What is multimodal artificial intelligence?

Multimodal AI distinguishes itself from traditional single-modal AI primarily by its ability to process data from multiple sources. While single-modal AI, like financial AI, specializes in a specific task using a single type of data (e.g., financial data), multimodal AI integrates data from various modalities, such as video, images, speech, sound, and text.

This allows for more comprehensive and nuanced analyses, mirroring human perception. For instance, a multimodal AI might analyze a video, accompanying audio, and written transcripts to understand a specific event better.

Multimodal AI integrates and processes data from various modalities, such as text, images, and audio, within a unified system. Each modality is encoded into high-dimensional representations using specialized models, preserving the unique features of each data type. These representations are then mapped into a shared latent space, enabling the system to align and relate information across modalities.

Integration often relies on cross-attention mechanisms, allowing features from one modality to influence the processing of another. This enables the model to capture relationships and dependencies between modalities, supporting tasks that require understanding and combining data from multiple sources, such as generating responses based on both text and images.

Training multimodal AI involves end-to-end optimization on paired multimodal datasets, ensuring effective learning of relationships between modalities. Loss functions encourage alignment and interaction, while the shared latent space enables seamless cross-modal understanding. The result is a unified model capable of handling complex, cross-modal tasks.

Generative AI and and multimodal AI are two powerful branches of artificial intelligence, each with distinct focuses but significant overlap.

• Generative AI is task-oriented, aiming to create new content—such as text, images, music, or videos—by learning patterns from existing data. It excels in tasks like content creation, image synthesis, and language generation.
• Multimodal AI, on the other hand, is domain-oriented. It processes and integrates information from multiple modalities, including text, images, audio, and video. This allows it to handle complex tasks that require understanding and generating content across diverse formats simultaneously.

The two intersect because generative AI can operate within the multimodal domain, enabling models to create cross-modal outputs (e.g., generating images from text or captions from videos). While generative AI defines what is done (generation), multimodal AI defines the context in which it operates (handling multiple data types).

OpenAI recently released GPT-4o, a multimodal model that offers the same impressive performance:

• Generate unique images based on text descriptions or analyze existing images to extract information.
• Process and understand data from various sources, such as text, images, and audio.
• Engage in natural-sounding conversations with the AI, making getting information or completing tasks easier.

But there are other examples of using multimodal AI that are widely used nowadays:

• Chatbots — AI-powered agents can understand and respond to customer inquiries through text or voice, often combining information from multiple sources ( customer history, product knowledge).
• Sentiment analysis — A company uses multimodal AI to analyze customer feedback from social media, reviews, and surveys, combining text, images, and audio data to understand overall customer sentiment.
• Traffic light recognition — A self-driving car uses a multimodal AI system to recognize a traffic light based on its color, shape, and position relative to the car's location.
• Language learning — A language learner practices speaking with a multimodal AI tutor, who provides feedback on pronunciation, grammar, and vocabulary based on audio and visual inputs.