Meta-learning is a type of machine learning technique in which a model adapts to new tasks using knowledge from previously solved tasks. It is a concept that has been around since the late 1980s and is used in various models in artificial intelligence.

In traditional machine learning, the model needs to be re-trained on new data and tasks, since it relies on learning from data that is directly related to the current task. On the other hand, when meta-learning is used, the task of learning a new task can be seen as a meta-task that is backed by the knowledge of how the model learned from previous tasks.

Meta-learning techniques have been used in various areas, such as reinforcement learning, neural networks, forecasting and robotics. It can also be used to improve the efficiency of the learning process, by having the model adapt faster and with less data.

For example, in multi-task learning, models are trained on a set of related tasks – a technique that some models can learn. This helps the model to quickly adjust to a new task, as it has already seen similar data from the previous tasks.

Meta-learning can also be used in transfer learning, where a model that has already been trained can transfer its knowledge to a new task. This allows for faster and more accurate training, by having the model leverage knowledge from previously trained tasks.

Overall, meta-learning makes the machine learning process more efficient, as the model can use existing knowledge to quickly adapt to new tasks. In the future, the use of meta-learning is expected to become more prominent, as models will be able to learn faster, more accurately and with fewer data sets.

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