What is multimodal AI? Meaning, examples, and more

What is multimodal AI?

Multimodal AI is an AI system that ingests and interprets multiple types of data at the same time (such as text, images, audio, video, and other modalities) and combines them into a unified understanding or output. 

Why does this matter? Because real-world information rarely comes in one flavor. A photo captures shape and color but not spoken context; a transcript gives words but not tone. By learning how different modalities complement one another, a multimodal model can align various inputs, fuse them into a single context, and produce responses that reflect the full set of clues.

An artificial intelligence model is considered multimodal when:

• It accepts input from two or more data modalities (e.g., text and image).
• It can fuse or align those modalities to form a joint representation.
• It can perform reasoning or generation based on the combined input.

For instance, GPT-4 (the architecture behind the current ChatGPT) can process both text and images, allowing users to upload visuals and get contextual responses. Earlier versions were unimodal, designed to receive text inputs and generate text outputs by using natural language processing (NLP).

How does multimodal AI work? 

Multimodal AI works by recognizing and connecting patterns across different types of data inputs. To do this effectively, these systems rely on three core components:

1. 1.
   Input module. Dedicated transformers (one per modality) transform text, images, audio, or sensor streams into numerical vectors. For example, a vision transformer slices an image into patches and treats them like word tokens. An audio encoder does the same with pitch and amplitude.
2. 2.
   Fusion module. Once the input module produces the vectors, they are aligned and combined. Two strategies dominate:

   • Early fusion. All modalities are embedded together from the start, letting the model learn joint concepts ("what a duck looks and sounds like").
   • Late fusion. Multiple models process each modality separately and merge them just before prediction. This is a more flexible approach when data sources vary in quality or timing.
3. 3.
   Output module. A decoder (or an LLM acting as a reasoning engine) turns the fused representation into an answer, caption, decision, or action plan. Teams then fine-tune with techniques like RLHF and red-teaming to reduce bias and hallucinations.

Put simply, transformers specialize, the fusion layer synthesizes, and the output layer explains. Together, they give multimodal AI the ability to read, see, listen, and then act on all three at once.

Multimodal vs. unimodal AI 

The key difference between unimodal and multimodal AI is the number of input types they can handle.

Unimodal AI processes just one type of data at a time. For example, an LLM like GPT-3 is trained purely on text. It can generate coherent responses or analyze language with impressive accuracy, but it can't interpret an image or recognize a voice.

This makes unimodal systems highly specialized but also constrained. They work well within narrow domains but struggle with contextual understanding when information comes from multiple sources.

Multimodal AI, on the other hand, is designed to take in and reason across several types of input: text, images, audio, video, and more. For instance, a multimodal AI system can interpret both text and image prompts and generate a caption, an explanation, or another image based on them.

In essence, unimodal models are specialists, while multimodal models are generalists capable of richer interpretation and interaction.

What is the difference between generative AI and multimodal AI?

Generative AI is an umbrella term for the use of machine learning models to create new content, like text, images, music, audio, and videos typically from a prompt of a single type. Multimodal generative AI expands on these capabilities, processing information from multiple modalities, including images, videos, and text. 

Multimodality can be thought of as giving AI the ability to process and understand different sensory modes. Practically this means users are not limited to one input and one output type and can prompt a model with virtually any input to generate virtually any content type.

Multimodal AI examples

Here are some widely known models that can process information from different modalities, like images, videos, and text:

• GPT-4 with vision (OpenAI) is an upgraded GPT-4 version that can process images as well as text, meaning the AI can generate visual content.
• Gemini (Google) is designed from the ground up as a multimodal model, handling text, code, audio, image, and video together.
• DALL·E (OpenAI) generates images from text prompts, combining NLP and computer vision.
• PaLM-E (Google) is an advanced language model that combines visual and textual data with ongoing observations like images and state information.
• CLIP (OpenAI) links images to text descriptions and can perform tasks like zero-shot image classification.
• Multimodal Transformer (Google) combines audio, text, and images to generate captions and descriptive video summaries.
• ImageBind (Meta) uses six data modalities (text, image, video, thermal, depth, and audio) to generate outputs.
• Tesla FSD uses a multimodal approach combining video, lidar, GPS, and driver behavior modeling.
• Inworld AI can create intelligent and interactive virtual characters in games and other digital worlds.

Multimodal AI tools

Several tools and platforms now enable developers and researchers to build or experiment with multimodal AI models:

• OpenAI API allows integration with multimodal capabilities in GPT-4 (including vision).
• Hugging Face Transformers hosts open-source multimodal models like CLIP, Flamingo, and BLIP.
• Google’s TensorFlow Multimodal is a framework for creating and training multimodal models.
• DeepMind’s Flamingo is a multimodal few-shot learner that works with vision and text.
• IBM Watsonx.ai includes multimodal analytics tools for enterprise applications.
• SuperAnnotate is a platform for multimodal data labeling, crucial for training models across images, audio, and text.

Applications of multimodal AI 

Practitioners use multimodal AI in all kinds of use cases, from analyzing medical images in healthcare to using computer vision alongside other sensory inputs in AI-powered autonomous vehicles:

• Healthcare. Multimodal models integrate medical imaging, genomic data, and patient records to improve diagnostic accuracy and early disease detection, especially in cancer screening.
• Customer service. Multimodal AI helps customer service teams better understand a customer's feelings and intentions by analyzing their voice tone, facial expressions, and written words. This allows for more personalized and effective interactions, improving customer satisfaction.
• Document transcription/extraction. Generative multimodal AI automates the conversion of various document types—like scanned images, PDFs, and handwritten notes—into structured, usable data. This technology combines advanced optical character recognition (OCR) with natural language processing (NLP) to transcribe text and also understand its context, making the data more useful. 
• Education. Tutoring systems that can see student work, hear questions, and provide real-time feedback.
• Autonomous vehicles. Use computer vision along with video, radar, lidar, and GPS data to improve navigation and safety.
• Creative tools. Generate video, music, or interactive media from textual descriptions or image prompts.
• Robotics and human-computer interaction. Multimodal learning improves interaction in robotics and AI by integrating sensory inputs like speech, vision, and touch, aiding autonomous systems and human-computer interaction.
• Security and surveillance. Security systems use multimodal AI to analyze both video and audio data to better detect threats. It helps identify unusual behavior and stressed voices, enabling quicker and more accurate responses to security incidents.
• Manufacturing. In manufacturing, multimodal AI monitors equipment using visual and sensor data. This helps predict when machines may break down, allowing for timely maintenance that keeps production lines running smoothly.
• Retail and e-commerce. Combine browsing behavior (text), product images (visual), and reviews (text/audio) for personalization.

Advantages and disadvantages of multimodal AI 

Before jumping into multimodal artificial intelligence projects, it’s worth understanding the pros and cons. Here’s a quick overview of the main advantages and disadvantages of multimodal AI:

Advantages of multimodal AI

Multimodal AI offers several key advantages that enhance its performance and versatility across applications:

• Greater accuracy. Combining multiple data types reduces ambiguity. For instance, image and text gives more context than text alone.
• Wide range of tasks. Depending on the specific model, multimodal AI systems can convert text prompts into AI-generated images, explain what’s going on in a video in plain language, generate an audio clip based on a photo and much more. 
• More human-like. Just like humans, multimodal AI processes input through multiple senses. Augmented reality, chatbots, and virtual assistants can use multimodal AI and provide a more intuitive user experience.
• Richer analytics. Better at identifying correlations between diverse data sources.
• Resilient to missing data. By combining inputs, these systems can often compensate for missing data in one modality with insights from other modalities.

Disadvantages of multimodal AI

While multimodal AI systems offer obvious benefits, they also pose challenges: 

• Training complexity. Requires aligned datasets, which are expensive and time-consuming to create. Inconsistent data quality across modalities can also affect the performance of multimodal AI systems.
• Misinterpretation of data. Multimodal AI's ability to synthesize information from different sources is powerful, but it's not foolproof. There's a real risk of the AI misunderstanding the nuances of combined data, potentially leading to misguided or harmful outcomes.
• Bias in AI models. As with any AI, multimodal systems can perpetuate existing biases in the data they're trained on. Given their complex data handling, these biases may manifest more broadly, affecting fairness and equity across multiple platforms.
• Computational costs. These models need to be able to process petabytes of diverse data types simultaneously, demanding substantial computational power that often leads to significant carbon and water usage.
• Modal mismatch. Related to data fusion, it's also challenging to align relevant data representing the same time and space when diverse data types (modalities) are involved.
• Deployment hurdles. More complicated to implement and debug in production environments.

Trends and future of multimodal AI

The multimodal AI market is gaining serious momentum. According to industry analysts, it's projected to grow significantly in the next five years, driven by demand in healthcare, autonomous systems, and content creation.

Current multimodal AI trends include:

• Native multimodality. New models like Google Gemini learn from multimodal training datasets from the start.
• Multimodal agents. Systems that can reason, plan, and act based on inputs from multiple sensors or user interfaces.
• Edge multimodal AI. Deploying models on local devices (phones, AR glasses, robots) with lower latency.
• Synthetic multimodal data. Using generative models to create synthetic image–text–audio datasets for training.

In the future, expect further convergence between multimodal AI, AI orchestration, and autonomous agents, especially in domains like robotics, smart home systems, and personal assistants. As these models become more capable and interconnected, the need for ethical oversight, data privacy protections, and bias mitigation will grow. If you’d like to learn more, you can take a look at our article about AI trends.

For businesses looking to harness the power of these technologies, leveraging an advanced AI platform for business can streamline integration, accelerate deployment, and ensure scalability across use cases.