Why ChatGPT Works: Inside Transformers, RLHF, and AI’s Latest Breakthroughs

ChatGPT has rapidly gained 100 million users, demonstrating capabilities from writing papers to coding.

Its versatility has led experts to explore its underlying technology, as highlighted in the book “Artificial Intelligence: A Modern Approach (4th edition).”

The book, authored by Stuart Russell, a UC Berkeley professor and AI pioneer, provides an in‑depth analysis of recent breakthroughs such as AlphaGo, AlphaFold and ChatGPT.

Chapter 2 explains that ChatGPT’s power stems from the Transformer architecture, a generative pre‑trained model that enables parallel data processing and mitigates issues like gradient vanishing that plagued earlier recurrent networks.

Transformers model the probability distribution of token sequences, allowing the system to predict the next word based on previously generated context.

Training combines supervised fine‑tuning, a reward model built from human‑annotated comparisons, and Proximal Policy Optimization (PPO) reinforcement learning, a pipeline known as RLHF.

The three‑stage process—supervised policy training, reward‑model training, and PPO fine‑tuning—iteratively improves response quality.

Beyond the core architecture, the book covers convolutional networks, recurrent neural networks, machine learning, deep learning, language models, self‑supervised learning, GANs, and the philosophical, ethical, and safety considerations of AI.

Overall, the text serves as a comprehensive guide to the technical foundations and future directions of modern artificial intelligence.