Why You Can’t Always Reproduce the Same AI Result.

"Why can't we always get the same result from an AI?"

Randomness: Many AI algorithms incorporate randomness to help them explore different possibilities during training. This randomness can influence the learning path, leading to varied outcomes.

Data Variability: The data used to train AI can vary. For example, if you have a model trained on images of cats and dogs, the results can change based on which images you include or exclude from your training set.

Algorithm Choice: Different algorithms may interpret the same data differently. For instance, one algorithm might prioritize certain features over others, affecting its predictions.

Read the What’s In AI article HERE.