While the range of AI use cases and potential implementations is quite versatile today, its generative capacities are among the most highly demanded ones. They are clear to end users as the interaction with GenAI tools can bring tangible and applicable results. With significant advancements made in the AI industry today, the focus is gradually shifting from traditional GenAI models that can work only with either text or images to multimodal AI. It can simultaneously rely on multiple data inputs to deliver a more accurate and valuable output.
The global generative AI market volume hit the level of $25.86 billion in 2024. Now, it is expected that it will reach the mark of over $1,005 billion by 2034, which indicates a CAGR of 44.2% during the period from 2025 to 2034.
As for the factors that drive this market growth, the increasing demand for workflow modernization remains one of the most significant. According to the latest McKinsey Global Survey on AI, 21% of companies that deployed GenAI tools managed to conduct fundamental redesigns of at least some of their workflows.
Meanwhile, Gartner predicts that 40% of all generative AI tools will be multimodal by 2027, which may greatly boost further differentiation of GenAI-powered use cases and offerings.
Now, let’s take a closer look at the peculiarities of multimodal AI models and their key differences from traditional generative AI.
What Is Multimodal AI?
This term is used to describe AI systems that can process and integrate multiple types of data such as text, images, audio, video, and sensor input. In this context, this data is also called modalities.
It’s vital to highlight that models of this kind can understand and analyze information across different formats but they do not necessarily generate new content. Multimodal generative AI is a separate subset of this category and we will focus on it further in our article.
Multimodal systems are typically powered by deep learning models that connect different types of data. As a result, these systems can understand relationships between data in several formats and take actions or provide outputs based on the results of this comprehensive analysis.
Among other core technologies that stand behind such models, we should name advanced natural language processing (NLP) and computer vision.
Key Benefits of Multimodal AI Models
Besides answering the question “What is multimodal AI?”, it is also vital to explain why its development matters.
- More human-like interaction. When we communicate with each other or interact with the world around us, we combine different senses and ways of perception, including sound, language, and signs. Multimodal AI can do practically the same. This ensures better context awareness and understanding.
- Enhanced decision-making and better accuracy. Single-modal AI can’t demonstrate an excellent understanding of complex scenarios, which may result in limited accuracy of provided responses. Thanks to the combination of multiple data types, it is possible to reduce errors in AI analysis.
- Expanded range of applications. Multimodal AI opens new possibilities for using artificial intelligence across sectors, including healthcare, education, eCommerce, transportation, manufacturing, etc.
- More inclusive and accessible AI. Such models can be used in solutions aimed at helping people with disabilities. For instance, AI can describe images, read text aloud, and navigate interfaces using voice commands. Additionally, AI can interpret sign language gestures and convert them into spoken language.
Popular Multimodal AI Models
The AI space is rapidly evolving as more and more new players are joining the game. At the current moment, we can name the following models that cemented their leading positions in this field.
GPT-4o by OpenAI
It can process text, images, audio, and vision. For example, it allows users to upload pictures and ask questions about them. The model can generate outputs in real time. The average response time is under 320 milliseconds. It is very close to natural human conversation. Today this model is one of the most popular ones used for the development of chatbots and other tools for different industries.
CLIP by OpenAI
This model called Contrastive Language-Image Pre-training combines vision and language to perform image classification. CLIP can learn to generate accurate image labels based on the content of the images. It is used for image annotation, image retrieval, and the creation of descriptions based on images.
Flamingo by DeepMind
This vision-language model can work with inputs containing videos, images, and text to provide textual responses. To get the most relevant responses, users can provide a couple of samples so that the model can learn what is expected from it.
Key Differences Between GenAI and Multimodal AI
To have a better understanding of what makes multimodal AI stay apart from traditional GenAI, it’s necessary to analyze the key aspects of their functionality and usage.
Goals
The main goal of GenAI tools is to create content in one of the formats based on the provided inputs.
Multimodal artificial intelligence systems are designed to work with content that involves multiple modalities simultaneously for a more comprehensive and interactive experience. Not all systems of this type are powered by content generation capabilities.
Complexity
GenAI models are usually simpler in data handling and architecture than multimodal AI as they focuse on a single modality.
As for multimodal systems, they need to have more advanced architectures and more sophisticated algorithms required for processing several modalities.
Data Requirements
Generative AI models typically require large amounts of data of a single type for training.
Multimodal systems need more diverse and complex datasets because they will process and learn from data across multiple modalities.
What Is Multimodal AI with Generative Capabilities?
Multimodal generative AI models can not only work with different modalities but also can generate content in different formats. In such a way, it can bridge gaps between different forms of media and offer more creative possibilities for users.
Additionally, these systems ensure more interactive user experiences. For example, when communicating with a virtual assistant powered by such technologies, people do not need to choose only one format for interaction. They can use a combination of voice, text, images, and video, while a system will generate responses in the most suitable formats.
Common Applications of Multimodal Generative AI
- Video synthesis. AI can generate videos based on text prompts or image inputs, eliminating the need for human animation or cinematography. This use case is already gaining popularity in marketing, as well as in the film and animation industry for pre-visualization.
- Multimodal chatbots. AI agents of this type can process and provide responses in different formats. Today they are used in customer service, healthcare, finance, education, real estate, and other domains.
- Digital art. AI can generate highly detailed, stylized art pieces, designs, and music. Though the ethical questions regarding the creation and further use of such content are still open, the advancements in this sphere are undisputed.
Ethical Challenges
Today there are a lot of concerns surrounding the ethical aspects of AI content generation. The use of AI models raises important questions about responsibility and authenticity in digital content creation. Is it fair to generate content with the help of AI? Who owns this content? Who is liable for any impact that it may have? These are pretty serious ethical dilemmas that require careful consideration.
Deepfakes and Misinformation
One of the most pressing concerns is the use of AI-generated content to deceive people. Deepfake technology can create highly realistic fake videos, images, and voice recordings. The advancements in this sphere make it very difficult to determine whether you have real or fabricated content in front of you.
Such content is widely used during political elections, which can cause serious reputational damage to candidates. Also, deepfakes often become a part of financial scams.
Though at the moment, there is no 100% efficient method to combat the dissemination of misinformation and fakes, the introduction of digital literacy initiatives can be a good idea. It’s worth educating people on how to identify fakes and check facts on their own.
Bias and Fairness
The quality of the data used for AI training is the key factor that will determine the fairness of AI-generated content.
As AI models are applied in different domains, the real-world consequences of the produced bias may reach impressive scales.
Today we can observe the signs of hiring discrimination when job screening systems favor certain demographics over others.
Another example is stereotypical image generation, which can reinforce racial and gender stereotypes. For example, AI systems usually depict scientists or software engineers as male, while teachers are usually shown as female.
Is it possible to reduce bias in AI-generated content? It may not be easy, but it is possible to do it. It is recommended to use diverse training datasets as well as regularly conduct bias audits on AI models.
Copyright and Intellectual Property
It’s not a secret that AI is often trained on works, like paintings, books, and music, created by real humans. Unfortunately, sometimes it may happen without proper consent from creators. These works may also be protected by copyright, which means that their creators hold exclusive rights to use and distribute them.
Therefore, there are serious legal and ethical concerns, including:
- Copyright infringement;
- Monetization of AI-generated content (Should the original creators be compensated for their contribution? And how to prove their rights for this compensation?);
- Devaluation of creators’ work (If an AI model produces works similar to what some human creators delivered, what will happen to the value of the original pieces?).
Of course, it’s not the easiest task to address these issues. However, there are some possible ways to do it:
- Implementation of copyright laws specific to AI;
- Introduction of international standards for AI training practices;
- Collaboration between AI developers, creators, and policymakers aimed at finding fair solutions.
Wrapping Up
Multimodal generative AI is the next big step in AI development. Unlike traditional AI, which focuses on just one type of content, multimodal systems offer more opportunities for creative and practical uses across various domains, including both business and art.
Nevertheless, the implementation of such systems is associated with a large number of ethical issues that remain unresolved. Addressing these challenges requires significant efforts both at the legislative level and in terms of collaboration among all industry stakeholders.
However, as new technological advancements emerge, the widespread adoption of such systems will only continue to gain momentum and the solution of the existing ethical dilemmas will become a must.
Are you also planning to launch an AI-powered tool? At Tensorway, we are always ready to share our expertise to help you achieve your business goals with the power of advanced technologies. Let’s discuss our potential cooperation!
FAQ
Is multimodal generative AI the same as multimodal AI?
Multimodal generative AI is a subset of multimodal AI. It can not only process and understand multiple data types but also generate new content in multiple modalities. Not all multimodal AI models are GenAI.
What are the examples of multimodal AI?
For instance, an AI assistant can take voice commands and recognize images, while the responses can be either spoken aloud or provided as text. Another typical use case of multimodal capacities is the description of pictures or summarization of video content.
How can multimodal AI tools be used in different industries?
There are a lot of examples across industries. To name a few, such solutions can be used in healthcare for medical imaging and diagnosis, as well as remote patient monitoring. In eCommerce, companies can implement personalized shopping assistants that rely on voice commands, text input, and images to suggest products. In the transportation industry, AI can process video feeds, sensor data, and traffic signals for self-driving cars and smart traffic management.
