Learn More: The Illustrated GPT-2 (Jay Alammar)
Core Concepts
Transformers
Transformers are the foundation of most modern generative AI models, including GPT. They were introduced in 2017 in the groundbreaking paper Attention is All You Need. Unlike earlier neural networks that processed information sequentially (one word at a time), transformers use a mechanism called self-attention to look at all words in a sentence simultaneously. This allows them to understand context more deeply—for example, distinguishing between bank as a financial institution vs. the side of a river.
Transformers are trained to predict the next word in a sentence, given all the previous ones. When scaled up and trained on massive datasets, they can generate paragraphs of text, write code, answer questions, or summarize articles—all by learning statistical patterns in language.
For a visual, intuitive explanation, see The Illustrated GPT-2 by Jay Alammar.
Diffusion Models
Diffusion models are behind many image and video generation tools (like DALL·E 2 and Stable Diffusion). These models start with a completely noisy image—like TV static—and gradually remove the noise in a step-by-step process to reveal a clean, realistic image that matches a given text prompt (e.g., a red panda riding a skateboard).
They are trained to do the opposite of adding noise: to recover the original image. During training, the model sees many images gradually noised, learns how to reverse that process, and finally can generate images from scratch by starting with noise.