Three Approaches to AI: LLMs, Diffusion Models, and VL-JEPA

The world of artificial intelligence has exploded with innovation in recent years, giving us powerful tools that can write essays, generate stunning images, and understand complex visual scenes. But not all AI models work the same way. Let's explore three fundamentally different approaches to AI: generative Large Language Models (LLMs), diffusion models, and VL-JEPA.

Generative LLMs: Masters of Language

Large Language Models like GPT-4, Claude, and Llama represent one of the most visible faces of modern AI. These transformer-based models have revolutionized how we interact with computers through natural language.

At their core, LLMs work by predicting what comes next. Trained on massive amounts of text data, they learn the statistical patterns of language and use this knowledge to generate text one token (word or word piece) at a time. This autoregressive approach means each new word is generated based on all the words that came before it.

What makes LLMs particularly powerful is their ability to understand context and maintain coherence over long stretches of text. They excel not just at generation, but at understanding, reasoning, and following complex instructions. However, they're fundamentally limited to working in the discrete space of language tokens.

Diffusion Models: Artists in Noise

While LLMs conquered text, diffusion models took the image generation world by storm. Models like DALL-E 2, Midjourney, and Stable Diffusion can create photorealistic images from text descriptions, and they do it through a fascinating process.

Diffusion models learn by studying how to reverse corruption. During training, they watch as random noise is gradually added to real images until those images become pure static. The model then learns to run this process backward—starting with random noise and progressively refining it into coherent, detailed images.

This iterative denoising process typically takes dozens or hundreds of steps, with the model making small improvements at each step. Unlike LLMs that work with discrete tokens, diffusion models operate in continuous space, directly manipulating pixel values or latent representations. This makes them ideal for generating visual content, though the iterative process can be computationally expensive.

VL-JEPA: A New Paradigm

Meta's Vision-Language Joint Embedding Predictive Architecture (VL-JEPA) represents a fundamentally different philosophy, championed by AI pioneer Yann LeCun. While LLMs and diffusion models focus on generation, JEPA focuses on understanding.

The key innovation is where prediction happens. Rather than predicting raw pixels or text tokens, JEPA predicts in abstract representation space. It learns to fill in missing or masked information not by reconstructing every detail, but by predicting high-level features and relationships.

Think of it this way: if you're shown half a photo of a cat, a diffusion model would try to reconstruct every whisker and fur strand in the hidden portion. JEPA, instead, would predict the abstract concept—"there's probably more cat here, in this orientation, with these characteristics"—without worrying about exact pixel values.

This approach has several potential advantages. It's more computationally efficient, as it doesn't need to model every low-level detail. It may also lead to better world models—internal representations of how the world works—because it focuses on meaningful structure rather than surface appearance.

The Bottom Line

These three approaches represent different philosophies in AI development. LLMs master sequential, discrete language generation. Diffusion models excel at creating high-quality continuous data through iterative refinement. VL-JEPA pursues efficient learning through abstract prediction.