Multimodal Models in Generative AI

Consider how you experience the world. You don’t rely only on your eyes: you’re likely seeing, hearing, smelling, and feeling things all at once. Humans naturally combine all five senses to build a rich understanding of what’s happening.

AI, however, was originally built to handle only one type of input at a time: either text or images. That’s called unimodal AI. However, the real world isn’t unimodal, so AI is now shifting toward multimodal systems that can integrate multiple types of information simultaneously.

Multimodal AI is like teaching AI to be more like us: to understand the world by simultaneously processing information from multiple data types. Just like we use all our senses, multimodal AI uses different senses of data to get a more complete and intelligent understanding.

What are modalities?

In AI, a modality is a specific type of data or input: a way information is represented.

For humans, modalities are our senses: sight, sound, touch, smell, and taste. For AI, modalities are data types it can process, such as:

• Visual: images, photos, drawings, videos

• Auditory: speech, environmental sounds, music

• Textual: documents, articles, web pages, social media posts, code

In other applications, you might also see:

• Sensor data: Temperature, pressure, GPS, lidar, radar

• Biological signals: EEG, ECG, and other medical signals

Each modality offers a different view of the same thing. For example, a photo of a cat (visual) and the sentence “This is a cat” (text) describe the same object in different ways. Multimodal AI learns to understand and combine these different perspectives.

Why multimodal AI matters

Why not just stick with AI that handles one thing at a time (like only text or only images)? Because combining modalities makes AI much more powerful.

• Richer understanding:
  Watching a movie on mute gives you only part of the story. Add dialogue, music, and sound effects, and the meaning becomes much clearer. Similarly, multimodal AI can understand situations better by combining visual, audio, and text inputs.

• More robust:
  When one sense is unreliable (like vision in fog), you rely on others (like hearing or touch). Multimodal AI does the same—if one data source is noisy or missing, it can rely on others. Example: speech recognition that also reads lip movements in a video.

• More accurate decisions:
  Doctors don’t diagnose from a single test. They use scans, lab results, history, and symptoms together. Multimodal AI mirrors this, combining different data types to make stronger, more reliable predictions.

• More natural interaction:
  Humans are multimodal by default. Building AI that can process and blend multiple modalities makes it feel more intuitive and better aligned with how the real world works.

In short, multimodal AI isn’t just “more data”: it’s about synergy. By combining modalities, the whole system becomes smarter than any single part on its own.

Multimodal AI in action

To understand the power of multimodal AI, let’s look at some real-world examples of how it’s being used:

• Image captioning: Imagine an AI that can look at any image you give and automatically generate a descriptive text caption explaining what’s in the picture. This is image captioning, a classic example of multimodal AI combining visual (image) and textual data. The AI needs to see the objects, scenes, and actions in the image and then write a coherent and relevant sentence describing them.

• Visual question answering (VQA): This is about creating AI that can describe and answer questions about an image. You give the AI an image and a question in text form, and the AI has to look at the image, read and understand the question, and then reason to find the answer within the image and provide it in text.

• Sentiment analysis from video: Want to know how someone feels in a video? Unimodal sentiment analysis might only consider the words being said. But multimodal AI can do much better! Multimodal sentiment analysis can get a much more accurate and nuanced understanding of emotions expressed in a video by combining the following:

  • Facial expressions (visual): Are they smiling, frowning, etc.?

  • Tone of voice (auditory): Are they speaking in a happy, sad, or angry tone?

  • Spoken words (textual): What are they saying?

These are just a few examples. Multimodal AI is being applied in many more areas, and its potential is growing rapidly!

How multimodal AI works

Now, let’s examine how multimodal AI works. To make this easier to understand, we’ll use Google Gemini as a real-world example of a powerful multimodal AI model.

Gemini’s brain: Transformer decoders

At its core, Gemini is built using an enhanced transformer decoder architecture designed to handle multiple data types—text, images, audio, and video—all within a single unified model. While it uses standard transformer blocks (with self-attention and feed-forward layers), it also includes several key innovations to boost efficiency and scale. Unlike some older AI models that might process images and text separately and try to combine them later, Gemini’s transformer decoder is designed to integrate different types of information from the very beginning. It’s like having a brain built from the ground up to think in multiple senses simultaneously.

At the heart of Gemini lies the transformer decoder architecture. You might recall that transformers were originally designed to handle sequential data effectively, and Gemini leverages this strength to work with text, images, audio, and even video. One key element is its enormous context length—Gemini can handle up to 32,000 tokens simultaneously.

Imagine being able to read a whole book or watch a long video segment in one go; that’s the power of such a large context window. To make this efficient, Gemini uses multi-query attention—a streamlined version of traditional multi-head attention. Instead of each attention head computing its keys and values, all heads share the same ones while generating queries. This design dramatically reduces the computational load and speeds up processing, which is essential when dealing with massive data.

Multimodal processing

Gemini’s power isn’t just that it can handle different data types, it’s how it learns them together.

• Joint training:
  Gemini is trained on text, images, audio, and video at the same time, not as separate models stitched together. This helps it learn the relationships between modalities, the way a cook learns how flavors work together rather than studying each ingredient alone.

• Interleaved inputs:
  You can feed Gemini mixed sequences of text, images, audio, or video in any order. It treats them as one coherent stream, much like how we naturally process sights, sounds, and words together.

• Variable resolution:
  Gemini can “zoom in” on complex parts of an image and use fewer resources on simpler areas. It dynamically adjusts its attention to where detail matters most.

• Native image generation:
  It can produce images directly using discrete image tokens—not as an add-on—making image generation a built-in part of its language.

• Direct audio input:
  Gemini processes raw audio waveforms instead of relying only on transcripts. This allows it to capture tone, emotion, background sounds, and other nuances beyond words.

In short, Gemini doesn’t just have multiple senses: it knows how to use them together intelligently.

Encoding modalities

When encoding different data types, Gemini builds on earlier breakthroughs while introducing its twists. Let’s zoom in on how Gemini converts different modalities into a format it can understand:

• Visual encoding (images and video)
  Gemini converts images into visual tokens (like words for pictures). These are then mixed with text tokens so it can reason over both together, building on models like Flamingo, CoCa, and PaLI.

• Video as frame sequences
  Videos are broken into sequences of frames—snapshots over time. Gemini samples key frames instead of every single one, so it can understand motion and events efficiently.

• Audio as signals
  For audio, Gemini uses features from Google’s Universal Speech Model (USM), which are extracted directly from raw audio. This lets it capture not just words, but tone, emotion, and background sounds that would be lost in plain text transcripts.

Post-training

Training Gemini isn’t just about showing it tons of data. There’s also a crucial post-training phase to make it useful and aligned with what we want AI to do:

• Supervised fine-tuning (SFT)
  Gemini is trained on many prompts paired with ideal answers. This teaches it to:

  • Understand what users are asking

  • Follow instructions precisely

  • Produce helpful, well-structured responses

• Reward model (RM) training
  A separate model is trained to score Gemini’s answers. Humans compare and rate responses on qualities like usefulness, safety, and factual accuracy. The RM learns to predict which responses people prefer.

• Reinforcement learning from human feedback (RLHF)
  Gemini generates answers, the RM scores them, and Gemini updates its behavior to get higher scores. This loop helps align its outputs with human values and preferences.

• Capability-specific tuning
  Finally, Gemini is further tuned for particular skills, like complex instruction following, tool and code use, multilingual support, and advanced multimodal abilities.

Reasoning across modalities

One of the most impressive aspects of Gemini is its cross-modal reasoning. Because it’s been trained on a diverse, interleaved set of data types, it can make connections between different forms of information. For example, Gemini can look at an infographic and describe its contents in text, generate a code snippet that rearranges its subplots, or even answer questions about the visual data in multiple languages. This ability to solve problems—whether understanding handwritten notes, reasoning about video content, or processing audio cues—demonstrates how Gemini integrates multiple modalities into a unified reasoning process.

The future of multimodal AI

Multimodal AI is not just a trend but a fundamental shift in building intelligent systems. It’s a move toward creating AI that can interact with the world and us more naturally, intuitively, and human-likely.

The future of multimodal AI looks incredibly promising and is set to redefine how we interact with technology. As models evolve, we can expect them to integrate an even broader range of data types—combining text, images, audio, and video, potentially sensor data, haptic feedback, and more—to create richer, more nuanced representations of the world. This means our devices will understand our commands more contextually, enabling more natural interactions in areas like augmented reality, robotics, and smart home systems.