Artificial intelligence can be organized in several ways, depending on stages of development or actions being performed. 

For instance, four stages of AI development are commonly recognized.

1. Reactive machines: Limited AI that only reacts to different kinds of stimuli based on preprogrammed rules. Does not use memory and thus cannot learn with new data. IBM�s Deep Blue that beat chess champion Garry Kasparov in 1997 was an example of a reactive machine.
2. Limited memory: Most modern AI is considered to be limited memory. It can use memory to improve over time by being trained with new data, typically through an artificial neural network or other training model. Deep learning, a subset of machine learning, is considered limited memory artificial intelligence.
3. Theory of mind: Theory of mind AI does not currently exist, but research is ongoing into its possibilities. It describes AI that can emulate the human mind and has decision-making capabilities equal to that of a human, including recognizing and remembering emotions and reacting in social situations as a human would. 
4. Self aware: A step above theory of mind AI, self-aware AI describes a mythical machine that is aware of its own existence and has the intellectual and emotional capabilities of a human. Like theory of mind AI, self-aware AI does not currently exist.

A more useful way of broadly categorizing types of artificial intelligence is by what the machine can do. All of what we currently call artificial intelligence is considered artificial �narrow� intelligence, in that it can perform only narrow sets of actions based on its programming and training. For instance, an AI algorithm that is used for object classification won�t be able to perform natural language processing. Google Search is a form of narrow AI, as is predictive analytics, or virtual assistants.

Artificial general intelligence (AGI) would be the ability for a machine to �sense, think, and act� just like a human. AGI does not currently exist. The next level would be artificial superintelligence (ASI), in which the machine would be able to function in all ways superior to a human. 

When businesses talk about AI, they often talk about �training data.� But what does that mean? Remember that limited-memory artificial intelligence is AI that improves over time by being trained with new data. Machine learning is a subset of artificial intelligence that uses algorithms to train data to obtain results.

In broad strokes, three kinds of learnings models are often used in machine learning:

Supervised learning is a machine learning model that maps a specific input to an output using labeled training data (structured data). In simple terms, to train the algorithm to recognize pictures of cats, feed it pictures labeled as cats.

Unsupervised learning is a machine learning model that learns patterns based on unlabeled data (unstructured data). Unlike supervised learning, the end result is not known ahead of time. Rather, the algorithm learns from the data, categorizing it into groups based on attributes. For instance, unsupervised learning is good at pattern matching and descriptive modeling. 

In addition to supervised and unsupervised learning, a mixed approach called semi-supervised learning is often employed, where only some of the data is labeled. In semi-supervised learning, an end result is known, but the algorithm must figure out how to organize and structure the data to achieve the desired results.

Reinforcement learning is a machine learning model that can be broadly described as �learn by doing.� An �agent� learns to perform a defined task by trial and error (a feedback loop) until its performance is within a desirable range. The agent receives positive reinforcement when it performs the task well and negative reinforcement when it performs poorly. An example of reinforcement learning would be teaching a robotic hand to pick up a ball.