CRIN←◎ BeginnerHow AI Understands MeaningPress enter or space to select a node. You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.
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How AI Understands Meaning
Token IDs are just addresses. The real meaning lives in embeddings — long lists of numbers where similar words sit close together, and relationships between words become directions you can calculate.
Course: Beginner.
This lesson covers 5 concepts: Token IDs In, The Meaning Table, A Word's Vector, Similar Words Are Close, Words Have Relationships.
Token IDs In
These are the token IDs from tokenization — raw integers. They identify each word piece but carry no meaning on their own.
Token IDs are just addresses. The actual meaning lives in what those addresses point to — vectors in the embedding table.
Like a library catalogue number — "BD 1234" means nothing until you walk to that shelf and open the actual book.
3956 = "cat", 38617 = "kitten", 5679 = "dog", 39937 = "puppy". Four integers. No meaning — yet.
The Meaning Table
A giant lookup table built during training — one row per vocabulary entry, each row packed with numbers encoding that word's meaning.
This table is where AI's understanding of language lives — everything it learned from billions of sentences is compressed into these numbers.
Imagine a spreadsheet with 100,000 rows — one per word — and each row has 4,000 numbers that somehow capture the word's meaning. That is the embedding table.
A table with 100,000 rows and 4,096 columns. Row 3956 is "cat". Row 5679 is "dog". Their rows are similar. Row 7723 for "inflation" looks nothing like either.
A Word's Vector
Each token ID retrieves one row from the table — a vector of hundreds of numbers. This is the word's meaning fingerprint in AI's mathematical space.
This vector is the only representation of meaning the model works with. Every subsequent step — attention, reasoning, generation — builds entirely on these vectors.
Every word gets a unique fingerprint made of thousands of numbers. "Cat" has one fingerprint. "Kitten" has a very similar one. "Democracy" has a completely different one.
"Cat" becomes a list of 4,096 numbers. Some encode that it is an animal. Some that it is small. Some that it purrs. Nobody labelled any of them — AI discovered the structure itself.
Similar Words Are Close
"Cat" and "kitten" have vectors pointing in nearly the same direction — they are neighbours in meaning-space. "Cat" and "democracy" point in completely different directions.
Proximity in vector space is how AI knows that words are related — even synonyms it has never directly compared. Similarity is pure geometry.
Imagine a city where similar ideas live on the same street. Animals on one street, emotions on another, technology on a third. AI measures how far apart any two words are on this map.
cosine("cat","kitten") = 0.89. cosine("cat","dog") = 0.77. cosine("cat","car") = 0.18. cosine("cat","democracy") = 0.04. The numbers match human intuition.
Words Have Relationships
Take the words you just saw. Subtract what makes a kitten different from a cat, then add a puppy — and you land almost exactly on "dog". The relationship is the same direction.
This is what lets AI complete analogies, fill in blanks, and transfer knowledge — because relationships are consistent geometric directions across the whole vocabulary.
The relationship between a cat and its baby is the same mathematical step as the relationship between a dog and its baby. The geometry is consistent across the whole language.
cat − kitten + puppy ≈ dog (0.94). The "baby animal" direction is the same for cats and dogs. AI learned this without being told — purely from reading billions of sentences.