Unsupervised machine learning involves:

using labeled inputs identified by humans to recognize objects.

programs that can detect digital photos of human faces and cats without humans labeling the input photos.

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

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

using genetic algorithms to identify patters in large datasets.