What Is Multimodal AI and Why Does It Matter?

Multimodal AI is defined by its ability to handle multiple different modalities, or types, of data. Earlier generative AI models were often limited to one mode of input and output. Users would enter text, in the most common example of traditional AI use, and receive a text-based output. Things began to change when developers found ways for these models to process, combine, and analyze various kinds of data.

The history of mainstream multimodal AI solutions has played out over a very short timeline. While the original release of ChatGPT could only process text when it was released in late 2022, its developer, OpenAI, quickly began work on multimodal applications, beginning with the Dall-E image processing tool.

That core Dall-E functionality, using a written prompt to produce an image, has now become part of ChatGPT. This is just one of the format interchanges possible as part of a multimodal solution.

A multimodal AI algorithm can, for example, analyze an image file and provide answers about that picture as text output. Developers have also produced multimodal systems capable of producing mixed-format output, such as text documents with images embedded in them, all generated by the algorithm.

“Multimodal” can mean that a system accepts various types of input, creates multiple formats of output, or both. The key defining factor is that there’s more than one type of content involved.

While discussing the process of multimodal AI, it’s worth defining generative AI, because multimodal models fall under this general heading. Generative AI is a blanket term for algorithms that process input to produce new output. Users can ask a GenAI system a question and have it answer in natural language, or tell it to create a new document.

Multimodal AI algorithms work by combining unimodal AI networks. Unimodal input modules are GenAI components that are designed to interpret one type of data, such as text or images. Next, a fusion model within the algorithm combines those inputs, which enables multimodal processing. The output then comes from yet another module.

These three types of models — input, fusion, and output — turn standard unimodal AI into something capable of crossing between mediums. Users don’t feel any of this complexity. They simply ask a model a simple question, such as “What is happening in this video?” or “What is this a picture of?” and receive a response.

The reverse process is also an example of a multimodal AI model in action, using a text prompt to produce an image or video based on user instructions. Any input-output pair combining multiple types of content is multimodal.

Using a single multimodal model for these many types of queries and tasks lets people draw more nuanced context from their data input and receive in-depth, high-complexity results.

As for the challenges, these largely revolve around data usage. Making safe, responsible use of information is a key consideration for GenAI systems of all kinds. The greater complexity of multimodal systems — which applies to both the multiple data types they interact with and the ways they process that data — adds even more governance pressure for users.

Since multimodal capabilities have become such a bedrock piece of modern GenAI, there are numerous examples of companies putting this technology to work. By applying the generalized power of these flexible algorithms to their business problems, business users are achieving useful outcomes. They include:

• Interpreting image files. GenAI algorithms that are able to assess image files can empower users to search for information within pictures, rather than only relying on the text-based tags or metadata associated with the files.
• Generating pictures or videos. Entering a text prompt and receiving a visual output is one of the core use cases for multimodal AI. This enables the quick creation of visual aids for presentations or documents.
• Generating summaries of video action. Multimodal algorithms capable of assessing videos are able to produce summaries to serve a variety of roles, including as descriptive captions for accessibility purposes.
• Processing in-depth inputs: Text, video, and images aren’t the only file types modern multimodal systems can interpret and assess. Advanced algorithms can also make use of complex sensor readings, such as thermal and depth data. 

There are as many multimodal AI use cases as there are companies today. Each business can apply these technology tools to its own objectives, and leaders are already making use of the latest AI developments.

Considering the relative newness of GenAI in general and multimodal models in particular, this technology has clear potential for growth and development.

One of the most direct evolutionary forces is the spread of multimodal capabilities to new settings. As OpenAI and Google add multimodal functionality to their mainstream consumer and business models, large new user bases are trying out these systems.

Other trends powering multimodal AI’s expansion and evolution include improved fusion models, more powerful real-time processing, and the release of open-source code to enable more contributors to work on key solutions.

In the near future, multimodal processing will allow GenAI systems to function more like true virtual assistants than any current product. These products’ ability to interpret an ever-larger amount of signals and produce output in new formats will help them fit into versatile roles for forward-thinking organizations.

Two of the most immediately useful multimodal AI features relating to video content include:

• Summary generation: Creating a quick summary of a video, without first converting it to another format or providing a transcript, can be a powerful capability. This makes it easier to search through a large archive of videos, whether a person is manually looking for relevant content or conducting an automated search.
• Metadata creation: The primary way to find meaningful content remains the application of metadata. When this process is automated, the ingestion and processing of new videos become quicker and less demanding of employees’ hands-on effort, freeing them up for other tasks.

With this functionality embedded in an enterprise video management platform, that system can serve as the central piece of a company’s media management infrastructure. Extracting usable intelligence and insights from videos without converting that content into another format helps companies see the value of their video content archives.

Gaining these new insights about videos is also helpful when users want to transfer the content into another enterprise platform like ServiceNow. Once multimodal algorithms have helped a company’s team assess and sort video data, those videos can migrate the relevant information to other platforms and perform agentic tasks to gain further usable insights from the content.

The same video management platforms enabling companies to use multimodal and agentic AI are also designed to provide more general benefits. For example, market-leading video solutions are built with data security in mind, ensuring that companies don’t have to expose their mission-critical video data to insecure digital environments to extract value from it.

• Streamlining video library management and organization.
• Extracting actionable insights from video content.
• Automating metadata generation and content summarization.

Since these capabilities reside in a dedicated and purpose-built enterprise video management platform, you don’t have to sacrifice video storage security or accessibility to gain access to the new functionality. The same platform that enables primary video management tasks also allows you to step into the multimodal GenAI era.

Schedule a demo of the Vbrick platform and see how multimodal AI can assist you with video management.