Understanding Multimodal Models: The Future of AI with GPT-4 and Meta’s Chameleon

In recent weeks, the AI world has seen major advancements with the release of groundbreaking multimodal models such as GPT-4.0 and Meta’s open-source alternative, Chameleon. Multimodal models are the future of AI, but what exactly do they do, and why are they so important?

A multimodal model is an AI system designed to process and understand different types of information, or “modes,” such as text, images, audio, and video. Each type of input is called a modality, and when a model works with multiple modalities, it’s considered “Multimodal.” Models that handle just one type of input, like GPT-3 (text only), are called unimodal.

With Multimodal models, we can feed images, text, and audio together to get a more comprehensive response without transforming these inputs beforehand. For instance, GPT-4.0 can accept both text and images and process them simultaneously. This can be useful in real-world applications, such as describing a scene from a movie to someone who cannot see it. To fully convey the scene, you’d need to describe not just the text (dialogue) but also visual elements like emotions, scenery, and mood. A multimodal model can process all these inputs together, synchronizing the text, visual frames, and even sounds.

To do this, multimodal models convert different types of data into a numerical form called “encoding.” Whether it’s an image, sound, or text, everything is turned into a set of numbers representing the key features of that input. This encoding compresses the information into a “latent space” where it can be processed by the model.

For example, a text encoder might convert a sentence into a sequence of numbers, while an image encoder does the same for a picture. Once the model processes these encoded numbers, it uses a decoder to transform the compressed data back into the output form we need, such as a generated sentence or image. This is the basic framework for models like DALL-E and MidJourney, which are multimodal models that generate images from text descriptions.

What’s revolutionary about Chameleon, Meta’s new model, is that it uses a unified encoding mechanism for both text and images. Instead of having separate processes for text and image encoding, Chameleon handles both within the same framework. This means the model can better understand and generate text and images in a more integrated way. Unlike previous models, Chameleon even uses a unified decoding mechanism — it processes everything in one seamless system, from encoding to decoding. This is what Meta calls a “fusion model.”

One key challenge multimodal models face is aligning different types of data in the latent space. For instance, text and images are inherently different, and making sure they’re aligned correctly is tricky. In Chameleon, both text and image data are encoded into a shared space, but they must remain perfectly synchronized. Imagine describing a movie scene: the text (like dialogue) and the image frames need to match exactly for the AI to understand and generate accurate descriptions.

Meta overcame these alignment challenges by refining how multimodal transformers work.

Transformers are the backbone of modern AI models like GPT-4. They use something called an attention mechanism, which allows the model to focus on important parts of the input. For example, when processing a movie scene, the model might “ask” specific questions (queries) like “who is in the scene?” and focus on relevant details (keys and values) such as the appearance and actions of characters.

A common problem in transformers is that the attention mechanism can become unstable over time, especially when processing large amounts of data.

This instability is caused by something called the softmax function, which transforms the model’s scores into probabilities. Over time, the input values fed into the softmax can grow too large, leading to instability. To fix this, Meta introduced key and query normalization (QKNorm) in Chameleon. This change ensures that the inputs to the softmax function remain stable, preventing the model from becoming unreliable.

Another key improvement in Chameleon’s architecture is the reordering of normalization steps. In traditional models like GPT-4 or LLaMA, normalization is applied after computing attention. In Chameleon, Meta moved the normalization step earlier in the process, which leads to more stable and efficient training.

Now that we’ve covered how multimodal models like Chameleon work, let’s talk about where they’re useful:

Multimodal models shine in tasks that involve both visual and textual information, such as :

• answering questions about images
• generating video summaries.

in the recent GPT-4 demo by OpenAI, the model was able to take visual input (like a photo) and answer questions about it. This requires a deep understanding of both the image and the accompanying text.

Chameleon excels in these types of tasks because it uses the same encoding representation for both text and images. These are the advantages:

• allows the model to learn relationships between different types of data more efficiently.
• leading to better reasoning and more accurate results. (Because Chameleon uses the same system for encoding and decoding, it’s more consistent and reliable than previous models that needed separate processes for each modality).

The advancements in multimodal AI, like those in Chameleon, are setting the stage for more sophisticated AI applications. These models are not just about processing text or generating images, they are about understanding and reasoning across different types of information. This could revolutionize how we interact with AI in fields like virtual assistants, education, healthcare, and entertainment.

If you’re interested in learning more about how to build and use large language models (LLMs), stay tuned for more AI updates as we dive deeper into the world of multimodal models and beyond.