In
artificial intelligence, learning is the process by which an
AI model automatically improves at a task through
training with data and experiences, similar to how a human learns from practice and examples.
Machine Learning is the most common method to achieve this learning.
In
artificial intelligence, there are different ways to
train an AI. For example, a model can learn to recognize cats by analyzing thousands of cat photos (supervised learning), or discover
patterns in sales data to predict trends by itself (unsupervised learning). It can also learn by playing repeatedly, like when it learns to win at chess through trial and error (reinforcement learning).
There are also more advanced forms, such as when a model learns to detect diseases by combining radiographs labeled by doctors with unlabeled ones (semi-supervised learning), or when it learns to predict the next word in a sentence by analyzing millions of texts on its own (self-supervised learning).
The ultimate goal is for the model to apply what it has learned to new situations, just like when a human applies their experience to similar but different problems.