A Brief History of Artificial Intelligence: From McCulloch‑Pitts Neurons to GPT‑4

In 1943, Warren McCulloch and Walter Pitts introduced the artificial neuron model, also known as the threshold logic unit (TLU), laying the foundation for neural network research.

In 1950, Alan Turing published "Computing Machinery and Intelligence," proposing the famous Turing Test, which marked the birth of the artificial intelligence concept.

The 1956 Dartmouth conference, organized by John McCarthy, Marvin Minsky and others, officially coined the term "Artificial Intelligence," establishing it as an independent discipline.

In 1959, Arthur Samuel created the first self‑learning program—a checkers player—and introduced the term "machine learning."

In 1966, Joseph Weizenbaum developed ELIZA, an early natural‑language processing program that simulated a psychotherapist, demonstrating the possibility of human‑computer dialogue.

In 1969, Marvin Minsky and Seymour Papert published "Perceptrons," exposing the limitations of single‑layer neural networks and contributing to the first AI winter.

In 1982 the field revived, and in 1986 David Rumelhart and colleagues re‑introduced the back‑propagation algorithm, reigniting interest in multilayer neural networks.

In 1989, Yann LeCun applied convolutional neural networks to handwritten digit recognition, marking an early success of deep learning in practical applications.

In 2006, Geoffrey Hinton and collaborators introduced Deep Belief Networks, providing a foundation for modern deep learning.

In 2012, Alex Krizhevsky, under Hinton's guidance, released AlexNet, achieving breakthrough performance in the ImageNet competition and popularizing deep convolutional networks.

In 2014, Ian Goodfellow proposed Generative Adversarial Networks (GANs), opening a new direction for generative modeling.

The 2017 paper "Attention Is All You Need" introduced the Transformer architecture, revolutionizing natural‑language processing and paving the way for large pre‑trained language models. In 2018, Google released BERT, a major breakthrough for NLP.

OpenAI released GPT‑2 in 2019, demonstrating the power of large‑scale pre‑training for text generation, followed by GPT‑3 in 2020 with 175 billion parameters, further advancing language understanding.

In 2022, OpenAI launched ChatGPT, a conversational model based on the GPT‑3.5 architecture that gained worldwide attention, and in 2023 introduced GPT‑4, a multimodal system with even stronger capabilities.

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