What Does Training an AI Mean?

Many people say they are "training AI," but understanding the technical meaning behind this can be a challenge.

In this lesson, we will explore exactly what it means to train AI.

AI, particularly artificial neural networks, can be thought of as operating like a function, as you might have learned in a math class.

A function takes an input and follows certain rules to produce an output, and AI follows the same principle.

For example, consider the quadratic function y = ax^2 + bx + c that you might have studied.

Here, ( a ), ( b ), and ( c ) have specific values, and the output ( y ) varies based on the input ( x ).

Similarly, AI models connect inputs and outputs by adjusting specific numbers called weights and biases.

The difference with AI models is that they represent massive functions that include millions of weights and biases.

Training an AI means more than just inputting data; it involves AI adjusting its weights and biases.

AI learns by adjusting weights and biases based on the data it receives to make more accurate predictions.

For instance, let's say you want to create an AI model that recognizes dogs in images.

Initially, the AI will use randomly set weights and biases to try to identify dogs, and naturally, its performance won't be great at first.

The AI then learns to make more accurate predictions by continuously adjusting the weights and biases through comparing its answers to the correct ones.

A major technique used in this process is Backpropagation along with Gradient Descent.

Backpropagation is the method by which AI adjusts its weights and biases by comparing its predicted result with the actual answer, while Gradient Descent is a method to update these weights and biases in a direction that minimizes the Cost Function representing the error.

We will delve deeper into backpropagation and gradient descent in future lessons.

In the next lesson, we'll explore how the results of training AI manifest and how they are stored.