Inference is when a trained AI model applies its learned knowledge to make predictions, classifications, or decisions about new, unseen data. It's like putting all that training into real-world practice.
It's like a chef who has mastered many recipes. During inference, they encounter a new recipe and use their existing knowledge to adapt, predict the outcome, and potentially improve it.
Contrast with Training: Inference is distinct from AI training. Training is where the model 'learns' from vast amounts of data to identify patterns and correlations. Inference is where it applies that learned knowledge.
Output Types: The outcome of inference can be: Classification: Categorizing something (e.g., "This image is a cat") Prediction: Forecasting a value (e.g., "The stock price is likely to rise") Decision: Recommending an action (e.g., "This customer is likely to churn") Generation: Creating new content (e.g., writing a summary of an article)
Image Recognition: A self-driving car uses inference to identify objects in real-time (stop signs, pedestrians, other vehicles) based on its training data. Medical Diagnosis: An AI system might analyze medical images, assisting doctors in diagnosing diseases by applying learned patterns from previous examples.
Spam Filtering: Email providers use AI inference to determine if a new email is likely spam. Natural Language Processing: Chatbots utilize inference to understand the intent of your questions and generate appropriate responses.
Inference is the heart of how AI adds value to the real world. It enables AI systems to: Adapt: Apply knowledge to new situations without needing to be explicitly reprogrammed. Reason: Use patterns learned from past data to make informed predictions and decisions. Automate: Handle tasks that would be overwhelming for humans at scale and speed.