👉 Conditioning computing refers to the process of providing additional context or information, known as conditioning signals, to a machine learning model to guide its predictions or decisions. This is particularly useful in scenarios where the model needs to make more nuanced or context-dependent choices, such as in natural language understanding, image recognition, or decision-making tasks. By conditioning the model on specific inputs—like labels, examples, or environmental cues—the model can better align its outputs with the desired context, enhancing its performance and interpretability. This approach is crucial for tasks where the model's predictions should be sensitive to external factors or prior knowledge, allowing it to produce more accurate and relevant results in real-world applications.
