Tweak my knobs, I learn.
Parameters are the internal numbers an AI learns from training data that determine how it processes inputs and generates outputs.
Imagine a recipe that starts with a basic dough, but you can tweak ingredients like sugar or salt to change the final cookie. Parameters are like those tweaks—they're the adjustable parts of an AI that get fine-tuned during training so the AI can learn patterns from data.
Parameters define what an AI has learned and how accurately it can perform tasks like translation or image recognition. The number of parameters often correlates with model capacity, so larger models (like GPT-4 with trillions of parameters) can handle more complex tasks.
People often think more parameters always mean a better AI, but that's not true—quality of training data and architecture matter just as much. Also, parameters aren't the same as the training data itself; they're the learned weights that summarize patterns from data.
In machine learning, parameters are the internal variables of a model that are learned from training data during the optimization process. They include weights and biases in neural networks, which are adjusted via backpropagation to minimize a loss function. The total number of parameters is a measure of model complexity and capacity.