We’re going to break down the math, expose the quirks, and show you how to turn those vectors into meaning using "Text Embeddings"
Language is messy, chaotic, and beautiful. Mathematics is precise, cold, and rigid. Today we're going to find out how those two can shake hands.
+ why is text embeddings important
+ identify a connect with something they care about
+ Add an explanation (introduction character telling) before the embedddings map. Walkthrough, tell people to try things
LET'S TRAVEL INTO THE SPACE OF MEANINGS!!
LET'S TRAVEL INTO THE SPACE OF MEANINGS!!
If every word in the English language was a star in the night sky, text embedding is the telescope that reveals the constellations connecting them.
Tokens & Embeddings: Text's Secret Code
Contents
- I ❤ strawberries
- Glitches & Quirks
- Vector Playground
- Next-Gen Magic
- Exit Ticket
01
I ❤ strawberries
How Does Your Text Become Math?
Let's use some "magic" to reveal the first step of how AI understands language.
Original Text
I ❤ strawberries
Tokenization ↓
Token ID Sequence
[40, 1037, 21020, 219, 23633, 1012]
Every word, symbol, and even typo must be converted to numbers before the model can start "thinking."
Tokenization Showdown: Whole Words vs. Characters
Word-based Tokenization
Splits by spaces and punctuation—simple and intuitive.
Drawback: Huge vocabulary, can't handle new words or typos.
Encountering "Covidlicious" results in [UNK] (Unknown).
Character-based Tokenization
Breaks text into individual characters, extremely small vocabulary.
Drawback: Sequences too long, loses semantics, low efficiency.
"unbelievable" is split into 12 characters, making it hard for the model to understand the overall meaning.
Subword Tokenization: The key to intelligence
Balances efficiency and meaning by breaking words into meaningful "building blocks."
un + believ + able = unbelievable
common prefix + root + common suffix = complete word
This way, the vocabulary is small, fewer unknown words, and it can understand that "unbelievable" is composed of "un + believable."
02
Glitches & Quirks
The Strawberry Mystery: Why Can't AI Count?
Ask AI: "How many 'r's are in 'strawberry'?"
Human Perspective:
s-t-r-a-w-b-e-r-r-y, at a glance, 3.
AI Perspective (Tokenized):
['str', 'aw', 'berry'], original letter information is lost, can only guess.
Tokenization Information Loss
The Challenge of Internet Slang "Tokenization"
How do tokenizers handle ever-evolving internet language?
😊 Emoji & Expressions
"🌟💀 slayyy 🖌️" gets split into ['🌟', '💀', 'slay', 'yy', '🖌️'].
If an emoji is a commonly used "word," it gets its own ID.
⌨️ Typos & New Words
"Covidlicious" might be split into ['Covid', 'licious'].
Subword models can "understand" and process newly coined words.
Platform Slang
"yyds", "amazing" etc.—if popular enough, they're treated as a complete token in new model training.
03
Vector Playground
From Words to Coordinates: Entering Vector Space
Embedding encodes word meaning as an "address" in high-dimensional space—a vector.
King → Royal, Male → [0.2, -0.5, 0.8, ..., 0.1]
In this space, words with similar meanings are closer together, and models understand relationships by calculating "distance."
The Magic of Embeddings: Word Vector Arithmetic
King - Man + Woman = Queen
Embeddings not only encode word meaning but also relationships.
Through vector addition and subtraction, we can explore analogies and logic in language.
The Birth of Embeddings: Ants Moving House
Imagine a colony of ants (the model) crawling through Wikipedia.
- Collect neighbors: Each ant collects words around the target word.
- Mutual attraction: Words that often appear together have vectors that "attract each other," getting closer in space.
- Form a map: After billions of crawls, a "semantic map" reflecting real language relationships is formed.
04
Next-Gen Magic
Upgraded Embeddings: Context-Aware
Early embeddings were static—one word, one vector.
Modern models (like BERT) can dynamically adjust word meaning based on context.
"I need to open an account at the bank."
→ Vector points to "financial institution"
"We're having a picnic by the bank."
→ Vector points to "riverbank"
The same word "bank" gets different vector representations in different contexts, allowing the model to truly understand its meaning.
Embeddings Beyond Text: Going Cross-Domain
Embedding technology has expanded beyond text—any data can be encoded as vectors.
🖼️ Images
Through models like CLIP, images and text can be mapped to the same vector space, enabling "search images with text."
🎵 Audio
Spotify uses it for music recommendations.
🛒 Products
Amazon uses it for product recommendations.
05
Takeaways
Interactive Time: Create Your Own Word Vector Equation
Challenge: Try to find your own word vector relationship!
$$\text{Sushi} - \text{Japan} + \text{Italy} = \text{Pizza}$$
Principle: (Food - Origin Country) + New Origin Country = New Food
This demonstrates how embeddings encode complex relationships.
Summary: Tokenize, Embed, Understand
This is the secret trilogy of how AI understands language:
-
Tokenize
Break text into small chunks the model can process. -
Embed
Convert tokens into vectors in high-dimensional space. -
Understand
Understand semantics through vector operations and model reasoning.
Next time you see "strawberries," you'll know it's just a string of mysterious code in the AI world.