Chapter 47: The History of AI

The History of Artificial Intelligence (AI)

I’m going to tell it like your favorite teacher — slowly, honestly, with real stories, Hyderabad/Indian connections where they exist, simple analogies you’ll remember forever, and without hiding the hype cycles, the bitter winters, the ethical fights, the genius moments, and the open questions that still keep researchers awake at night in 2026.

Let’s begin with the most important sentence of the whole lesson:

AI is not a technology that suddenly appeared in 2022 with ChatGPT. AI is humanity’s 70+ year dream of creating machines that can think (or at least appear to think) like us.

That dream has had golden summers, long cold winters, huge promises, huge disappointments, and right now (February 2026) we are in one of the hottest summers ever — but history tells us summers are usually followed by winters unless we are very careful.

Phase 1: The Dream Takes Shape (1940s – 1956)

Before anyone said the word “Artificial Intelligence”, several big ideas already existed:

• Alan Turing (1950) — “Computing Machinery and Intelligence” → The famous Turing Test: “Can a machine converse so well that a human cannot tell it is a machine?” → Turing predicted yes by year 2000 (he was wrong about the date, but right about the question).
• Cybernetics (1940s–1950s) — Norbert Wiener → Machines that use feedback loops (like thermostat or self-guided missile) → early idea of control & learning.
• Neural networks (1943) — McCulloch & Pitts → First mathematical model of artificial neuron → inspired by real brain cells.

Then the official birth:

1956 — Dartmouth Summer Research Project Organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, Claude Shannon → First time the term “Artificial Intelligence” was officially used → Famous prediction: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

→ They thought major breakthroughs would happen in 10–20 years.

→ Optimism was sky-high.

Phase 2: First Golden Age & First Winter (1956 – 1974)

What worked:

• Logic & theorem proving — Newell & Simon’s Logic Theorist (1956) proved mathematical theorems
• Early neural nets — Frank Rosenblatt’s Perceptron (1958) — could learn simple patterns
• Game-playing — Arthur Samuel’s checkers program (1959) learned by playing against itself
• Natural language — ELIZA (1966, Joseph Weizenbaum) — simulated a Rogerian psychotherapist (very simple pattern matching, but people thought it understood them)

What didn’t work:

• Perceptrons could only learn linearly separable problems → Minsky & Papert’s book (1969) showed they couldn’t learn XOR → killed funding for neural networks for 15+ years
• Programs were brittle — worked only in tiny, hand-crafted worlds
• Combinatorial explosion — searching all possibilities became impossible for real problems

First AI Winter (~1974–1980) → Funding dried up in US & UK → “AI” became a dirty word in academia

Phase 3: Expert Systems Boom & Second Winter (1980 – 1993)

Expert Systems — rule-based programs that captured human experts’ knowledge in “if-then” rules

• MYCIN (1970s) — diagnosed bacterial infections better than junior doctors
• XCON (DEC, 1980s) — configured VAX computers → saved company millions
• Japan’s Fifth Generation Computer Project (1982–1992) — $400M+ government funding → created huge hype

Then collapse:

• Expert systems were brittle (failed outside narrow domain)
• Knowledge acquisition bottleneck — very hard to extract rules from experts
• Lisp machines bubble burst (expensive hardware)
• Second AI Winter (~1987–1993) — funding collapsed again

Phase 4: Quiet Progress & Machine Learning Rise (1993 – 2011)

While “AI” was unfashionable, several fields quietly advanced:

• Machine Learning (not called AI) — backpropagation revived (1986), support vector machines (1995), random forests (2001)
• Big Data — internet created huge datasets
• Better hardware — Moore’s Law, GPUs
• Specific wins
  • Deep Blue beats Kasparov in chess (1997)
  • IBM Watson wins Jeopardy! (2011)
  • Roomba vacuum robots everywhere

Phase 5: Deep Learning Explosion & Current Boom (2012 – 2026)

2012 — AlexNet (Krizhevsky, Hinton, Sutskever) wins ImageNet by huge margin → Deep convolutional neural networks + GPUs + big data = magic

Then avalanche:

• 2014–2016 — DeepMind AlphaGo beats world Go champion (2016)
• 2017 — Transformer architecture (Vaswani et al.) → basis of all large language models
• 2018–2022 — GPT series (OpenAI), BERT (Google), T5, PaLM…
• 2022 — ChatGPT (late 2022) → public discovers conversational AI
• 2023–2025 — Multimodal models (GPT-4V, Gemini, Grok with vision), diffusion models (Stable Diffusion, Midjourney), agentic AI, robotics + foundation models
• 2025–2026 — Reasoning models (o1, o3 style), long-context windows (1M+ tokens), open-source LLMs catching up (Llama 3/4, Mistral, Qwen, DeepSeek), AI in robotics (Figure, Tesla Optimus, 1X)

Indian contributions (proud moments):

• IITs, IISc, IIIT-H — strong in deep learning theory & applications
• Bhashini (2022–) — Indian language AI
• Sarvam AI, Krutrim, CoRover — building Indian LLMs
• UPI + AI fraud detection — one of the largest real-time AI systems in the world

Quick Timeline – History of AI (Big Picture)

Final Teacher Words

The history of AI is a 70-year roller-coaster of:

• Wild optimism (“machines will think like humans in 20 years”)
• Crushing winters (“AI is impossible”)
• Quiet breakthroughs in labs
• Sudden explosions when hardware + data + algorithms align

In Hyderabad 2026 we live right in the middle of the hottest summer yet:

• Your phone has AI (face unlock, voice typing in Telugu)
• UPI fraud detection runs AI models
• Startups in HITEC City build AI agents
• Students use LLMs to study & write assignments

But history teaches us caution: Every previous summer ended in winter when promises outran reality.

So enjoy the current wave — but keep asking hard questions:

• Who controls the AI?
• Who pays the energy bill?
• What jobs will disappear?
• Can machines ever truly understand or feel?

Understood the long, thrilling, cautionary tale now? 🌟

Want to go deeper?

• The Indian contribution to AI (from Ramanujan to Bhashini)?
• Why AI winters happened & signs of the next one?
• Timeline of large language models (BERT → GPT → Grok)?
• How Hyderabad became one of India’s AI hotspots?

Just tell me — next class is ready! 🚀