Brainstorming ChatGPT Business Ideas With Billionaire Dharmesh Shah
My First Million
Reducing Concepts Down to Vectors
Vector embeddings are a way to represent concepts using numbers in multiple dimensions. In one dimension, a point on a line can be described by its distance from the origin. In two dimensions, a point is defined by its coordinates, and the distance between points can be calculated mathematically. The same applies to three dimensional space. However, in an abstract world with 1000 dimensions, any concept, such as a paragraph or a tweet, can be reduced to a point in this space using a set of 1000 numbers called a vector. The distance between vectors represents the semantic distance or the meaning-based relationship between concepts, rather than just keyword matching. This concept of vector embedding allows for measuring the similarity between concepts, even if they use different words. This approach presents a significant opportunity in AI to move away from crude keyword-based matching and enable more intelligent and nuanced search capabilities.
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