Compression: AI Mental Model #5

============================================================ 📓 Free visual lecture notes for this episode: https://vizuaraai.github.io/great-mental-models-of-ai/lecture-05-compression.html ============================================================ When something in AI becomes too big, the answer is often not to make the model brute-force its way through it. The answer is to find the smaller space hiding inside it. This is Lecture 5 of The Great Mental Models of Artificial Intelligence. In this episode, we explore Compression: the idea that real-world data, images, matrices, memory caches, and even model updates often contain far fewer true degrees of freedom than they appear to. The move is simple: squeeze something large into a compact latent space, work there, and project it back up. The surprising part is how little information is lost. ============================================================ In this lecture, we look at: (1) The narrow-waist idea behind compression (2) Why real-world data is lower-dimensional than it looks (3) Byte-pair encoding and shorter codes for text (4) Stable Diffusion and why image generation happens in latent space (5) DeepSeek MLA and compressing the KV cache (6) LoRA and low-rank adaptation (7) World Models and agents learning inside compressed dreams (8) Why many AI breakthroughs come from finding the smaller hidden space ============================================================ #ArtificialIntelligence #MachineLearning #DeepLearning #Compression #LatentSpace #StableDiffusion #LoRA #DeepSeek #Vizuara