What is inference in AI?
Inference in AI refers to the process of making predictions or decisions based on a trained model and new input data. For example, once a machine learning model has been trained to recognize images of cats, it can infer from a new image whether it contains a cat.
How does inference work in AI?
Inference in AI involves feeding new input data into a trained model and calculating the model's output. The specifics of this process depend on the type of model. For example, in a neural network, the input data is passed through the network's layers, each of which applies a set of weights and a non-linear function to the data. The final output is the network's prediction.
Inference is typically much faster than training, as it involves a single pass through the model rather than iterative optimization of the model's parameters.
What are the challenges of inference in AI?
While inference is generally faster than training, it can still be computationally intensive, particularly for large models and large amounts of input data. This can be a challenge for applications that require real-time predictions. Additionally, the accuracy of the inference depends on the quality of the trained model and the relevance of the input data to the data the model was trained on.