How AI Converts Words into Vectors 📚

Ever wondered how a computer learns the meaning of words like king and queen? How does an AI know that king is more related to queen than it is to apple? This video demystifies one of the most fundamental concepts in modern AI, vector embeddings. We explain exactly how a machine assigns a special list of numbers, or a vector, to every single word.

Join us at blackboardAI as we break down this complex topic into simple, easy to understand steps. We'll draw it all out on our digital blackboard.

In this explanation, you will learn:
What one-hot encoding is and why it's an inefficient way to represent words.
The core concept of the Embedding Matrix, a giant lookup table that modern AI models use.
The surprisingly simple process of how a token or word is assigned its vector. It's just a lookup.
How these vectors, which start as random numbers, become smart and meaningful through the training process.
We visualize how models use feedback from making predictions to adjust these vectors, causing words with similar contexts to end up mathematically close to each other. This is the magic behind how AI understands language.


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