Imagine a photograph of Earth taken from the International Space Station: a breathtaking view of continents, oceans, and forests spinning in the vast darkness of space. The photograph encapsulates the entirety of the planet, but from 250 miles above, it is only a model — a small sample of reality. It offers a simplified, idealized version of Earth, capturing its beauty and complexity from a distance, but it doesn't reveal the full depth of life and experience unfolding beneath it. Similarly, AI models provide us with snapshots of reality, designed to simulate specific tasks, yet they are limited in scope and cannot encompass the entirety of the world they represent.
AI "thinks" by embedding data as vectors — arrows that have two important features: direction and magnitude. The direction of the arrow shows which way something is pointing, and the magnitude tells us how "strong" or significant it is in that direction. These vectors help organize complex data like words, images, or sounds. By assigning each piece of information a direction and magnitude, AI can compare new information to a model's existing vectors to draw insights and make predictions about the reality it represents.
It's easy to get caught up in the excitement of the next big breakthrough, the allure of a model that seems to be able to do amazing things; however, it's essential to remember that artificial intelligence is always a model, a limited perspective of a much larger, more intricate reality. This understanding is crucial in a time when AI is being increasingly integrated into society. It may be good at specific tasks, but it can't replace the richness and fullness of human experience, nor can it truly capture the complexity of the world in the same way that a living observer can.
