AI inference is the process of using a trained AI model to make predictions or decisions based on new input data. It brings the power of AI to real-world applications improving our lives in countless ways.
How does AI Inference work?
Imagine you taught a robot how to recognize stop signs. Once it’s learned this from many pictures during its learning phase, it can later identify stop signs it has never seen before on its own. This ability to apply what it’s learned to new situations is called AI inference.
For example, think of a self-driving car that knows to stop at a stop sign, even if it’s on a new road. Or a computer program that can predict how well a sports player will perform in the future just based on past performances. These are all uses of AI inference – the robot or program making decisions based on what it has learned.
AI Inference vs. Training
There are two main stages in teaching a robot or program:
- Training: This is when the robot is learning, which can be like trying and failing until it gets better, or being shown many examples of what it needs to know.
- Inference: This happens after training. The better the training, the better and more accurate the decisions or “inferences” the robot can make, though it’s not always perfect.
For instance, to make our self-driving car recognize stop signs, it would be shown many images of stop signs during training. It might even be driven around by a human to get real-life experience. After enough training, it can start to recognize stop signs on its own.
Uses of AI Inference
AI inference is used in many areas of daily life:
- Large language models: These are programs that read and understand text they’ve never seen before.
- Predictive analytics: These programs make predictions about the future based on past data, like guessing if a stock’s value will go up or down.
- Email security: Some programs can learn to spot spam or scam emails and filter them out to protect users.
- Driverless cars: As mentioned, these need to understand road signs and traffic to operate safely without a human driver.
- Research: In fields like science and medicine, programs can help researchers analyze data and find new insights.
- Finance: Programs that can predict financial trends or market movements after learning from past data.
How Does AI Training Work?
Training a robot or program involves feeding it lots of data. This could be images, texts, or any type of information depending on what it needs to learn. Some data might tell the program exactly what to learn, and other times it just practices until it starts to see patterns. As it gets better, its creators adjust it to improve accuracy, like making sure it knows the difference between a cat and a dog in pictures.
Power Usage in AI: Training vs. Inference
Training a program or robot to learn something new uses a lot of computer power, almost like how you need more energy and focus to solve a hard math problem than an easy one. Once it’s trained, it uses less power, but still needs a significant amount to keep making decisions or “inferences” as it applies its training to new data. This ongoing use of power can add up, especially if the program is used a lot.
