If you're already amazed by the growing realism with which artificial intelligence can recreate human expressions, generate text, and more, multimodal AI technology will leave you in awe. Multimodal AI models can understand, process, and provide output in a variety of modalities, or forms of data. They then use that wide range of data to generate a more comprehensive understanding of user input and needs.
Tavus API humanizes AI with tools like multimodal AI, enabling highly realistic, real-time video conversations with your very own AI spokesperson. Read on to learn more about how multimodal AI works. This article covers:
- What is Multimodal AI?
- How Multimodal AI Works
- Multimodal AI use casessome text
- Generative AI
- Autonomous Vehicles
- Learning Tools
- Virtual Assistants
- Benefits of Multimodal AIsome text
- Greater Accuracy & Performance
- Natural Interactions
- Contextual Understanding
- More Powerful AI Technology
- Challenges of Multimodal AI
- Learn More About Multimodal AI
- Leveraging the Power of Multimodal AI for Your Business
What is Multimodal AI?
Multimodal AI is artificial intelligence that can process multiple types of data or modalities (i.e., modes of communication like text, video, image, or audio). These machine learning models can analyze various types of data—in different forms—to create more comprehensive understanding and output.
Multimodal AI models can move between modalities with ease. For example, a model might generate an image based on text input or vice versa. Multimodal AI also enables models to make stronger informed decisions when analyzing data and generating outputs.
Multimodal AI vs Unimodal AI
The primary difference between unimodal and multimodal AI is their ability to process certain types of data. While multimodal AI can process and create output in several forms, unimodal AI only understands and generates content in one modality, such as video or text.
This seemingly small difference makes a greater impact on an AI model’s output than you might imagine. With access to data in several modalities, multimodal AI can better understand requests and data, enabling richer outputs for a variety of user needs.
How Multimodal AI Works
Multimodal AI uses large language models (LLMs) and data fusion techniques to process multiple input modalities. A 2022 Cornell paper on multimodal machine learning breaks down the process into three main components:
- Heterogeneity: The many structures and qualities of different modalities
- Connections: Shared qualities between different modalities, such as semantic correspondence or statistical similarities
- Interactions: The ways modalities interact when integrated
Multimodal AI models also utilize natural language processing (NLP) and computer vision technologies. Other tools for quality multimodal AI include text analysis, and resources for storage and computation to enable data mining.
Multimodal AI Use Cases
Multimodal AI models offer a range of services and tools, with many potential uses. Read on to learn about a few of the most common multimodal AI use cases.
Generative AI
Older AI models often focused on making predictions based on datasets, but generative AI can create new content. Generative AI uses deep learning models to analyze data, “learn” the data, and generate output based on what it sees as a statistically likely response to the input.
Multimodal generative AI models don’t just process multiple modalities of data and provide output based on user requests. These models also learn patterns and associations based on the data and generate feasible responses and content.
Tavus’ conversational video is one example of multimodal generative AI’s capabilities. With Tavus API, developers can integrate tools that allow end users to create digital twins that act as conversational agents. Tavus’ Conversational Video Interface (CVI) makes it possible for talking head agents to speak, see, and hear. The model uses advanced speech recognition, vision, and conversational awareness to enable instant and natural-sounding responses.
Autonomous Vehicles
Driverless cars, or autonomous vehicles, are revolutionizing transportation. Many proponents claim these cars will help make car travel safer, more efficient, and more accessible.
For autonomous vehicles to work, they need several types of AI, including multimodal AI. Innovators are enhancing autonomous driving with multimodal AI to help these cars gain a more comprehensive understanding of their surroundings. The ability to process multiple modalities enables autonomous vehicles to use cameras, radar, and other forms of data input to improve depth perception and accuracy. Using a wide variety of data enables better awareness and decision-making so passengers can feel confident and safe with AI drivers.
Learning Tools
Multimodal AI has the potential to revolutionize online education with its ability to analyze different learning materials and student responses. Effective teachers interpret students’ facial expressions, tone, and class work to determine their grasp of materials and concepts. This allows teachers to adjust their approach and accommodate different learning styles.
Online education can make this more difficult, especially since students work with material in various formats. Multimodal AI offers the potential for AI models to conduct analyses similar to how a teacher would—by using student expressions, tones, and text submissions to develop a comprehensive view of each student’s learning process and needs. Multimodal AI can personalize learning paths, provide intelligent tutoring, generate educational content, evaluate and give feedback on assignments, and enhance access to learning materials.
Virtual Assistants
Multimodal AI has the power to make personal AI assistants more effective. AI virtual assistants use conversational AI models to create agents that can interact with human users in ways that feel natural. With multimodal AI, those conversations become seamless experiences thanks to the model’s ability to process input and generate output in a variety of forms, including voice, chat, and text.
Benefits of Multimodal AI
Multimodal AI offers wide-ranging benefits, from improved performance to increased contextual understanding. Read on to learn more about the ways multimodal approaches are pushing AI technology further.
Greater Accuracy & Performance
Access to data in various forms, including text, image, and video, allows multimodal models to understand input with greater accuracy—enabling better outputs. Multimodal AI can fill information gaps with data from multiple formats, improving its knowledge base and how effectively it interprets and responds to user input.
Tavus’ Phoenix model offers exceptionally realistic and accurate digital twins, making it easier than ever to generate lifelike replicas without sacrificing video quality or performance.
Natural Interactions
Multimodal AI utilizes its access to multiple types of data to generate a comprehensive understanding of user needs. These models don’t just use text or speech input; they can also process data such as facial expressions and gestures to interpret engagement and needs.
Developers choose Tavus API for this very reason—with an incredibly realistic, interactive experience, Tavus’ conversational AI delivers authentic-sounding AI agents with less than a second of latency.
Contextual Understanding
More data means more context, and multimodal AI systems utilize this context to better understand concepts, phrases, and more. Multimodal AI works with NLP models, combining an NLP’s understanding of linguistic information with visual data to increase its contextual understanding.
Multimodal AI helps Tavus’ AI agents gain improved understanding of user input with advanced speech recognition, visual analysis, and conversational awareness. Tavus API’s powerful ability to generate contextual understanding helps it generate rich, natural dialogue.
More Powerful AI Technology
The ability to understand a wider variety of data enhances multimodal AI models’ understanding of many input types and their given contexts. This increased understanding and knowledge creates more powerful AI models with enhanced capabilities and performance. With the ability to understand and respond to more complex commands, multimodal AI models can improve user satisfaction.
Developers from across industries are discovering the power of Tavus’ AI video generation tools. Tavus’ cutting-edge technology only needs two minutes of video to generate a photo-realistic digital replica. End users can use these digital twins as conversational AI agents, and with Tavus’ AI tools, they can translate content into over 30 languages and personalize unlimited videos.
Challenges of Multimodal AI
AI models are constantly evolving as AI leaders work to improve their functionality and accuracy. Multimodal AI is no different, with innovators facing several challenges:
- Difficulty aligning data: Multimodal AI requires strong datasets, but building such datasets can be challenging. Each mode of data has its own features and requires different steps for cleaning, normalization, and prepping for integration with other modes. Possible solutions include using multimodal transformers to align data or autoencoders to map features of one modality onto another.
- Limited computational resources and infrastructure: Multimodal AI models need vast computational resources and specific infrastructure for training and implementation. Some AI leaders use hardware accelerators or specialized chips to optimize performance.
- Costly and time-consuming training processes: Multimodal AI models require a large amount of data for each AI architecture involved (including transformers, memory networks, and capsule networks). Possible solutions include using modular networks that are trained separately or using hierarchical multimodal networks that process data at multiple levels of abstraction.
- Ethical and privacy-related concerns: Multimodal AI’s need for vast data means models have to pull from several sources, including private data like healthcare records, social media, smartphones, and more. Possible solutions include differential privacy techniques or ethical AI frameworks and regulatory compliance.
Learn More About Multimodal AI
Still curious about multimodal AI? Check out answers to common questions about how it works, how it compares to other types of AI, and more.
What is the difference between generative AI and multimodal AI?
Generative AI can create new content, which may make it seem similar to multimodal AI. However, generative AI uses learned data to create content that is similar to existing content. For instance, it can use learned data about images or videos to generate similar images or videos.
Multimodal AI, on the other hand, uses multiple forms of data (like images, audio, video, or text) to generate a comprehensive understanding of this data and our world. It can help us make more informed decisions, and it can power incredibly realistic interactions with conversational AI, like with Tavus’ video API.
Is ChatGPT a multimodal model?
Yes, ChatGPT uses a multimodal AI model. Users can engage in vocal interactions with ChatGPT and provide voice, text, and image input. The model can switch between voice, text, and video input, analysis, and output.
What are the benefits of multimodal AI?
The benefits of multimodal AI are many. Whether you’re looking for more powerful AI technology, improved accuracy, increased contextual understanding, or more natural AI interactions, you can find what you need with multimodal models.
What is an example of multimodal AI?
One example of multimodal AI use is in self-driving cars, which need a variety of data inputs to generate a comprehensive understanding of the road and other drivers’ behavior.
AI chatbots also benefit from multimodal AI. Tavus is a great example of multimodal AI at work. Tavus API offers conversational AI digital replica agents that use multimodal AI to understand user input and provide highly accurate responses in real time.
Leverage the Power of Multimodal AI for Your Business
Multimodal AI offers many benefits—don’t miss the opportunity to optimize your AI technology and provide customers with increasingly accurate and powerful AI models.
With Tavus API, you gain access to cutting-edge multimodal AI tech that integrates with your existing apps and workflows seamlessly. Tavus’ developer-first experience and built-in security and privacy management make it a top choice for developers looking to integrate AI video generation and conversational AI into their platforms.
Let Tavus help you provide customers with easy, high-quality AI video generation tools, including tools for lip syncing, AI translation, and photo-realistic replica generation. With only two minutes of training footage, your end users can create digital twins and scale video creation to meet all their needs.
