WTF is AI?
You know it's a big deal. You see the headlines. But what does it all mean?
Let's break it down with pretty graphics and minimal BS.
This is What's Happening Inside
Neural networks process data through layers of mathematical functions. Input goes in, gets transformed through multiple layers (the "neurons"), and predictions come out. It's like a very sophisticated pattern-matching machine built from millions of tiny math operations.
The Concepts, Decoded
Click any card to expand. Prepare for enlightenment (or at least mild understanding).
WTF is AI Anyway?
It's basically spicy autocorrect
AI (specifically LLMs) are massive pattern-matching machines. They learned from billions of text examples and now predict what word comes next. That's it. That's the magic. It's autocomplete on steroids with a PhD in everything.
Neural Networks
Lasagna of math
Imagine a lasagna where each layer transforms information. Input layer receives data, hidden layers do the magic math (matrix multiplication, baby!), output layer gives you an answer. Each 'neuron' is just a fancy math function that says 'how much do I care about this input?'
WTF are Tokens?
Words, but smaller and weird
Tokens are how AI 'sees' text. A token can be a word, part of a word, or even punctuation. 'Hello' = 1 token. 'Hello, world!' = 4 tokens. Why? Because AI doesn't read like humans - it chunks text into bite-sized pieces. Context window = how many tokens the AI can remember at once.
WTF do the Benchmarks Mean?
Standardized tests for robots
MMLU = Massive Multitask Language Understanding (57 subjects from math to philosophy). HumanEval = Can it code? GPQA = Graduate-level science questions. GSM8K = Grade school math (surprisingly hard for AI). These measure if the AI is actually smart or just good at sounding smart.
Context Windows
AI's working memory
Context window = how much text the AI can 'see' at once. GPT-4 Turbo: 128K tokens. Claude: 200K. Gemini: 1M. Bigger = better memory but slower & pricier. It's like RAM for your brain. More RAM = more browser tabs before your computer starts crying.
WTF is Cost Per Million Tokens?
The AI tax
Every time you use an AI, you're buying tokens. Input tokens (what you send) + Output tokens (what it responds) = your bill. GPT-4: $10-30/M tokens. Claude: $3-15/M. DeepSeek: $0.55/M (it's basically free wtf). The math: 1M tokens ≈ 750K words ≈ a novel. So you're paying ~$3-30 per novel's worth of conversation.
Parameters
Size matters (kind of)
Parameters = the 'knobs' the AI adjusts during training. More parameters ≠always better (see: DeepSeek R1 with 'only' 671B params beating models with trillions). It's like saying a 10GB app is better than a 1GB app. Sometimes the smaller one just works smarter. Quality > quantity.
Still Confused? That's Normal.
AI is complicated. These visualizations are simplified. But now you know enough to sound smart at parties. Check out the leaderboards to see which models are actually good at this stuff.
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