This is the complete history of Artificial Intelligence and Machine Learning, exploring the key building blocks from ancient probability to modern Generative AI. We break down the math and programming concepts (like the Perceptron and Transformer Architecture) that power systems like GPT and Gemini.
1. The Precursors
Root: Human observation and pattern recognition.
1763: Bayes' Theorem establishes the mathematical foundation for probability and prior knowledge.
2. The Theoretical Dawn (1940s-1950s)
1943: McCulloch-Pitts Neuron created the first mathematical model of an artificial neuron.
1950: The Turing Test proposed as a test for machine intelligence.
1956: Dartmouth Conference officially coins the term Artificial Intelligence.
1959: Machine Learning term coined by Arthur Samuel (IBM Checkers program).
3. The Early Networks and AI Winters (1957-1990s)
1957: The Perceptron (Frank Rosenblatt) creates the first working neural network model.
1974: Backpropagation Algorithm published, essential for training multi-layer networks.
1970s & 1980s: AI Winters—funding and interest drops due to hardware limits.
4. The Data & Deep Learning Revolution (1990s-Present)
1990s: Shift to Data-Driven ML (Statistical methods like Decision Trees).
2006: "Deep Learning" coined (Hinton) with effective training algorithms for many layers.
2012-2017: Deep Learning Triumphs (ImageNet, AlphaGo) prove the technology's power.
2017: The Transformer Architecture breakthrough (Attention mechanism) enables massive scale.
2018+: Generative AI Era (GPT, Gemini)—focus shifts to creating content.
Today (2025): Race for high-quality Training Data and solutions for Energy Demand.