We’re in a world where technology doesn't just listen to our voice or read text but also picks up on facial expressions and the details around us. This is what multimodal AI does – it processes multiple forms of data, like images, sounds, and words, all at once. This technology makes our daily interactions with technology as easy and natural as chatting with a friend.
The multimodal journey started with GPT-4, released in 2023, which was the first to handle both text and images effectively. The most recent multimodal model, GPT-4o Vision, goes even further by creating interactions that are incredibly lifelike. The last year was huge for multimodal AI, making it one of the most talked about Gen AI trends in 2024.
The market for multimodal AI was valued at USD 1.2 billion in 2023 and the market size is expected to grow at a CAGR of over 30% between 2024 and 2032. This just shows how hot multimodality is expected to get in the future.
Multimodal AI is quickly becoming a favorite tool for businesses, as they tailor it to fit their specific needs. For example, in retail stores, smart shopping assistants can now see and respond to the products you're interested in. In customer service, this helps agents understand not just the words but also the emotions of customers. Businesses are becoming more and more obsessed with using multimodal Gen AI in their operations.
In this article, we'll explore multimodal AI, learn about how large multimodal models work and are trained, and how to customize them to your own business use case with SuperAnnotate.
What is multimodal AI?
Multimodal AI is a type of artificial intelligence that processes and integrates multiple types of data—such as images, sounds, and text—at once. In machine learning, modality is a given kind of data.
By combining different data types, multimodal AI can perform tasks that single-modality AI cannot. For example, it can analyze a photo, understand spoken instructions about the photo, and generate a descriptive text response. This makes it highly useful in various applications, from customer service to advanced security systems.
Multimodal AI vs. unimodal AI
When comparing multimodal AI to unimodal AI, the key difference lies in how they handle data. Unimodal AI systems work with one type of data at a time, such as only images or only text. This makes them specialized but limited in scope.
Multimodal AI, on the other hand, can process and integrate multiple types of data simultaneously, like images, text, and sound. This ability allows them to understand more complex scenarios and provide richer, more comprehensive responses. Let’s get deeper into multimodality.
How does multimodal AI work?
Usually, a multimodal AI system would contain 3 components:
- Input module: The input module is made up of several unimodal neural networks. Each network handles a different type of data, collectively making up the input module.
- Fusion module: After the input module collects the data, the fusion module takes over. This module processes the information coming from each data type.
- Output module: This final component delivers the results.
In essence, a multimodal AI system uses multiple single-mode networks to handle diverse inputs, integrates these inputs, and produces outcomes based on the specifics of the incoming data.
Multimodality can be expressed in different ways – like text-to-image, text-to-audio, audio-to-image, and all these combined together (+text-to-text). Note that at their core, multimodal models share similar operating principles regardless of the particular modalities considered. Due to this similarity, we'll concentrate on one modality type - text-to-image, which can be generalized to have the big picture for other modalities, too.
But how does multimodality actually work? Let’s dive into text-to-image.
Text-to-image models
Text-to-image models start with a process known as diffusion, which initially generates images from random patterns, or what's called gaussian noise. A common issue with early diffusion models was their lack of direction—they could create any image, often without any clear focus.
To make these models more useful, text-to-image technology introduces textual descriptions to direct the image creation. This means if you give the model the word "dog," it uses the text to shape the noise into a recognizable image of a dog.
Here's how it works: both text and images can represent the same idea. For instance, the word "dog" and a picture of a dog both point to the same concept.
Text-to-image technology transforms text and images into mathematical vectors that capture their underlying meanings. This helps the model understand and match the text with appropriate images.
How is text-to-image trained?
Continuing the example, the technique that’s used to train these models is called RLHF (followed by large scale pretraining.)
To start, imagine we have a dataset of images each paired with a caption. For each pair—say, a dog, a cat, and a giraffe—we process the text and the image through their respective encoders. This results in a pair of vectors for each image-caption pair.
The training process involves adjusting these vectors so they align more closely when they represent the same concept. We use something called cosine similarity, a measure that helps us see how close or far apart these vectors are in space. By maximizing the similarity for pairs that should match, we ensure that vectors for the same concept point in the same direction. This gives the direction a specific meaning within the model.
Conversely, for pairs that shouldn't match—like a dog text with a giraffe image—we minimize their similarity. We repeat this for every combination in our dataset, training the model to map text and images to the same conceptual space effectively.
This training is at the heart of how diffusion models work. When it comes time to generate an image, the model embeds the input text into this meaning space, translates the textual vector into a visual one, and then decodes this visual vector to create the final image.
Audio-to-image models
Turning audio into images might sound straightforward, but it's actually quite complex. Currently, there isn't a single model that directly converts audio to images. Instead, we use a series of steps involving three multimodal models to make this happen.
First, we start with audio input—let's say, someone describing a scene. This audio isn't directly turned into an image. Instead, it first gets translated into text because text acts as a universal medium that ties different forms of data together. This is due to the clarity and detail that text can convey, which are crucial for the next steps.
Once we have the text, it's used to guide the image creation. The process of determining exactly how the model chooses whether to output text or an image isn't fully transparent yet, and the finer details haven't been widely shared.
However, there might be a component where the model is trained to output both images and text during its learning phase. Users then interact with these outputs, choosing the ones that best meet their needs. This interaction helps the model learn over time which type of output—text or image—is expected in various scenarios.
By using this method, the model gradually becomes better at predicting and fulfilling user expectations, creating images from audio inputs that are as accurate and relevant as possible.
How SuperAnnotate empowers companies to train and evaluate Multimodal AI
Training and evaluating multimodal AI systems demand versatile and adaptable annotation tools, but traditional platforms are often limited to single-modality functions—either image, text, or audio alone. SuperAnnotate helps overcome these limitations by providing an easily customizable no-code/low-code editor that allows companies to design multimodal annotation interfaces tailored to their unique use cases.
With a range of ready-made components supporting images, audio, video, PDFs, and more, SuperAnnotate enables teams to seamlessly configure interfaces, adapting the tool to their exact needs rather than struggling to fit their use case into outdated, rigid systems.
From custom workflows to collaborative feedback loops, SuperAnnotate has helped leading companies, such as Twelve Labs, push the boundaries of multimodal AI. By using SuperAnnotate’s platform, Twelve Labs can power advanced video analysis and search, unlocking new capabilities in video content understanding.
What sets SuperAnnotate apart
SuperAnnotate delivers advanced, managed solutions to meet the complex data needs of foundation model builders:
- Fully managed data foundry: Offload the operational workload with our expert-managed services. Our carefully vetted PhD-level specialists and dedicated account teams ensure your data projects run smoothly from instructions to quality assurance.
- Multimodal, low-code, customizable interfaces: You can customize interfaces for any data type—text, image, audio, or video—quickly and without coding, making it easy to handle intricate multimodal datasets.
- Model-in-the-Loop RLHF: Integrate human feedback with model insights in real-time to enhance data quality and speed, fostering efficient and dynamic dataset creation.
- Advanced workflows: Design custom workflows with code execution, database queries, and multi-specialist routing to support complex tasks with precision and quality control.
Let us handle your multimodal data needs with the expertise and tools required to elevate your foundation models.
Multimodal AI use cases for businesses
Multimodal AI is changing the way businesses operate by combining different types of data—like text, images, and audio—to make smarter decisions. Here’s how companies are putting this technology to work:
- 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. For example, Uniphore's conversational AI platform uses multimodal analysis to enhance call center performance and customer experience.
- 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. An example of this in action is Azure AI document intelligence. This tool simplifies the extraction of information from forms and documents, helping businesses efficiently process invoices, receipts, and contracts.
- Retail: In retail, multimodal AI is used to offer more personalized shopping experiences. It looks at a customer’s previous purchases, browsing history, and social media activity to suggest products that they are more likely to buy. A prime example of this in action is Amazon's StyleSnap feature, which uses computer vision and natural language processing to recommend fashion items based on uploaded images.
- Security: 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 might break down, allowing for timely maintenance that keeps production lines running smoothly.
Popular multimodal AI models
GPT-4o (OpenAI): This model handles text, images, and audio. It's great at blending different types of inputs during conversations, making interactions feel more natural and aware of the context.
Claude 3 (Anthropic): This model works with text and images. It's especially good at understanding visual information like charts, diagrams, and photos with impressive accuracy.
Gemini (Google): Developed by Google DeepMind, Gemini used to process text, images, audio, and video. Its image generation feature got recently paused for good reasons.
DALL-E 3 (OpenAI): Focused on text-to-image creation, this model interprets complex text prompts and produces images that capture specific artistic styles accurately.
LLaVA (Large language and vision assistant): This system merges vision and language understanding. It's open-source, which means anyone can contribute to or modify it.
PaLM-E (Google): An advanced language model that combines visual and textual data with ongoing observations like images and state information.
ImageBind (Meta): Capable of working with six modalities—images, text, audio, depth, thermal, and IMU data—this model is a powerhouse at linking and understanding multifaceted information.
CLIP (OpenAI): This model connects text with images and is known for its zero-shot learning capabilities, allowing it to handle a variety of image classification tasks without specific training on those tasks.
Multimodal AI risks
A report from Stanford's Institute for Human-Centered Artificial Intelligence (HAI) points out that as multimodal models like DALL-E improve, they could produce higher-quality, machine-generated content. However, this raises a concern: it might become easier to use such content inappropriately, for example, to craft misleading content aimed at different political groups, nationalities, or religious communities. Have you seen those videos of famous people's deepfakes? They are extremely realistic, showing how big the risks are with multimodal AI. Here are a few of the many risks with multimodal AI:
- Privacy concerns: These systems process extensive personal data, including voice, images, and text. Such deep access to personal information raises significant privacy issues, especially if stringent safeguards aren't in place.
- 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 could manifest more broadly, affecting fairness and equity across multiple platforms.
- Increased complexity in management: The advanced nature of multimodal AI systems makes them more challenging to manage and maintain than simpler, unimodal systems. This complexity can translate into higher operational costs and potential difficulties in ensuring consistent performance.
- Dependence on technology: The sophistication of multimodal AI might lead us to rely heavily on technology in our daily lives, possibly at the expense of human judgment and skills. This dependency could reshape how we make decisions, impacting our independence and critical thinking abilities
Closing remarks
As we wrap up our discussion on multimodal AI, this technology is transforming how the AI industry operates. By combining different data types, such as images, text, and audio, multimodal AI is making interactions more intuitive and tailored to individual needs, from customer service to retail and security.
However, multimodality comes with great responsibility. We need to be mindful of privacy, potential data misinterpretations, and biases to ensure its ethical use.
