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 systems begin by training individual neural networks on specific data types (e.g., text, images). Recurrent neural networks (RNNs) are often used for text, while convolutional neural networks (CNNs) are common for images.

To process data, these systems:

1. Encode — Unimodal encoders process each data type separately (e.g., text encoder for text, image encoder for images).
2. Fuse — A fusion network combines the extracted features from different modalities into a unified representation using techniques like attention mechanisms or concatenation.
3. Classify — A classifier analyzes the fused representation to make predictions or assign the input to a specific category.

Generative AI and multimodal AI are powerful types of artificial intelligence but serve different purposes.

• Generative AI focuses on creating new content based on patterns learned from existing data. It can generate text, images, music, and more.
• Multimodal AI can process and understand information from multiple sources or modalities, such as text, images, audio, and video. It’s designed to handle and generate content in various formats simultaneously.

In many cases, generative AI is a component of multimodal AI. For example, a multimodal AI system that can generate captions for images or videos might use generative AI to create the text.

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.