Meta-learning, mostly known as a ‘branch of metacognition’ or ‘machine learning’ is focused on one’s learning process. It aims to fill the gap between the learning process of a person to that of a machine by using the science of systematically observing how machine learning approaches perform under a wide range of learning tasks. It then learns from the results to ultimately learn new tasks faster than previously thought possible.
The outcome is expected to be an excellent meta-learning model with the ability to adapt and fit well when confronted with new tasks and new environments that were not presented during their training.
The prime reason meta-learning is also known as learning to learn is because the learning sessions commonly take place in the machine’s testing phase with reduced exposure to any new type of task parameters.
When it comes to advantages, here are some examples.
Less data is required to train the machines.
This means that the methods used help create a more versatile framework that can transfer information from one context to an entirely different one. This reduces the amount of data you would need in the new context to solve any problems you may endure.
Speed.
Meta-learning has ways and methods that help the creation of custom made models which can then perform better at a higher speed.
Scalable.
Meta-learning models help to increase the level of scalability that AI applications have by using automatic processes and improving algorithms.
The models can be agile and adaptable when confronted with environmental changes such as Reinforcement learning.