History of AI

Introduction

AI didn't appear in 2022 with ChatGPT — it has a 70-year history of breakthroughs and 'AI winters' (funding droughts when hype outran reality). Understanding the timeline helps you separate marketing from substance.

Beginner analogy: AI's history is like rocketry — decades of research, several spectacular failures, and then suddenly SpaceX-style commercial liftoff with deep learning.

Understanding the topic

Core concepts to understand:

• 1956 — Dartmouth Conference coins the term 'Artificial Intelligence'.
• 1980s — Expert systems boom; early neural networks (backpropagation).
• 1997 — IBM Deep Blue beats Garry Kasparov at chess.
• 2012 — AlexNet wins ImageNet → Deep Learning revolution.
• 2017 — Google publishes Attention Is All You Need → Transformers.
• 2020 — GPT-3 (175B parameters) shocks the world.
• 2022 — ChatGPT launches; AI goes mainstream.
• 2023+ — Multimodal LLMs, AI agents, enterprise rollouts.

Syntax reference

Visual workflow / architecture:

1956 ─── Dartmouth (AI is born)
1980 ─── Expert systems
1997 ─── Deep Blue beats Kasparov
2012 ─── AlexNet → Deep Learning
2017 ─── Transformers (Attention paper)
2020 ─── GPT-3
2022 ─── ChatGPT (mainstream)
2023 ─── GPT-4, Claude, multimodal
2024 ─── AI agents, voice, video
2025+ ── Enterprise AI adoption

Real-world use

Each breakthrough enabled the next. Without GPUs (gaming) → no AlexNet → no deep learning → no transformers → no ChatGPT. Today's LLMs literally stand on 70 years of compounding research.

Best practices

• Read landmark papers — Attention Is All You Need (2017), GPT-3 (2020), Chinchilla (2022).
• Watch for new milestones; the field moves monthly, not yearly.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What were AI winters and why did they happen?
• Q2. Why did deep learning explode after 2012?
• Q3. What did the 2017 Transformer paper change?