What are embeddings?
Understanding embeddings is important to know how AI products work.
Here, I will try to give basic idea about embeddings.
Embeddings are numerical representations of data.
Data can be words or phrases.
Embeddings capture their meanings and relationships in a compact form.
Embeddings do not treat words as isolated labels.
Embeddings place these words into a continuous vector space where similar words are positioned closer together.
Let me give you an example to understand:
Imagine you have a box of colored pencils.
Each pencil is unique.
But similar colors (like light blue and dark blue) are more alike than completely different ones (blue and red).
In the same way, embeddings represent words with lists of numbers so that related words (e.g., “king” and “queen”) have similar values.
These numbers reflect closeness of words in meaning.
Example of embeddings:
If ‘King’, ‘Queen’ and ‘Dog’ are represented as embeddings in 3-D space, here is how they look like:
King = [0.80,1.5,2.0]
Queen = [0.85,1.2,1.8]
Dog = [1.5,0.1,0.2]
Since King and Queen are closely related, their numbers are close to each other.
However dog is not closely related. So the numbers are not so close.
How this helps in AI?
Embeddings help AI understand language by revealing the relationships between words.
This is crucial for tasks like translation, sentiment analysis, and text summarization.
Note: I have taken example of text only. In the same way, images will also be converted to numbers based on relationship.
I will try to explain about how embeddings work in upcoming posts.
P.S: Does this help to understand ?
#embeddings #ai #testing #aiproducts #basics
