One Sunday afternoon, Mom walked into my room, holding a crossword puzzle.

Mom: “You work with these AI things, right? Tell me — how does a machine even know what a word means?”

I smiled.

“Alright, Mom. Let’s go on a little treasure hunt.”

The Mysterious Map (Vector Embeddings)

“Imagine,” I began, “that every word in the world is a house in a huge city called Word Land.

Some houses are close to each other - like cat and kitten - because they mean similar things.

Others are far apart - like volcano and sandwich.

Now, we can’t just write the address as ‘No. 7, Cat Street.’

Instead, we draw a treasure map where every word becomes a dot in a secret 300-dimensional space.

The closer two dots are, the more alike their meanings.

That treasure map is called a vector embedding.

It’s like GPS for words - except instead of north, south, east, west, you have 300 invisible directions that describe meaning.”

The Twist — Words in Motion (Positional Encoding)

“But here’s the thing, Mom: if the words start moving around on the map, we’ll get lost.

So we give each word a tiny magical compass that tells us where it is in the sentence.

That’s called positional encoding - it keeps the meaning of ‘The cat chased the dog’ different from ‘The dog chased the cat’.”

The Gossip Network (Self-Attention)

“Once we know where the words are, they start gossiping.

Every word whispers to every other word:

‘Hey, cake, did you hear delicious talking about you?’

‘Yes, oven mentioned me too.’

This gossip circle is called self-attention - each word decides how much it should care about the others based on their meaning on the map.”

The Party Committee (Multi-Head Attention)

“But Mom, one gossip group is never enough.

We have multiple little gossip committees - one talks about flavors, one about shapes, one about emotions - and each gives its opinion.

That’s multi-head attention: several ways of looking at the same treasure map.”

The Vote Counter (Softmax)

“Finally, we have to turn all the gossip into a decision.

Softmax is like counting the votes and saying:

‘Alright, for the word cake, 60% of your meaning comes from delicious, 30% from oven, and 10% from birthday.’”

The Payoff

“Put it all together, Mom:

Vector embeddings give us the treasure map of meanings.
Positional encoding gives each word a compass.
Self-attention makes words gossip.
Multi-head attention gets different perspectives.
Softmax turns it all into a final decision.

And that’s how AI doesn’t just know where words live - it knows how they live, who they talk to, and which ones matter most in the story.”

Mom: “So basically, you’re telling me your computer has a gossip-filled neighborhood watch?”

Me: “Exactly, Mom. Except this one predicts the next word instead of who stole the mangoes.” 🍋🤭

The Secret Treasure Map of Words