Generalizing AI as a Compression Algorithm

Generalizing AI as a Compression Algorithm

The Black Box Perspective

Imagine AI as a black box. You feed something in, and something comes out. It’s that simple on the surface — input goes in, output emerges. But what’s happening inside that box is far more fascinating than it first appears.

Think of this relationship as a massive map where each possible input connects to one or more outputs. Every question you could ask, every image you could show, every piece of text you could provide — all mapped to their corresponding responses, classifications, or transformations.

Here’s the crucial insight: since both inputs and outputs have maximum possible sizes in any practical system, the total number of possible input-output combinations is finite. Large, yes — astronomically large — but finite nonetheless. Every possible conversation, every possible image recognition task, every possible translation could theoretically be pre-computed and stored in one giant lookup table.