What Is AI Inference?

What is AI inference?

AI inference describes the process of a machine learning model making deductions based on information it's been trained on.

Inference follows the training stage to make an AI model work. It happens when you give an AI model like ChatGPT a prompt and get a response. The response is based both on the data that the AI model has been trained on, and on its ability to infer answers.

Why is AI inference important?

The two components that differentiate AI models from one another are training and inference. The two work closely together. Training, consisting of a set of billions or even trillions of parameters, directly influences a model's ability to infer. The better trained it is, the better its inferences will be.

Training is key in determining a model's strength, but inference ultimately determines its accuracy. Although the strength of the training dataset influences inference, a model is not worth much without the model to infer.

Inference enables self-driving cars to drive safely on roads they've never been on before, and allows large language models to answer questions they haven't seen before. Without inference, AI models wouldn't be able to do new things.

Most of an AI model's life is spent in inferencing since training takes place before a model is ready to be used.

How it works

One example of AI inference is a self-driving car that can recognize signage on the road -- even if it hasn't been on that road.

Inference also gives AI predictive abilities, so it can predict what you're about to type or make forecasts from a given set of information.

As inference enables humans to make decisions and judgments, inference does the same thing for artificial intelligence.

As models get more powerful, the inference is likely to become even better, making progress toward artificial general intelligence (AGI). AGI will only be reached when inference capabilities that can match those of humans.