How ai Learns
There are several ways that artificial intelligence (AI) systems can learn:
- Supervised learning: In supervised learning, an AI system is trained on a labeled dataset, which includes input data and the corresponding correct output. The AI system makes predictions based on this input-output mapping and is corrected by the supervisor (a human or another AI system) when it makes a mistake.
- Unsupervised learning: In unsupervised learning, an AI system is not given any labeled training data. Instead, it must find patterns and relationships in the data on its own. This can be used to discover hidden structures in the data.
- Semi-supervised learning: In semi-supervised learning, an AI system is given some labeled training data and some unlabeled data. This can be more efficient than supervised learning, as it requires less human effort to label the data.
- Reinforcement learning: In reinforcement learning, an AI system is trained to take actions in an environment to maximize a reward. The AI system learns through trial and error, receiving positive or negative feedback based on its actions.
- Transfer learning: In transfer learning, an AI system is able to apply knowledge and skills learned from one task to a related task. This can be more efficient than training a system from scratch on a new task.