A short history of AI

AI did not begin with chatbots. As a research field it took shape in the mid-1900s. The phrase artificial intelligence became widely used after the 1956 Dartmouth workshop, where researchers proposed that aspects of learning and intelligence could be simulated on computers.

Progress was not a straight climb. There were boom periods, “AI winters” when funding cooled, and later comebacks when data, algorithms, and hardware finally matched old ambitions.

History cuts hype. When someone says AI is brand new, you can place today’s tools on a longer timeline—and notice which ideas (search, probability, neural nets) are old wine in new bottles.

• 1950s–1970s: Symbolic AI—logic, search, hand-built knowledge. Good demos on tidy puzzles; weak on messy perception.
• 1980s: Expert systems—commercial rule bases for narrow domains. Valuable when experts could write rules; brittle when they could not.
• 1990s–2000s: Statistical machine learning—learn weights from data; stronger vision and speech.
• 2010s–today: Deep learning at scale, GPUs, huge datasets; then transformers (2017 paper) powering modern language models and generative apps.

A chess engine using search + evaluation sits in the classical tradition. A photo app that tags faces sits in deep learning. A chatbot sits in generative LLMs. All are “AI,” different eras’ tools.

Write a three-line timeline: one classical idea still used, one ML idea from the 90s/00s, one post-2012 deep learning idea.

⚠️ Myth: AI was invented yesterday with ChatGPT.

✓ Reality: Chat products popularized LLMs; the field is decades old.

⚠️ Myth: Symbolic AI disappeared.

✓ Reality: Search, planning, and logic still power maps, scheduling, games, and parts of agents.

• Berkeley CS188: https://inst.eecs.berkeley.edu/~cs188/ ↗
• Vaswani et al., “Attention Is All You Need” (2017): https://arxiv.org/abs/1706.03762 ↗
• ANN live: https://www.ainerdnetwork.com/learn/history-of-ai ↗