Historical evolution of introductory examples in machine learning and AI

A historical survey of 'Hello World' examples in machine learning and artificial intelligence, tracing the evolution of the most accessible methods in each period. Random Forests, introduced in 2001, became popular only after integration into scikit-learn in 2012. XGBoost, released in 2014, gained prominence in 2015 through Kaggle competitions. Multilayer perceptrons and general neural networks gained significant traction after TensorFlow's 2015 release and TensorFlow 1.0 in 2017. AlexNet and deep convolutional networks, though introduced in 2012, only became mainstream with PyTorch and modern frameworks availability in 2017. The analysis demonstrates how tool and library adoption was as crucial as fundamental innovation in making AI methods accessible to broader audiences.