Deep Learning, also referred to as"Artificial Neural Networks," was introduced in 1943, when researchers Warren McCulloch and Walter Pitts showed that highly simplified models of neurons could encode mathematical functions. A neuron within a Deep Learning network is similar to a neuron of the human brain.  Key milestones in this type of machine learning include the development of the Perceptron, the discovery of backpropagation, and the introduction of AlexNet and ImageNet.

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During the 1960s and 1970s, AI research primarily focused on rule-based systems and symbolic reasoning. This period witnessed significant progress, with programs capable of solving complex problems and playing games, such as chess and checkers, at an expert level.

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In 1973, the creation of an AI program called MYCIN marked another milestone as it demonstrated advanced capabilities in medical diagnosis. However, the field faced a setback in the late 1970s due to limited computing power and insufficient funding. This led to what is known as the "AI Winter," a period during which interest in AI declined.

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A significant advancement in artificial intelligence occurred in 1986 with the introduction of machine learning algorithms. These algorithms enabled computers to learn from data independently, without requiring explicit programming for each task. This innovative approach has proven highly effective in fields such as image recognition and natural language processing.

In the 1990s, support vector machines (SVMs), originally developed by Vladimir Vapnik and Alexey Chervonenkis, gained widespread use for addressing complex classification problems. Simultaneously, decision trees rose to prominence as user-friendly and interpretable models for both classification and regression. Their ability to clarify decision-making processes made them essential tools in various applications. Furthermore, decision trees laid the groundwork for ensemble methods, which significantly enhanced predictive performance.

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Throughout that decade, artificial intelligence was widely used in fields such as fraud detection, document classification, and facial recognition, demonstrating its practical benefits across multiple industries. Additionally, there were notable advances in reinforcement learning, particularly in function approximation and policy iteration. Techniques such as Q-learning, first introduced in 1989, were refined to address more complex decision-making scenarios, thus paving the way for the development of adaptive AI systems.

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The turn of the century brought another wave of advances in deep learning techniques, enabling computers to process vast amounts of data using neural networks inspired by the human brain's structure.

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In 2011, IBM's Watson defeated human champions on Jeopardy!, demonstrating the significant progress AI had made since its early days. 

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AI has achieved several milestones, including AlphaGo's victory over world champion Lee Sedol in the game of "Go", a game believed to require intuition; autonomous robotic vacuums; self-driving cars hitting the roads; and virtual assistants like Siri and Alexa, which have become mainstream devices used daily by millions.