$ cat Let's Learn About AI: A Fun Adventure!
> Hi there! Welcome to the exciting world of Artificial Intelligence (AI). Don't worry - you don't need to be a scientist or have a robot friend to understand this! We're going on a fun adventure to learn about AI in a simple way.
$ ls -la
$ cat tokenization.md
Before an AI can understand text, it needs to break it down into smaller pieces called tokens. Think of it like breaking a long word into syllables, or a sentence into words. This helps the AI process text more efficiently and understand patterns better.
For example, the word "unhappiness" might be broken into: "un" + "happi" + "ness". This helps the AI understand that "unhappiness" is related to "happiness" and that "un" means "not".
Modern AI models can handle about 32,000 different tokens! That's like having a huge vocabulary of word pieces that can be combined in many ways to understand and generate text.
Try Tokenization
Type a sentence and see how it gets broken into tokens:
$ cat vector-embedding.md
When AI processes words, it converts them into numbers called "vectors". These vectors help the AI understand relationships between words by placing them in a special space where similar words are closer together.
For example, in this space, "cat" and "dog" might be close because they're both pets, while "tree" might be further away. This helps the AI understand that some words are more related than others.
This special arrangement also helps the AI understand patterns. If it knows that "king" is to "queen" as "man" is to "woman", it can use these relationships to better understand language!
Vector Embeddings: Analogies & Space
Word embeddings place words in a high-dimensional space where distances represent semantic relationships. This allows for mathematical analogies like understanding plurals or associating objects with colors.
Select an example below to see a conceptual 2D visualization with relationship arrows, or switch to the 3D view to explore the conceptual space.
Visualizing: Pluralization
$ cat positional-encoding.md
When AI reads text, it needs to understand not just the words, but also their order. Just like how "Man bites dog" means something very different from "Dog bites man", even though they use the same words!
To help the AI remember word positions, each spot in a sentence gets a special "position tag" - like giving each position its own unique color. When a word lands in that position, it picks up that position's color tag.
This way, even when the AI looks at all the words at once, it can still tell which word came first, second, or third by looking at their color tags. It's like giving each word a special badge that shows exactly where it belongs in the sentence.
Positional Encoding: The "Color Tag" Analogy
Since Transformers look at words all at once, they need a way to know the original order. Imagine each position (1st, 2nd, 3rd...) in the sentence has a permanent, unique "color tag".
When a word lands in a position, it picks up that position's color tag. The model gets both the word's meaning and its position tag.
Notice how "bites" is in Position 2 (Green Tag) in both sentences. The word in Position 1 (Blue Tag) is different, and the model knows this because the word arrives with its position's unique tag.
$ cat self-attention.md
When AI reads a sentence, it needs to understand how words relate to each other. Self-attention is like giving the AI the ability to focus on different parts of a sentence with varying levels of importance.
Think of it like using a spotlight: when the AI looks at one word, it shines its spotlight on all other words, making some brighter (more important) and others dimmer (less important).
This helps the AI understand context better. For example, in "The cat sat on the mat", when looking at "cat", the AI pays more attention to "sat" (what the cat is doing) than to "the" (which is less important for meaning).
Self-Attention: The "Spotlight" Analogy
When the model processes a word (like "cat" below), self-attention helps it weigh the relevance of other words in the sentence. It's like shining a spotlight back onto the sentence.
Brighter words below indicate higher attention scores – meaning the model considers them more important for understanding the context of the focus word ("cat").
Focus Word:cat
Here, "cat" pays high attention to "sat" and "mat" (brighter text), and less to "on" and "the" (dimmer text). Hover over words to see conceptual scores (tooltips planned).
$ cat softmax.md
When AI makes decisions, it often needs to choose between multiple options. Softmax is a special mathematical tool that helps the AI convert its raw thoughts (scores) into probabilities that are easier to understand and use.
Think of it like turning test scores into percentages: if you have scores like 85, 92, and 78, softmax helps convert these into probabilities that add up to 100%, making it clear which option the AI prefers most.
This is especially useful when the AI needs to make choices, like predicting the next word in a sentence or deciding if an image shows a cat or a dog.
Visualize Softmax Transformation
Softmax converts raw scores into probabilities. Watch how the bars transform when you click "Apply Softmax". Negative scores shrink, positive scores rescale, and the final bars represent probabilities summing to 100%.
$ cat knowledge-cutoff.md
AI models learn from a vast collection of data, but this data only goes up to a certain point in time. Think of it like a history book that stops at a specific date - the AI can't know about events that happened after the book was printed.
This stopping point is called the "knowledge cutoff date". For example, if an AI's knowledge cutoff is September 2024, it won't know about events from October 2024 or later, just like how we can't read tomorrow's news today!
This is important to remember because it means AI needs to be updated regularly to learn about new events, discoveries, and changes in the world. It's like getting a new edition of a textbook to stay current with the latest information.
Visualize Knowledge Cutoff
AI models are trained on data up to a certain point. This timeline represents the flow of information and the model's knowledge cutoff.
$ cat bonus-buzzwords.md
This is the realm of creative robots that whip up text, pictures, or even music. It's like having an artsy friend who never sleeps—just don't ask them to cook dinner.
Here, we take a general-purpose AI and give it a crash course in something specific, like writing poems or spotting cats in photos. It's like turning a jack-of-all-trades into a master of one.
This is the land of improvisation. The AI hasn't been trained on a task but tries it anyway—like guessing how to bake a cake without a recipe. Sometimes it's brilliant, sometimes it's a glorious mess.
Finally, we visit the wizards who've mastered the art of talking to AI. It's all about crafting the perfect question—like "Write me a story about a dragon who loves pizza"—to get the best results. Think of it as AI whisperer training.
Imagine a bunch of tiny robot brain cells playing telephone with each other, passing messages back and forth until they figure something out. That's a neural network—it's how AI learns to recognize patterns, just like your brain does!
Think of this as AI doing homework with many, many layers of practice. Each layer helps it understand something more complex. It's like learning to draw by starting with stick figures, then shapes, then finally creating a masterpiece!
This is how AI learns by trial and error, like a puppy learning tricks. When it does something right, it gets a digital treat. When it makes a mistake, it tries something different next time. Pretty soon, you've got a very clever AI!
This is what lets AI 'see' things in pictures and videos. It's like giving a computer a pair of super-smart glasses that can spot cats, read signs, or even tell if you're smiling in a photo!
This is how AI understands and talks in human languages. It's like teaching a robot to read your storybooks, understand your jokes, and even write its own tales. Though sometimes it still gets confused between 'their' and 'there'!
Think of this as AI's special way of paying attention to important things, like how you focus on the most exciting parts of a story. It helps AI understand context and connections, making it better at tasks like writing and translation.