AI Inference

AI Inference

AI Inference is the second step in the two-part process of machine learning and deep learning, with the first step being AI Training. These two steps are a significant reason why modern artificial intelligence (AI) is suitable for such a diverse range of tasks, from generating content to autonomous driving. During the inference phase, a pre-trained AI model is exposed to fresh, unlabeled data. It relies on the database it "studied" during training to analyze new input data and provide correct output data. To use generative artificial intelligence as an example, every time you ask a question in ChatGPT or request Stable Diffusion to draw something, the AI model is making inferences. The reason it can come up with such human-like responses is due to the extensive training it underwent previously. Even during inference, the artificial intelligence also registers the responses of human users for the next training session. It notes when its creativity is praised or criticized. This continuous loop of learning and inference makes artificial intelligence increasingly realistic.

The basic steps that take place during AI inference:

1. Initially, the input data that will be fed into the AI model must be properly prepared. The data needs to be in the appropriate format and comply with what the model expects.
2. In this stage, the input data is processed to be ready for the model's input. Depending on the model type and task, this may involve scaling, normalization, resizing images, text tokenization, etc.
3. Now comes the actual inference. The input data is passed to the trained AI model, which performs computations based on the input and returns the result in the form of predictions, classifications, text generation, or other types of responses.
4. The obtained result from the model may require further processing to get the final answer. For example, if the model made a classification, the result may be an index corresp