1.2. A Brief History of AI
Hou I (Esther) Lau
Understanding the history of artificial intelligence helps us see today’s tools in context rather than as sudden, alien inventions. By tracing how ideas about mechanical reasoning and symbolic systems developed over time, we begin to recognize AI not as an abrupt break from the past, but as part of a longer human effort to extend knowledge and problem-solving through technology. This genealogy reminds us that current debates about power, ethics, creativity, and intelligence are continuations of questions people have been asking for generations.
From Mechanical Dreams to Digital Systems
Artificial intelligence did not begin with computers. Its origins reach back to the nineteenth century, when Charles Babbage designed the Analytical Engine and Ada Lovelace recognized that such a machine could manipulate symbols, not only numbers. Their vision planted the idea that machines might one day imitate aspects of human thought.
Key Milestones (click arrows to expand)
📜 Early Foundations
- 1800s: Babbage and Lovelace imagined machines capable of more than calculation. Lovelace in particular anticipated creative and symbolic uses for computing.
🧠 The Mid-Twentieth Century
- 1950: Alan Turing asked “Can machines think?” and introduced the Turing Test as a measure of machine intelligence. The Turing Test is based on whether a human evaluator can distinguish a machine’s text-based conversation from that of a human.
- 1956: At the Dartmouth Summer Research Project on Artificial Intelligence, John McCarthy coined the term Artificial Intelligence, marking the official start of the field.
- 1960s–1980s: AI research focused on symbolic logic and rule-based “expert systems.” These systems showed promise but were limited and costly to scale.
❄️ The AI Winter
- In the 1970s and again in the late 1980s, optimism about AI collapsed. Funding dried up, progress stalled, and researchers called this period the AI Winter. Still, work continued in smaller communities, setting the stage for later breakthroughs.
🚀 Renewal and Generative AI
- By the 2000s, the combination of big data, faster processors, and new algorithms reignited the field. The 2010s saw rapid progress in machine learning, image recognition, and natural language processing. In the 2020s, generative AI tools such as ChatGPT, Gemini, and Midjourney demonstrated how far machine intelligence had advanced.
📖 Analogy: Cathedral Construction
The history of AI resembles the building of a medieval cathedral. No single generation completed the structure; instead, each added foundations, arches, or stained glass that made the next stage possible. Some parts remained unfinished for centuries until new tools or resources revived the work. Generative AI today is not a sudden invention; it is the latest vaulted ceiling built upon long-laid stones.
📚 Weekly Reflection Journal
Which part of AI’s history surprises you most: the early visionaries, the setbacks of the AI Winter, or the sudden rise of generative systems? Write down a few notes in your reflection journal document.
Looking Ahead
AI’s history shows that progress has been uneven, with cycles of enthusiasm and disappointment. Understanding this context helps us see today’s generative tools as part of a longer story rather than a sudden revolution. In the next section we will clarify what AI actually is and how to distinguish between different models and categories.
