Let's say a simple AI predicts a student's exam score based on study hours.---Step 1: The AI initially predicts a score of 70, but the actual score was 75. The 'error' is 75 - 70 = 5.---Step 2: We have a simple formula for prediction: Score = (Study Hours * Weight) + Bias. Let's say Weight = 5 and Bias = 50, and Study Hours = 4. Predicted Score = (4 * 5) + 50 = 70.---Step 3: To reduce the error, we need to adjust the 'Weight' and 'Bias'. Calculus (specifically, partial derivatives) tells us how much the error changes if we slightly change the 'Weight' or 'Bias'.---Step 4: If changing the 'Weight' by a tiny bit reduces the error a lot, the AI knows to change the 'Weight' more. If changing the 'Bias' has little effect, it changes the 'Bias' less.---Step 5: Backpropagation uses these 'derivative' values to update the Weight and Bias. For instance, if the derivative of error with respect to Weight is positive, it means increasing Weight increases error, so we decrease Weight.---Step 6: New Weight = Old Weight - (Learning Rate * derivative of error with respect to Weight). Let's say the derivative is 1 and Learning Rate is 0.1. New Weight = 5 - (0.1 * 1) = 4.9.---Step 7: Similarly, adjust Bias. The AI repeats this many times until the predicted score is very close to the actual score.---Answer: By using calculus, the AI iteratively adjusts its internal settings (weights and biases) to minimize prediction errors, making its predictions more accurate.