Multimodal AI is artificial intelligence that combines multiple types, or modes, of data to create more accurate determinations, draw insightful conclusions or make more precise predictions about real-world problems.
Multimodal AI systems train with and use video, audio, speech, images, text and a range of traditional numerical data sets. Most importantly, multimodal AI means numerous data types are used in tandem to help AI establish content and better interpret context -- something missing in earlier AI.
The foundation of multimodal AI systems lies in their architecture, which employs specialized AI frameworks, neural networks and deep learning models designed to process and integrate multimodal data.
How does multimodal AI differ from other AI?
At its core, multimodal AI follows the familiar AI approach founded on AI models and machine learning.
AI models are the algorithms that define how data is learned and interpreted as well as how responses are formulated based on that data. Once ingested by the model, data trains and builds the underlying neural network, establishing a baseline of suitable responses. The AI itself is the software application that builds on the underlying machine learning models. The ChatGPT AI application, for example, is currently built on the GPT-4 model.
As new data is ingested, the AI determines and generates responses from that data for the user. That output -- along with the user's approval or other rewards -- is looped back into the model to help the model refine and improve.
Multimodal AI's ability to process diverse data types boosts its performance across various applications and gives it a clear advantage over traditional AI models with more limited functionality.
What technologies are associated with multimodal AI?
Multimodal AI systems are typically built from a series of the following three main components:
- Input module. An input module is a series of neural networks responsible for ingesting and processing -- or encoding -- different types of data, such as speech and vision. Each data type is generally handled by its own separate neural network, so there will be numerous unimodal neural networks in any multimodal AI input module.
- Fusion module. A fusion module is responsible for combining, aligning and processing the relevant data from each modality -- for example, speech, text or vision -- into a cohesive data set that utilizes the strengths of each data type. Data fusion is performed using various mathematical and data processing techniques, such as transformer models and graph convolutional networks.
- Output module. An output module creates the output from the multimodal AI, including making predictions or decisions or recommending other actionable output the system or a human operator can use.
Typically, a multimodal AI system includes a variety of components or technologies across its stack:
- Natural language processing (NLP) technologies provide speech recognition and speech-to-text capabilities along with speech output or text-to-speech capabilities. NLP technologies detect vocal inflections, such as stress or sarcasm, adding context to the processing.
- Computer vision technologies for image and video capture clarify object detection and recognition, including human recognition, and differentiate activities such as running or jumping.
- Text analysis enables the system to read and understand written language and intent.
- Integration systems enable the multimodal AI to align, combine, prioritize and filter types of inputs across its various data types. This is the key to multimodal AI because integration is central to developing context and context-based decision-making.
- Storage and compute resources for data mining, processing and result generation are vital to ensure quality real-time interactions and results.
- Speech language and processing enable multimodal AI to understand and process spoken language. By combining speech data with visual or textual information, these systems can perform tasks such as voice-activated commands and audio-visual content analysis.
- Multimodal learning is a specific application of multimodal AI, focusing on the training and development of AI models that can handle and integrate multiple types of data for improved performance and insights.
Multimodal vs. unimodal AI
The fundamental difference between multimodal AI and traditional single-modal AI is the data. A unimodal AI is restricted to processing a single type of data or source, such as text, images or audio and can't understand complex relationships across different data types. For example, a financial AI uses business financial data and broader economic and industrial sector data to perform analyses, make financial projections or spot potential financial problems for the business. Another example could be a unimodal image recognition system that might identify objects but lacks context from text or audio.
On the other hand, multimodal AI ingests and processes data from multiple sources, including video, images, speech, sound and text, enabling more detailed and nuanced perceptions of the environment or situation. In doing this, multimodal AI more closely simulates human perception and decision-making as well as uncovers patterns and correlations that unimodal systems might miss.
What are the use cases for multimodal AI?
Multimodal AI addresses a wider range of use cases, making it more valuable than unimodal AI. Common applications of multimodal AI include the following:
- Computer vision. The future of computer vision goes far beyond just identifying objects. Combining multiple data types helps the AI identify the context of an image and make more accurate determinations. For example, the image of a dog combined with the sounds of a dog are more likely to result in the accurate identification of the object as a dog. As another possibility, facial recognition paired with NLP might result in better identification of an individual.
- Industry. Multimodal AI has a wide range of workplace applications. An industrial vertical uses multimodal AI to oversee and optimize manufacturing processes, improve product quality, or reduce maintenance costs. A healthcare vertical harnesses multimodal AI to process a patient's vital signs, diagnostic data and records to improve treatment. The automotive vertical uses multimodal AI to watch a driver for signs of fatigue, such as closing eyes and lane departures, to interact with the driver and make recommendations such as stopping to rest or changing drivers.
- Language processing. Multimodal AI performs NLP tasks such as sentiment analysis. For example, a system identifies signs of stress in a user's voice and combines that with signs of anger in the user's facial expression to tailor or temper responses to the user's needs. Similarly, combining text with the sound of speech can help AI improve pronunciation and speech in other languages.
- Robotics. Multimodal AI is central to robotics development because robots must interact with real-world environments; with humans and pets; and with a wide range of objects, such as cars, buildings and access points. Multimodal AI uses data from cameras, microphones, GPS and other sensors to understand the environment better and interact more successfully with it.
- Augmented reality (AR) and virtual reality (VR). Multimodal AI enhances both AR and VR by enabling more immersive, interactive and intuitive experiences. In AR, it combines visual, spatial and sensor data for contextual awareness, enabling natural interactions through voice, gestures and touch as well as improved object recognition. In VR, multimodal AI integrates voice, visual and haptic feedback to create dynamic environments, enhance lifelike avatars and personalize experiences based on user input.
- Advertising and marketing. Multimodal AI can analyze consumer behavior by combining data from images, text and social media, enabling companies to craft more targeted, personalized and effective ad campaigns.
- Intuitive user experiences. Multimodal systems enhance the user experience by enabling interactions that feel more natural and intuitive. Instead of explaining problems or providing detailed lists, users can simply upload audio clips or photos, such as a car engine sound for troubleshooting a car engine, or pictures of their fridge when looking for recipe ideas.
- Disaster response and management. Multimodal AI improves disaster response and management by integrating and analyzing diverse data sources, such as social media, satellite imagery and sensor data to provide real-time situational awareness. This capability helps emergency responders assess disaster consequences more effectively, identify the most affected areas and allocate resources efficiently.
- Customer service. Multimodal AI can transform customer interactions by analyzing text, voice tone and facial expressions to gain deeper insights into customer satisfaction. It can also enable advanced chatbots to provide instant customer support. For example, a customer can explain an issue with a product via text or voice and upload a photo, enabling the AI to resolve the problem without human intervention automatically.
Multimodal AI challenges
Multimodal AI's potential and promise comes with challenges, particularly with data quality and interpretation for developers. Other challenges include the following:
- Data volume. The data sets needed to operate a multimodal AI, driven by the sheer variety of data involved, pose serious challenges for data quality, storage and redundancy. Such data volumes are expensive to store and costly to process.
- Learning nuances. Teaching an AI to distinguish different meanings from identical input can be problematic. Consider a person who says "wonderful." The AI understands the word, but it can also represent sarcastic disapproval. Other contexts, such as speech inflections or facial cues, differentiate and create an accurate response.
- Data alignment. Properly aligning meaningful data from multiple data types -- data that represents the same time and space -- is difficult.
- Limited data sets. Not all data is complete or easily available. Limited data, such as public data sets, are often difficult and expensive to find. Many data sets also involve significant aggregation from multiple sources. Consequently, data completeness, integrity and bias can be a problem for AI model training.
- Missing data. Multimodal AI depends on data from multiple sources. However, a missing data source can result in AI malfunctions or misinterpretations. For example, if audio input malfunctions or if it provides no audio or audio such as whining or static noises, AI's recognition and response to such missing data is unknown.
- Decision-making complexity. The neural networks that develop through training can be difficult to understand and interpret, making it hard for humans to determine exactly how AI evaluates data and makes decisions. Yet this insight is critical for fixing bugs and eliminating data and decision-making bias. At the same time, even extensively trained models use a finite data set, and it's difficult to know how unknown, unseen or otherwise new data might affect the AI and its decision-making. This can make multimodal AI unreliable or unpredictable, resulting in undesirable outcomes for AI users.
- Data availability. Since the internet mainly consists of text, image and video-based data, less conventional data types, such as temperature or hand movements, are often difficult to obtain. Training AI models on these data types can be challenging, as they must be generated independently or purchased from private sources.
Examples of multimodal AI
The following are examples of multimodal AI models currently in use:
- Claude 3.5 Sonnet. This model, developed by Anthropic, processes text and images to deliver nuanced, context-aware responses. Its ability to integrate multiple data types and formats enhances user experience in applications such as creative writing, content generation and interactive storytelling.
- Dall-E 3. Dall-3 is the latest version of Dall-E and the predecessor to Dall-E 2. It's an OpenAI model that generates high-quality images from text descriptions.
- Gemini. Google Gemini is a multimodal model that connects visual and textual data to produce meaningful insights. For example, it can analyze images and generate related text, such as creating a recipe from a photo of a prepared dish.
- GPT-4 Vision. This upgraded version of GPT-4 can process both text and images, enabling it to generate visual content.
- ImageBind. This model from Meta AI integrates six data modalities to produce diverse outputs, including text, image, video, thermal, depth and audio.
- Inworld AI. Inworld AI creates intelligent and interactive virtual characters for games and digital environments.
- Multimodal Transformer. This Google transformer model combines audio, text and images to generate captions and descriptive video summaries.
- Runway Gen-2. This model uses text prompts to generate dynamic videos.
Future of multimodal AI
According to a report by MIT Technology Review, the development of disruptive multimodal AI-enabled products and services has already begun and is expected to grow.
Recent upgrades to models such as ChatGPT highlight a shift toward using multiple models that collaborate to enhance functionality and improve the user experience. This trend reflects a growing recognition of the value of multimodal capabilities in developing more versatile and effective AI tools.
Multimodal AI is also poised to revolutionize industries such as healthcare by analyzing medical images and patient data to deliver more accurate diagnoses and treatment recommendations. Its ability to synthesize information from multiple sources is expected to enhance decision-making and improve outcomes in critical areas.
Explore how multimodal AI transforms industries such as healthcare, automotive, media and telecom. Understand and evaluate its growing role across various sectors.