A Guide to Landmark AI Papers (2013-2023)

This post is a fast, high-signal tour through landmark AI papers that shaped modern machine learning. It starts with the architectural shifts that redefined deep learning, then moves into generation, scaling, and reasoning. You will see how breakthroughs in language, vision, reinforcement learning, and multimodal systems connect. The goal is not to overwhelm with trivia, but to give you a durable mental map of what mattered and why. Use the index to jump by topic, or read top-to-bottom as a compact research timeline. At the end, there is a distilled concept list you can reuse as a study and interview checklist.