Artificial Neural Networks (ANN): The Brains of the Operation
Definition: Computer systems designed to mimic brain structures, composed of interconnected nodes organized in layers for processing information.
How it Works (Simply)
An ANN is a giant voting machine.
- Input Layer: Pixels of an image or tokens of text.
- Hidden Layers: Billions of knobs (parameters) that get tweaked during training.
- Output Layer: The prediction (“This is a cat” or “print(‘Hello World’)”).
Why Vibe Coders Should Care
You don’t need to build ANNs to use them, but understanding their structure explains their failures.
- Forgetting: ANNs have fixed capacity. If you push the old context out of the window, it “forgets” your instruction. It’s not malicious; it’s just how the layers work.
- Pattern Matching: ANNs are statistical pattern matchers. They don’t “know” logic. They know that
foris usually followed byi in range. If you have a weird loop structure, the ANN will struggle because it hasn’t seen that pattern often.
Coding with ANNs
- Embeddings: You can use ANNs in your app! Use
text-embedding-3-smallto turn user search queries into vectors (numbers) and find relevant documents. This is the “Hello World” of modern AI engineering.
The Limitation
ANNs are “Probabilistic.” Traditional code is “Deterministic.”
- The Clash: Vibe coding is the art of managing the friction between probabilistic intent and deterministic execution.