What is multimodal AI and how does it work?

While text-to-text AI may be what most people think of when it comes to AI, this is just a small piece of what it offers. Many AI applications today have advanced far beyond a single text format, opening up the possibilities for companies that understand this technology and use it to communicate using both visual and audio data.

These AI solutions are known as multimodal AI, and unlike their predecessors, they can process, interpret, and communicate in multiple data formats. This advanced capability has put them front and center in the technology community, as these solutions have a much wider set of capabilities compared to previous AI models.

How does multimodal AI work? What can you use it for? And how does it benefit you? We’ll explore everything you need to know so you can make the right investment to get the most from this technology.

How multimodal AI works

Similar to text-to-text AI (also known as unimodal AI), multimodal AI’s immense capabilities are due to the technology it uses to process data. Different types of data are known as modalities, and many early AI systems were designed to handle only one modality at a time. Multimodal AI uses more advanced technologies to process multiple forms of information at once.

To get a clearer picture of how multimodal AI processes data, compare the inner workings of these solutions to your brain. To process multiple forms of data, AI must be set up with neural pathways that can understand, consolidate, and interpret all sorts of information.

Multimodal AI achieves this through its three core components:

• Input module: A multimodal input module is a complex network of individual data networks. Each of these networks is responsible for a specific type of data, whether it be text, images, or audio. By combining these individual networks into a single module, multimodal AI becomes capable of accepting prompts in any form.
• Fusion module: This is where the magic happens. Fusion modules are responsible for combining, analyzing, and processing data from each form into a single set of information. Some types of data provide certain information in better ways than others, so these modules pull together the best parts of each. This process is performed using complex data processing techniques and mathematical equations, including transformer models.
• Output module: The final module takes the fused data created by a multimodal AI system and produces a response to your prompt. 

Data fusion is the most important part of this process, as it’s the phase that allows multimodal AI to understand multiple data types simultaneously. The type of data fusion a multimodal AI tool uses is typically described in one of three forms: early, mid, or late. Which descriptor is used to describe your solution depends on how different data is combined.

At their core, traditional AI machine learning (ML) models act like a straight line, processing one type of data and spitting out a response to match. Multimodal ML models can bring in pictures, text, and audio, combine the data of each, and give you an answer or action that takes each into consideration.

Key applications of multimodal AI

The advanced capabilities of multimodal AI tools allow for diverse potential uses. For example, you can use multimodal AI applications to improve customer service or to redefine healthcare professionals’ approach to patient diagnostics. 

Your use of multimodal AI will vary depending on your business’s needs. Consider the following potential uses and how they may apply to your daily workflow:

AI assistants and chatbots

In many industries, adding AI-powered chatbots can enhance user interaction using voice, text, and visual data. These chatbots and AI assistants act as part of your team, streamlining customer communication and improving personalization in every interaction.

There are many solutions available that incorporate multimodal AI into their chatbots, such as Jotform AI Agents.

Jotform AI Agents are an easy-to-use, multimodal, customizable solution that can significantly improve your workflows. To begin, simply open Jotform’s AI Chatbot Builder and create a custom AI assistant. These assistants can help with common tasks involving multimodal inputs like

• Answering customer questions
• Filling out forms on your website
• Providing personalized experiences
• Making recommendations
• Compiling and analyzing data

AI agents allow your business to create interactive chatbots that offer real-time assistance for whatever your customers may need. Customize your solution to fit your brand’s visual style and train it to answer like one of your human agents using internal data or hands-on conversations. By investing in AI agents, your team can transform your customer experience, accelerate response times, and increase your team’s overall efficiency.

Jotform AI Agents allow you to elevate and personalize the customer experience in a matter of minutes. You can even change your existing Jotform forms into agents centered around a specific function. Creating these custom agents is as easy as a few clicks:

1. Start from scratch or with a form, or customize a template.
2. Train the AI using internal data, hands-on training, or test conversations.
3. Customize your agent using the Agent Builder. Choose elements such as color, voice, avatar, and more to add the finishing touches to your AI assistant.

If you don’t feel like building your own AI agent from scratch, Jotform offers more than 7,000 AI Agent templates that you can easily clone, customize, and implement to avoid any hassle.

Comprehensive healthcare diagnostics

In the world of healthcare, vital patient information comes in many forms. From medical imaging to patient history, there’s no single medium used for all medical data. That’s why multimodal AI tools are so valuable for improving the lives of medical professionals.

With multimodal AI, medical professionals can input data such as X-ray imaging, patient history, and real-time monitoring details to create a comprehensive picture of patients’ health. This unified insight can then be used to diagnose patients more accurately, create personalized care plans, and monitor progress.

AI tools can also make commonly frustrating tasks easier for patients. For example, a Hospice Care Coordinator AI Agent can streamline the process of filling out necessary forms and handling administrative processes during an emotional time for patients and relatives.

Personalized learning in education

Providing each student with a personalized approach to learning can be a burden for a single teacher managing a classroom full of kids. Teachers must consider test scores, overall comprehension, individual learning styles, and measurable goals while creating individualized education programs (IEPs).

Multimodal AI tools can help teachers generate the information they need through multiple forms of data collection. This could include videos of students being tested using multiple learning styles, essay submissions, and test result history. By combining and analyzing this data, multimodal AI can create actionable outputs that teachers can use to build their IEPs.

AI can also generate outputs designed to help students understand materials. For example, a student could submit a question asking for a visual explanation of a concept. Multimodal AI tools could then take that text-based prompt and produce a visual output that suits that student’s learning style.

AI tools are also helpful in taking administrative tasks off teachers’ plates so they can focus on the important job of working directly with students. For example, the School Administrator AI Agent chatbot can handle communication between an institution and students or parents, automating various administrative tasks that may otherwise bog down educators.

Predictive analytics for retail and marketing

Knowing what your shoppers want is the age-old challenge of retail and marketing. While customers may leave clues for you to find in their shopping history, in-store behavior, and online interactions, analyzing and understanding these clues can be difficult.

With multimodal AI, you can streamline your data analysis process and improve your predictive analytics. Integrating multimodal AI tools throughout your customers’ shopping experiences allows you to upload key data points, like interactions, social media activity, and in-store shopping patterns, to remain proactive in your retail or marketing strategy.

Multimodal AI can use ML models to conduct sentiment analysis on customer interactions and posts to add context to your data. It can then combine other data inputs to build clear personas of your target customers, highlighting their wants, needs, and dislikes.

These insights can be applied directly to the shopping experience in various markets. A Real Estate Consultant AI Agent, for instance, can assist clients in finding their dream homes by analyzing their data and making personalized recommendations.

Autonomous systems

If you’ve ever driven a vehicle with “smart driving” capabilities, the technology that keeps your car on the road is a form of multimodal AI. Using a combination of visual data from cameras mounted on your vehicle, sensors, and radar, multimodal AI works with your vehicle’s internal technology to stay inside lane lines, adjust cruise control, and even turn your steering wheel.

Although this technology is far from perfect, combining multiple data inputs allows multimodal AI to create a semi-autonomous experience. As this technology progresses, these capabilities will likely become more accurate and could even produce fully autonomous systems.

Benefits and challenges of multimodal AI

Multimodal AI is a revolutionary technology that many industries are taking advantage of due to a few key benefits:

• Accuracy: Through ML, multimodal AI tools are capable of producing more accurate results than traditional solutions. By using data fusion to combine the best elements of multiple data streams, these solutions can improve analysis, contextual awareness, and decision-making.
• Adaptability: Due to their range of potential inputs and outputs, multimodal AI tools can be used for a wider scope of applications. This could include generating AI images, producing a script for a video, or generating an audio description for a picture on your website.
• Usability: Multimodal AI tools are not only more powerful for your team, they’re easier for your customers to use. They apply natural language processing to facilitate personalized and intuitive interactions, creating a smoother and more satisfying experience for your users.

However, multimodal AI tools are nowhere close to perfect. This constantly evolving technology may provide benefits, but it comes with some challenges as well, such as

• Data silos: To train multimodal AI, your team must be able to input large amounts of data smoothing processes into the AI backend. However, since different types of data are often stored in different locations, formats, and systems, it can be challenging or time-intensive to consolidate your data into a unified view that can be processed by multimodal AI.
• Computational resources: Multimodal AI tools are complex and require vast amounts of data to operate effectively. This means they need a lot of storage and energy to stay running, which can be taxing to maintain.
• Model complexity: Multimodal AI is more complex than traditional ML models, making it difficult to train, measure, and sustain. Due to the level of data required to train and scale these models and the difficulty of ensuring accuracy, today’s market offers a minimal number of models. 

The future of multimodal AI

Although multimodal AI is already available in a number of today’s AI solutions, there are still many advancements to be made when it comes to the application of these tools. While it’s hard to predict the exact future of multimodal AI, here are a few developments that may come down the line:

• Generative AI models: Many generative AI solutions, such as GPT-4 Vision and DALL·E 3, are already adopting a multimodal approach. The use of multimodal AI can improve the effectiveness of these generative models by expanding their input and output options.
• Enhanced cross-domain learning: The capability of multimodal AI to intake data and communicate in multiple formats may be beneficial for improving specialized areas of AI. For example, AI tools designed for customer service could learn skills using data from solutions designed for healthcare.
• Real-time applications: Multimodal ML models may also be able to improve the way AI processes real-time data. As the processing capabilities of AI speed up, adding the ability to analyze and understand multiple forms of data simultaneously can significantly improve the way AI reacts instantaneously to real-world situations.

Incorporate multimodal AI tools into your workflow with Jotform

If you’re considering using AI tools in your business, choose a solution that offers the most benefits for your company. That’s where multimodal AI tools, like Jotform AI Agents, come in. Our practical, versatile, and scalable solution can improve how you interact with customers, boost your team’s efficiency, and grow your overall brand success. When shopping for your next AI solution, look for multimodal options to ensure your team is getting the maximum value possible.