Trying to explain AI to my mom led to the perfect analogy 👩💻👩🍳…
Armed with her recipe box and a smart fridge 🍗🍋🧊, I turned tech jargon into kitchen magic.
Me: Mom, got a sec? I need your help with my AI project. 🧠🤖
Mom: My help? With coding? Well, these carrots aren't going to peel themselves, but talk while we work! 🥕✂️
Me: Perfect! It's about your recipe box. 📚➡️🍳
Mom: My famous, overstuffed recipe box? You've got my attention! 👀
Me: Yes! Imagine your fridge were a genius. You walk up with chicken, lemon, and rosemary... 🍗🍋🌿
...and it instantly says, "You're all set to make your lemon rosemary chicken!" 🧠🧊
Mom: A fridge that can cook? But how? It can't taste! 👅❌
Me: Not taste—it uses a "Food Map"! 🗺️
Alright, Mom, let's break down some technical jargon using our Recipe box and ingredients analogy.
1. Vector (The Recipe Card).
This is simply a detailed description card for an ingredient, but written in a language of numbers that a computer understands.
For example:
An apple 🍎 might be described as: [sweet: 8, crunchy: 7, used in pies: 9].
A carrot 🥕 would have a different description: [sweet: 3, crunchy: 8, used in pies: 1].
2. Vector Space (Your Entire Recipe Box )
Simple Meaning: This is the container that holds and organises all those description cards (vectors). It's the entire system with rules for where each card belongs.
Kitchen Example: Your recipe box 📦 itself is a vector space. It's the organised container where:
The apple card 🍎 is placed in the "Fruits" section.
The carrot card 🥕 is placed in the "Vegetables" section.
The entire box, with its dividers and organised sections, is the Vector Space. It defines the rules and structure that keep similar items (vectors) grouped.
3. Cosine (The "How Similar?" Measurer)
This is the tool the computer uses to find the best matches on the map. It doesn't just measure distance; it checks how aligned two items are.
It measures rosemary 🌿 and thyme and finds they point in the same direction (both herby, savoury).
It measures rosemary and a banana 🍌 and finds they point in completely different directions.
4. Machine Learning Model (The Brilliant Student Chef)
This is the computer's brain—the intelligent system we train to create the map. Imagine a student chef 👩🍳 who reads every cookbook in the world 📚. They don't just memorise recipes; they grasp deep patterns and relationships between ingredients. This chef is the model, and their deep understanding enables them to produce our accurate "Food Map."
5. Embedding Learning (The Cooking School)
Simple Meaning: This is the training process where the computer learns to build the "Food Map" in the recipe box
Kitchen Example (How the AI Draws the Map): Our student chef reads millions of recipes from your recipe box and others. They don't need a teacher; they just need to read. They start noticing patterns all on their own: “Oh, 'sugar,' 'flour,' and 'vanilla' are always best friends!” or “‘Sear,’ ‘sauté,’ and ‘garlic’ always hang out together.” 🎓 By seeing what words always appear together, they slowly figure out the rules for where everything belongs on the map. This is Embedding Learning.
6. Semantic Cluster (The "Friend Groups" on the Map) 👭👫👬
Simple Meaning: A group of things on the map that are all similar in meaning or purpose.
Kitchen Example: In your Recipe Box, you wouldn't put a single apple card all alone. You'd put it in a group with its closest friends.
All the sweet fruits 🍎🍐🍑 would form one "friend group" or semantic cluster (for pies, snacks, and desserts).
All the hearty vegetables 🥕🥔🧅 would form another semantic cluster (for stews, soups, and roasting).
All the herbs 🌿🌿🌿 form their own little group too.
Why it's Important: The computer doesn't just know that an apple is a fruit; it knows that an apple belongs to the sweet fruit friend group. This helps it make much smarter suggestions, like recommending a pear if you're out of apples, because they're in the same semantic cluster.
In a Nutshell: We use Embedding Learning (the cooking school) to train a Machine Learning Model (the chef) to create Vectors (description cards) and place them in a Vector Space (the recipe box), organising them into Semantic Clusters (friend groups). Then, we use Cosine (our measurer) to find similarities between them. That's the magic behind your intelligent fridge
What Is This "Food Map" (or Vector Embedding) ?
You know how in your recipe box, you don't just throw cards in randomly? You have sections — desserts, soups, grilling, and salads. That’s your way of organising. You can say that you have a certain map of flavours, ingredients, and recipes, all organised based on meaning and relationship.
So, you putting cards in the box, is based on your understanding of the ingredients — that’s your vector embedding. And the computer’s vector embedding is based on math, so numbers are its vector embedding.
| Concept | Mom's World (Your Recipe Box) 👩🍳 | Computer's World (The "Food Map") 🤖 |
| Understanding an Ingredient | You know sugar is sweet and belongs in desserts. | The computer calculates numbers for sugar and where it is used: [sweet: 9.8, used_in_cakes: 9.5]. |
| "Vector Embedding" | Your knowledge and intuition. ❤️ | A list of numbers that describes the ingredient. [9.8, 9.5, 0.1, ...] |
| How It's Created | Through a lifetime of experience—tasting, cooking, and remembering. | Through math, analysing millions of recipes to find patterns. |
| The Result | You place the sugar card in the Dessert section of your box. | The computer places the sugar vector near other sweet items in its "Dessert" neighbourhood. |
So you’re both doing the same thing — understanding what things mean and how they relate — just using different languages:
But in the end, your recipe box taught the computer how to understand food. 👩🍳📦➡️🤖
How Does the Computer Draw This Food Map? (The Technical Magic)
The computer learns to draw this map through a process called embedding learning (explained in the previous sections). It uses a machine learning model (like Word2Vec or a neural network) which is trained on millions of cookbooks, food blogs, and menus.
It operates on a key principle: "a word is characterised by the company it keeps." It performs statistical analysis on which words appear together.
How It Works:
The computer learns to build this map through embedding learning. It analyses millions of recipes from the machine learning model, noticing which words constantly keep company—like "sugar," "vanilla," and "bake”
Items that share context, like apples, pears, and peaches, end up forming a semantic cluster (explained in the previous section) because their numerical descriptions are similar.
The precise numerical address of an item on this map is its vector embedding. This is what allows the computer to intelligently navigate the world of food, understanding context and connection, not just words.
How the Fridge Knows Chicken and Rosemary—Two Different Things—Go Together
This is the real magic. The map isn't just for similar items; it charts the relationships between different items that share a context.
Chicken (a protein) and Rosemary (an herb) are different, but they are strongly connected through the context of action and cuisine.
1. Learning from Context:
After reading millions of recipes, the computer builds a web of connections:
Chicken's Vector is strongly influenced by its associations with herbs, roast, garlic, lemon, and rosemary.
Rosemary's Vector is strongly influenced by its associations with roast, garlic, olive oil, chicken, and lamb.
2. The Result on the Map:
The computer doesn't place the "chicken" vector near the "rosemary" vector because they are alike. It places them near each other because they share a common context. Their vectors exist in an overlapping region of the vector space defined by savory roasting and Mediterranean cuisine.
3. Making the Suggestion:
When you stand with chicken and rosemary, the fridge:
Encodes your ingredients into their vector embeddings.
Performs a nearest neighbour search in the vector space.
Finds that the vector for "Lemon Rosemary Chicken" has the smallest cosine distance to the combined vectors of your ingredients.
Suggests that recipe because its location on the map is the perfect representation of savory roasting and Mediterranean cuisinecombination.
This is Bigger Than Just a Fridge
Mom, we’ve covered so much already — you’re doing amazing! 🌟 Let’s gently unpack one last handful of terms together. I promise to keep it light, clear, and rooted in that trusty recipe box of yours. Almost there!
Netflix/Spotify: Collaborative Filtering 👥
What it is: A smart recommendation system that suggests items based on the preferences of people with similar tastes.
How it works: It connects "taste-buddies." If you and a group of friends that love the same recipes (e.g., lemon chicken 🍋🍗 and chocolate cake 🍰), and one of them discovers a new favourite (e.g., apple pie 🥧), the system will recommend that pie to you.
Key point: It doesn’t need to know anything about the item itself—just that people with similar tastes enjoy it.
Google Photos: Convolutional Neural Network (CNN) 👁️✨
What it is: A type of AI that acts like “superhero eyes” for a computer, allowing it to understand and identify images.
How it works:
It breaks down an image into tiny details (e.g., the smooth skin of a tomato 🍅, its round shape, the green stem).
It then pieces these clues together to recognise the whole object (“That’s a tomato!”).
Key point: It mimics how humans quickly recognise objects by focusing on small features first before seeing the big picture.
Google Search: Natural Language Processing (NLP) 🗣️💻
What it is: The technology that helps computers understand, interpret, and respond to human language.
How it works: It allows machines to grasp meaning and context, not just keywords. For example, when you ask your phone, “How do I make lemon chicken?” 🍋🍗, NLP helps it understand what you mean and find a relevant answer.
Key point: It’s essentially about teaching machines to speak human by understanding our language the way we do.
A Final Pinch of Wisdom (And a New Cooking Student) 🌿👩🍳🤖
So, Mom, what started as a confusing tech talk ended up back where we always do: in the kitchen, surrounded by the recipes and relationships that give our lives flavour.
Your recipe box—and all the intuition and love within it—taught the AI what belonging means. The real genius isn’t in the algorithm—it’s in the human experience it was trained on.
Now, of course, you’ve taken your job as Head Chef-Instructor very seriously. I just walked in and found you showing our new AI robot assistant how to properly season a soup 😄. “A little love means a little more garlic,” you said, while the robot diligently recorded “love = +0.3 garlic” in its database. 😂
You’ve been the teacher all along. Now you’ve just got a very literal, very eager silicon-based sous-chef. Bon appétit, indeed! 👩🍳📦➡️🤖
Do share your thoughts in the comments! 💬
