Unsupervised machine learning involves:

using very large databases to store common sense knowledge, then searching the database for patterns.

using algorithms to simulate the neurons and synapses of human brains.

using labeled inputs identified by humans to recognize objects.

using genetic algorithms to identify patterns in large datasets.

programs that can "teach themselves" without human intervention.