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Brief History of AI & Deep Learning

Foundations (1940s–1950s)

  • 1943 – Artificial neuron

    • Warren McCulloch & Walter Pitts: First mathematical model of a neuron.

  • 1950 – Turing Test

    • Alan Turing: Proposed a behavioral test for machine intelligence.

  • 1956 – Birth of Artificial Intelligence

    • Dartmouth Conference (McCarthy, Minsky, Shannon, Rochester): Term “AI” coined.

Early Neural Networks (1950s–1960s)

  • 1957 – Perceptron

    • Frank Rosenblatt: First trainable neural network model.

  • 1960 – ADALINE

    • Bernard Widrow & Ted Hoff: Adaptive Linear Neuron; LMS (Widrow–Hoff) rule.

  • 1969 – XOR problem

    • Minsky & Papert: Showed perceptrons cannot solve non-linearly separable problems → major setback.

First AI Winter (1970s)

  • Reduced funding and enthusiasm due to limited compute, data, and theory.

Revival & Backpropagation (1980s)

  • 1986 – Backpropagation

    • Rumelhart, Hinton, Williams: Efficient training of multi-layer neural networks.

  • Universal Approximation Theorem

    • Neural networks can approximate any continuous function (with enough hidden units).

Statistical Learning Era (1990s)

  • 1995 – Support Vector Machines (SVMs)

    • Cortes & Vapnik: Strong performance with limited data.

  • 1998 – CNNs (LeNet-5)

    • Yann LeCun: Convolutional Neural Networks for handwritten digit recognition.

Second AI Winter (Late 1990s–early 2000s)

  • Neural networks underperformed compared to kernel methods and ensemble models.

Deep Learning Renaissance (2006–2011)

  • 2006 – Deep Belief Networks / RBMs

    • Geoffrey Hinton: Layer-wise pretraining revitalizes deep nets.

Modern Deep Learning Era (2012–present)

  • 2012 – AlexNet

    • Krizhevsky, Sutskever, Hinton: CNNs dominate ImageNet → deep learning breakthrough.

  • 2014 – GANs

    • Ian Goodfellow: Generative Adversarial Networks.

  • 2017 – Transformers

    • Vaswani et al.: “Attention Is All You Need.”

  • 2020 – GPT-3

    • Large-scale transformer language model.

  • 2020 – AlphaFold2

    • Breakthrough in protein structure prediction.

  • 2022 – ChatGPT

    • Public deployment of conversational LLMs → widespread adoption.

“Ages” of AI (Big Picture)

  • 1st Golden Age: 1956–1969 (symbolic AI & early neural nets)

  • 1st Dark Age (AI Winter): 1970s

  • 2nd Golden Age: 1980s (expert systems, backprop)

  • 2nd Dark Age: 1990s–early 2000s

  • 3rd Golden Age (Deep Learning Era): 2012–present
































































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