Half a century ago, cerebellar learning models based on a simple perceptron were proposed independently by Marr and Albus. Soon, these models were combined with Ito's flocculus hypothesis that the cerebellar flocculus controls the vestibulo-ocular reflex through teacher signal-dependent learning, and consequently integrated into the so-called Marr-Albus-Ito cerebellar learning hypothesis. Ten years later, Ito found the synaptic plasticity of long-term depression at cerebellar Purkinje cell synapses, which underlies cerebellar learning. The liquid-state machine (LSM) model, which adds the random inhibitory recurrent neural network composed of granule cells --Golgi cells loop to a simple perceptron, explained the learning of timing in eyeblink conditioning, the learning of gains in ocular reflex, and the formation of short- and long-term motor memories in the cerebellum. The LSM model is now extended to the cerebellar internal model-based voluntary movement control and cognitive function. Artificial intelligence (AI) based on the neural network models originating from a simple perceptron, has now developed to deep learning. As the LSM model of the cerebellum is the counterpart of deep learning in the brain, the cerebellum is considered to be the origin of current AI. Finally, we discuss the impact of the evolution of AI on future clinical cerebellar neurology.