Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. It is a branch of computer science that deals with the creation and development of intelligent machines capable of performing tasks that would typically require human intelligence.

AI systems work through a combination of algorithms and data processing. Here's a simplified explanation of how AI works:

1. Data Collection: AI systems require a significant amount of data to learn from. This data can be collected from various sources, such as databases, sensors, or the internet.

2. Data Preprocessing: The collected data is processed and cleaned to ensure its quality and consistency. Irrelevant or noisy data may be removed, and the data may be transformed or standardized to make it suitable for analysis.

3. Training: AI models are created by using machine learning algorithms. During the training phase, the AI system is exposed to the preprocessed data. The algorithms analyze the data, identify patterns, and learn from the examples provided. The goal is for the AI model to recognize and generalize patterns in the data.

4. Testing and Evaluation: Once the AI model is trained, it is tested using separate datasets to assess its performance. This helps determine how well the model can make predictions or perform specific tasks.

5. Deployment: If the AI model performs satisfactorily during testing, it can be deployed to perform real-world tasks. This may involve integrating the AI system into existing software or hardware infrastructure.

6. Feedback and Improvement: As the AI system operates in the real world, it can gather new data and receive feedback on its performance. This feedback is used to refine and improve the AI model over time, making it more accurate and effective.

It's important to note that AI encompasses various subfields, including machine learning, deep learning, natural language processing, computer vision, and more. Different AI techniques and approaches may be used depending on the specific problem or application.