An AI learning machine, which refers to a system that utilizes machine learning, works by using algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Here's a more detailed breakdown of how it works:

### Data Ingestion

The first step in machine learning is to collect and prepare the data. Data can come in many forms—text, images, numbers, etc.—and from many sources. This data is often preprocessed to make it suitable for use by a machine learning model. Preprocessing might include normalizing data (scaling it to a given range), handling missing values, or encoding non-numeric data into a numeric format.

### Model Selection

Next, a specific machine learning model is selected based on the problem at hand. This could be a simple linear regression model for predicting numeric values, a convolutional neural network for image recognition, or a recurrent neural network for processing sequential data like language.

### Training

The selected model is then "trained" using a large set of data. During training, the model makes predictions or decisions based on the input data it receives, and its output is compared to the expected result. The difference between the output and the expected result is used to adjust the model's parameters through a process known as optimization, which typically involves a technique called gradient descent.

### Evaluation

After the model has been trained, it is evaluated using a separate set of data that it hasn't seen before, known as the test set. This step is crucial to assess the model's performance and ensure that it can generalize its predictions to new, unseen data, rather than simply memorizing the answers (a problem known as overfitting).

### Prediction

Once trained and evaluated, the machine learning model can be used to make predictions or decisions with new data. For instance, a model trained to recognize spam emails can start filtering incoming emails, or a model trained to predict stock prices can be used to make investment decisions.

### Learning and Adaptation

Some machine learning models can continue to learn and adapt over time as they are exposed to new data. This is especially true for models deployed in changing environments. For example, recommendation systems (like those used by Netflix or Amazon) constantly refine their predictions based on user interactions.

### Types of Machine Learning

There are three main types of machine learning:

1. **Supervised Learning**: The model learns from a labeled dataset, which provides the answers (known as ground truth) during training. The goal is to learn a mapping from inputs to outputs.

2. **Unsupervised Learning**: The model learns from unlabeled data and tries to find inherent patterns or groupings in the data, such as clustering customers by their purchasing behavior.

3. **Reinforcement Learning**: The model learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward.

In summary, an AI learning machine (or a machine learning system) is designed to automatically learn from experience (data) and improve its performance over time without being explicitly programmed with rules to follow for every possible scenario. This capability to learn and adapt is what makes machine learning a powerful tool for a wide range of applications.