The video explains that AI perceives the world by converting inputs like images and text into numerical data known as embeddings, which are then mapped into a vector space based on their similarities and differences. It emphasizes that AI’s “intelligence” is based on mathematical relationships and patterns rather than genuine understanding, highlighting the fundamental differences between AI perception and human experience.
The video explores how artificial intelligence perceives the world fundamentally differently from humans. Unlike humans, who experience colors, shapes, and meanings, AI interprets inputs as numerical data. When presented with an image, a sentence, or even a voice, AI converts these inputs into what is known as an “embedding.” This embedding is essentially a large list of numbers that encapsulates the essence of the input, allowing AI to process and analyze it.
Once the input is transformed into an embedding, AI maps it into a vector space. In this space, similar items are positioned closer together, while dissimilar items are placed further apart. For example, a dog and a wolf would be located near each other in this space due to their similarities, while a toaster and a tiger would be positioned far apart. This spatial arrangement is crucial for how AI models recognize objects and link ideas, functioning more like a geometric representation rather than a traditional understanding.
The video emphasizes that AI’s processing is not based on comprehension or context but rather on mathematical relationships and patterns. It highlights that AI does not “understand” in the human sense; instead, it operates through geometry and numerical relationships. This distinction is vital for grasping how AI functions, as it builds its own version of reality based on the proximity of data points rather than any inherent meaning.
Furthermore, the video points out that when AI successfully identifies a vibe or accurately tags an image, it is not a result of magical intelligence but rather a navigation through a complex and abstract map of numerical relationships. This process showcases the capabilities of AI in recognizing patterns and making connections, which can often appear intelligent to human observers.
In conclusion, the video sheds light on the underlying mechanics of AI perception, illustrating that its “intelligence” is rooted in mathematical computations rather than genuine understanding. By framing AI’s capabilities in terms of geometry and embeddings, it provides a clearer picture of how AI interacts with the world, emphasizing the importance of recognizing the limitations and differences in AI’s perception compared to human experience.