.

As well as increasing processing capabilities, using multiple data types significantly enhances the range of possible outputs. A model trained to process only textual data can generate only textual outputs, while a multimodal model can generate output in a different format from the input data due to diverse training data. 

How does multimodal learning work?

Multimodal machine learning is distinct from unimodal machine learning in that models are trained on a single type of data. This data is used to train the model to identify patterns, and then the model is used to generate outputs based on these patterns. These outputs could be generative, ​predictive, or analytical.

Multimodal AI consists of using multiple unimodal models in conjunction with one another. Input modules are made up of multiple unimodal ​neural networks, each equipped to handle a specific type of data.

A fusion module then aligns and processes the data from each module. This approach allows the model to process multiple data types simultaneously and create a singular output from this data.

What is the history of multimodal models?

Multimodal AI has a relatively recent history, driven by the exponential growth in artificial intelligence over the last 20 years. As AI models became more sophisticated and usable, the demand for increasingly adaptable models grew.

Multimodal models were developed from this demand. Prominent examples of large multimodal models include Google Gemini and GPT-4o. Launched in 2023, these models continue to receive regular updates to enhance their usability. Multimodal machine learning models represent the future of user-friendly artificial intelligence. 

And with a wider breadth of data processing capabilities comes more innovative outputs.

What are key types of multimodal models?

Multimodal models can be broadly categorized into two types: unified or singular. 

• Unified models: These models integrate multiple existing unimodal models into a single, cohesive AI architecture. This integration means they can handle multiple kinds of data, such as images and text, simultaneously, enhancing overall capabilities.
• Singular models: In contrast, singular models are designed to handle only one type of data at a time. For example, they can process textual data. These models operate in a distinct mode, each with its own architecture, which is combined at a later stage.

As the field of multimodal AI continues to evolve, unified models are becoming the preferred choice due to their versatility and data-handling capabilities.

How are multimodal models used?

So far, the most exciting use cases for multimodal models have been the extremely user-friendly generative models seen across most major players in the tech sphere.

OpenAI’s GPT-4o is an example of how an innovative textual ​generative model (ChatGPT) was combined with DALL-E, a model that generates and processes image data. GPT-4o has multiple functions, acting as a centralized model that brings together the innovative elements of multiple OpenAI models while still retaining the usable interface that has resonated with users.

Similar models include Google’s Gemini, another generative experience that brings together multiple data types with an easily navigable and usable platform. Microsoft’s Copilot has achieved a similar goal, with both tech giants offering a generative AI tool to assist users.