Editing History of Artificial Intelligence (section)

== <span style="color: #FFFFFF;">Applying</span> ==
The historical timeline reveals recurring patterns useful for understanding the present:
<syntaxhighlight lang="text">
AI History Key Milestones:

1943  McCulloch-Pitts: First mathematical model of artificial neuron
1950  Turing: "Computing Machinery and Intelligence"
1956  Dartmouth: "Artificial Intelligence" coined
1957  Rosenblatt: Perceptron
1969  Minsky & Papert: "Perceptrons" book — shows XOR limitation
1974  First AI Winter begins (Lighthill Report)
1980  Expert systems boom (XCON, MYCIN)
1986  Rumelhart, Hinton: Backpropagation (rediscovered)
1987  Expert system market collapses — Second AI Winter
1989  LeCun: Convolutional networks for handwriting recognition
1997  Deep Blue defeats Kasparov at chess
1998  LeCun: LeNet for MNIST (modern CNN architecture)
2006  Hinton: Deep Belief Networks — "deep learning" revival
2009  ImageNet dataset created (Li Fei-Fei)
2012  AlexNet wins ImageNet challenge — deep learning explosion
2013  Word2Vec (Mikolov): word embeddings
2014  GANs introduced (Goodfellow)
2015  ResNet: very deep networks via residual connections
2016  AlphaGo defeats Lee Sedol at Go
2017  "Attention Is All You Need" — Transformer architecture
2018  BERT: bidirectional pre-trained language models
2019  GPT-2: larger language model, feared for misuse
2020  GPT-3 (175B): few-shot in-context learning
2020  AlphaFold2: solves protein folding
2021  DALL-E, CLIP: vision-language models
2022  ChatGPT: RLHF + LLM → conversational AI at scale
2022  Stable Diffusion: open image generation
2023  GPT-4, Claude, Gemini: multimodal frontier models
2023  Llama 2: open-weight LLMs mainstream
2024  Gemini 1.5 Pro (1M context), Claude 3 Opus
2024  Reasoning models (o1, DeepSeek-R1): inference-time scaling
</syntaxhighlight>

; Key lessons from AI history for practitioners
: '''Hype cycles are real''' → Every AI breakthrough is initially overhyped; expect ~5-10 years to practical deployment
: '''Data beats algorithms''' → More data typically matters more than cleverer algorithms (ImageNet lesson)
: '''Compute enables new paradigms''' → GPU → deep learning; TPU → LLMs; new hardware unlocks new paradigms
: '''Simple methods scale surprisingly well''' → SGD, transformers, attention — simple ideas + scale win over clever complexity
: '''AI winters result from misaligned expectations''' → Underpromise and deliver to avoid repeating history
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">