Types of Data in AI Models

In artificial intelligence, the types of data used in training models play a critical role in determining model capabilities and the applications they support. Data is generally categorized based on its nature, format, and structure, each type being valuable for specific AI tasks. Understanding these categories helps in selecting the right data for different AI applications, ultimately improving the performance and accuracy of the models.

Labeled and unlabeled data

Labeled data comes with predefined tags or annotations that identify the characteristics or categories of each data point. For example, labeled data for an image recognition task might include images of animals, each labeled with the type of animal it depicts (e.g., “dog” or “cat”). Labeled data is essential in supervised learning, where the model learns by example, associating specific input with the expected output. It’s widely used in applications like fraud detection, where each transaction is marked as fraudulent or legitimate, and in medical diagnosis, where images are labeled by condition.

Unlabeled data, on the other hand, lacks these explicit labels, requiring the AI model to identify ...