Imagine you want to shop a particular style and ask your AI assistant to find the style for your body type, size & fit by giving it a picture. Isn't it intelligent how it combines textual and visual inputs to provide you with your desired recommendations, which would otherwise have cost you hours of research?
With the recent advancements in AI, this new branch, named “multimodal AI,” can handle any data input you give. Nevertheless, it also replies to you in your desired output data modality.
But, what is “multimodal AI”? How is it different? What benefits does it offer? Are there any real-world multimodal AI models in action? Let's explore all of this together!
What Is Multimodal AI?
Let's understand the terms “multimodal AI” from scratch. In artificial intelligence, modality refers to the types of data. The most common data modalities include text, audio, video, and images, but there are many more.
In simple terms, multimodal AI refers to an AI system or machine learning model that can operate in any data modality. They can handle, process, and input data in any form and modality, making them more robust, comprehensive, and powerful. Furthermore, they can also generate outputs in multiple data modalities.
For example, you shared a picture of your messy desk with a multimodal AI model asking, "Can you help me find my glasses?” The AI instantly responds, saying, “Yes! Your glasses are under the blue notebook on the left side of your desk.” Here, the model uses both the inputs you provided - the image and your question in text format to generate its response in the most natural and human-like way.
These models have a more comprehensive understanding of the world around them as they mimic the same internally. They attempt to combine and perceive various types of sensory inputs and respond to me like a human.
How Does Multimodal AI Differ From Other AI Models?
But what sets multimodal AI apart from other AI models? Let's first understand how it's different from unimodal AI.
The primary difference is that while a multimodal AI model can handle, process, integrate, and operate with multiple data types and modalities, an unimodal AI model cannot handle this diversity. Additionally, unimodal AI models have limited data processing capabilities, which makes them less contextually relevant. On the contrary, multimodal AI models are more comprehensive and contextually intelligent.
Multimodal AI models have more diverse and intricate infrastructure, making them more complex and functional than unimodal models. In terms of performance, unimodal models are suitable for automating basic and routine tasks, but multimodal ones can also handle context-intensive tasks. With these differences being stated, traditional AI models are generally unimodal.
Some also question how multimodal AI differs from generative AI. The key difference between multimodal AI and generative AI is that while the former focuses on processing various data types, the latter focuses on creating new content from existing data. Multimodal AI is known for its multisensory capacity, whereas generative AI is known for its creative power.
How Does Multimodal AI Work?
As one of the most advanced innovations in artificial intelligence, multimodal AI uses various components for its work. Some of these are: Natural Language Processing (NLP), speech language and processing, multimodal learning, computer vision technologies, text analysis, and integration systems.
But, how are these models capable of handling various data modalities? A multimodal AI is built with numerous single-mode networks that ingest, process, and integrate multiple data types, giving the desired outcome. It's possible because of their three main components. Let's explore these and how they build up a multimodal AI model.
1. Input Model: This module has numerous unimodal neural networks, each responsible for ingesting and processing a particular data type.
2. Fusion Module: The fusion module collects the incoming data, combining and processing it based on the data’s modality. This process of data fusion involves various data processing and mathematical techniques.
3. Output Module: As the name suggests, this module creates and delivers the outputs to the user.
3 Key Characteristics of Multimodal AI
According to a paper by Carnegie Mellon, every multimodal AI model has three basic characteristics: heterogeneity, connections, and interactions.
1. Heterogeneity: Different types of data are different in terms of their qualities, structures, and representations. Thus, a multimodal AI has to handle various data modalities and their unique, diverse structures and qualities.
For example, if you ask a multimodal AI to imagine a photo alongside a text description of a birthday party, one of its outputs will be an image woven with colors, faces, and expressions, and the other will be a beautiful story. While both these outputs are for the same event, they are different in structure.
2. Connections: This means that different types of data often share similar meaning. In the above example, while the data types differ, they describe the same thing. Thus, a multimodal AI model must be capable of finding and understanding such links and connections.
3. Interactions: When text, images, or other types of data are brought together, they can help each other. For example, an image might help clarify something vague in the text, or vice versa. Multimodal AI doesn’t just look at each part—it looks at how they work together to give a fuller understanding.
Top 7 Examples of Multimodal AI Models
With the rapid advancement of multimodal AI across various fields, let's understand it better with some well-known examples.
1. GPT-4 Vision: This multimodal AI version by OpenAI is excellent at analysing images to understand their content and connections. It's an upgraded version of GPT-4 4 which can process and generate visual content, besides text.
2. DALL-E 3: Launched by OpenAI as the predecessor to DALL-E 2, this latest version primarily emphasizes text-to-image content creation. It can easily generate artistic and creative styles by interpreting complex user text-based descriptions and prompts.
3. Gemini: Google Gemini can process text and data in other formats like images, audio, and video. It provides meaningful analytical insights by connecting visual data with textual data.
4. ImageBind: Developed by Meta AI, this is a powerful multimodal AI capable of handling data in six different modalities: text, images, audio, video, thermal, and depth. It can also generate outputs in these modalities while understanding multi-faceted information.
5. Claude 3.5 Sonnet: Developed by Anthropic, it can easily process images to deliver context-aware outputs. Best for handling visual information, its applications are best seen in fields like content generation, interactive storytelling, and creative writing.
6. LLaVA: LLaVA (Large Language and Vision Assistant) is an open-source multimodal AI that can process, reason, understand, and respond to textual and image-based data requests.
7. Google's Multimodal Transformer (MTN): This multimodal AI model processes and integrates information from different sources, like text, images, and video, using a unified architecture to understand and generate meaningful, context-aware responses across modalities.
5 Benefits of Multimodal AI
Multimodal AI models are much more flexible and versatile than unimodal ones. Below are some key benefits offered by multimodal AI.
1. Better Contextual Awareness & Understanding
Multimodal AI models combine information from various sources and analyze and interpret it simultaneously in multiple modalities. It gives the model a broader, more well-rounded, and comprehensive understanding of each data type and its context and connections.
For example, suppose you provide a picture of a broken laptop to a multimodal AI, prompting, “It won't turn on”. In that case, it helps you figure out the problem with a layered understanding of both the visual image and the textual complaints.
2. Cross-Domain Learning
Besides processing various data modalities, it also learns from them, which helps it make meaningful connections across multiple domains. For instance, it might know how people describe emotions (text/audio) and how those emotions look (images/video). This allows it to perform tasks requiring integrated knowledge, like understanding sentiment from tone of voice and facial expression, which is valuable in mental health support, entertainment, and education.
3. Improved Creativity & Problem-Solving Skills
The potential of multimodal AI in blending data proves helpful in fields like content creation, giving rise to more creative opportunities and ideas. Furthermore, they can also handle complex situations such as analysing multimedia content. They approach these problems and data naturally and creatively like humans.
4. More Accurate Outputs
With their access to various data types, they can cross-check and validate their responses, reducing the chances of errors. For example, A health assistant that takes multi-layered input, including patient notes, X-ray images, and voice description of symptoms, will have more reliable diagnoses and recommendations.
5. More Intuitive User Experience
The best part about multimodal AI is that it interacts with users like a human would - naturally and flexibly. Whether a user speaks a command, uploads a photo, or types a question, the AI can process it. This makes interfaces more human-friendly, especially for users with disabilities, low digital literacy, or who prefer visual or spoken input over typing.
Seven Business Use Cases of Multimodal AI
Multimodal AI is well known for its untapped potential and promising benefits. But, how are businesses implementing it in their operations & tasks? Let's explore it.
1. Computer Vision
While traditional computer vision focuses only on visual data, multimodal AI goes beyond it to understand & interpret the context of the data. It's useful for tasks like image captioning, visual question answering, scene understanding, content moderation, medical imaging analysis, product recognition, surveillance, and anomaly detection. It does not “just see” the images, but reads, hears, and understands, allowing it to give powerful and intelligent analysis.
2. Document Transcription & Extraction
Unlike traditional OCR, multimodal AI can effortlessly convert one document type to another. Advanced OCR features help it understand the context of the data type, making transcription and conversion more accurate and meaningful.
This proves helpful in automating form & invoice processing, recognising handwriting, understanding multi-language documents, analysing legal documents, extracting and interpreting tables, and credentials verification. For example, the Azure AI Document Intelligence tool allows businesses to extract valuable information from any document or form.
3. Robotics
Robots are built to operate in real-world scenarios, making multimodal AI crucial in their developmental stage. It helps the robots gain a more comprehensive & richer understanding of the human intent & the surrounding environment.
With multimodal AI capabilities, these robots can touch data and combine it, making them more adaptable, intelligent, and capable of handling complex situations. This also improves their decision-making skills, making them more accurate in real-life problems.
4. Retail
AI assistants, equipped with multimodal AI capabilities, assist shoppers in getting their dream fit in their size within minutes. For example, Amazon’s StyleSnap feature uses computer vision to recommend products to users based on the image they've uploaded.
Besides advanced visual search, multimodal AI is also useful for enhancing the in-store experience and improving virtual try-on and augmented shopping.
5. Augmented Reality & Virtual Reality
When multimodal AI is used in AR & VR technology, it creates a more intelligent & immersive experience by combining visual, auditory, and textual awareness. It facilitates more natural interactions and haptic feedback to create dynamic, life-like environments to deliver personalized experiences.
It can be used for various purposes such as education & training, immersive shopping experiences, virtual tourism & cultural exploration, entertainment & gaming, and remote, virtual meetings.
6. Security
When multimodal AI is used in security systems, it helps identify threats and unusual behaviour and voices more intelligently and proactively. It combines data from audio inputs, text reports, video feeds, and sensory data to improve security, reduce false positives, and speed up response time.
It's primarily used in surveillance & threat detection, multimodal biometric authentication, anomaly detection in crowds, incident reconstruction, and secure document management.
7. Customer Service
Multimodal AI enhances customer service by enabling support systems to understand and respond to a mix of text, voice, and images. Customers can upload screenshots, describe issues verbally, or type queries, while the AI processes all inputs together.
This leads to faster issue resolution, more accurate responses, and a seamless, human-like support experience across channels.
Future of Multimodal AI in 2025 and Beyond
According to Han Xiao, CEO of Jina AI, “Communication between humans is multimodal,”. Multimodality allows AI to receive various modes of information from multiple sources and combine them into a nuanced and realistic representation of the real environment.
The future of multimodal AI looks promising, considering the report by MIT Technology Review, which says that the development of such products is expected to grow shortly. We can expect unified models within a single infrastructure, such as Google's Gemini.
The cross-modal interaction will be refined and enhanced with advanced mechanisms to make the outputs more contextually aware and coherent. Researchers and developers are also looking ahead for collaborations and advancements, keeping some of these tools open source.
With further advancements and fine-tuning, we can create multimodal AI that responds to, understands, and acts like us in real-life scenarios. They are expected to grow beyond self-driving vehicles and be implemented in many ways across other industries beyond our imagination.
FAQs: Multimodal AI
1. What is multimodal AI?
In simple terms, multimodal AI refers to an AI system or machine learning model that can operate in any data modality. They can handle, process, and input data in any form and modality, making them more robust, comprehensive, and powerful. Furthermore, they can also generate outputs in multiple data modalities.
2. What is unimodal vs multimodal AI?
The primary point of difference between them is that while a multimodal AI model can handle, process, integrate, and operate with multiple data types and modalities, a unimodal AI model isn't capable of handling this diversity.
3. What is the difference between multimodal AI and generative AI?
The key difference between multimodal AI and generative AI is that while the former focuses on processing various data types, the latter focuses on creating new content from existing data. Multimodal AI is known for its multisensory capacity, whereas generative AI is known for its creative power.
4. What is an example of multimodal AI?
Some examples of multimodal AI are: GPT-4 Vision, DALL-E 3, Gemini, and ImageBind.
5. Is ChatGPT multimodal?
Yes, ChatGPT has multimodal capabilities, allowing it to interact with users in multiple input formats and making it a multimodal AI.
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