This description is based on a conversation with 'Sam', which took place over several days in January 2025
At the heart of AI model training are two key processes: forward propagation and backward propagation. In forward propagation, data moves through the model’s layers, with each layer applying mathematical transformations until it produces a prediction.
That prediction is then compared to a known correct answer, which has been determined by the dataset labels provided by human annotators, predefined rules, or statistical benchmarks. The difference—called the error—is then calculated, showing how far off the model's guess was. This is where backward propagation comes in, adjusting internal parameters through a process called gradient descent, refining the AI’s ability to make more accurate predictions over time.
But how does the model actually decide on a final prediction? The answer lies in a complex interplay of statistical probabilities, weighted data, and learned patterns. AI doesn’t “think” in the way humans do—it evaluates vast amounts of previous examples and determines which outcome is most statistically probable.
Augmetrics® enhances this process by ensuring that predictions are based on structured, reusable knowledge rather than ephemeral computations. This approach reduces noise, enhances accuracy, and creates a more explainable decision-making process—ensuring AI models are not just guessing, but reasoning with reliable information.