• How AI was born from a simple question: "Can machines think?"
• The brilliant pioneers who dared to dream of intelligent machines
• Why AI went through not one, but TWO "winters"
• How overpromising and underdelivering nearly killed the field
• Why understanding this history helps us appreciate today's AI revolution

1940s The world's first electronic computer, ENIAC, filled an entire room and weighed 30 tons!

This massive machine could do calculations faster than any human, but it was basically a very expensive calculator. The dream of true intelligence was still just that - a dream.

But here's what's amazing - some visionaries looked at these room-sized calculators and asked: "What if we could make them think like humans?" That question would change everything.

1950 Alan Turing! This brilliant British mathematician asked the question that started it all:

But Turing was clever. He realized this question was too philosophical, so he created something practical: The Turing Test

• It's a blind conversation test
• Talking through text
• If the machine fools us into thinking it's human, it passes!
• This test is STILL used today to measure AI capabilities

1956 The most important summer camp in history! For two months at Dartmouth College, four brilliant minds gathered to officially create the field of Artificial Intelligence.

• John McCarthy - Coined the term "Artificial Intelligence"
• Alan Newell - Logic and reasoning expert
• Herbert Simon - Problem-solving pioneer
• Marvin Minsky - Neural network visionary (more on him later!)

These four visionaries believed they could create machines that could learn, reason, and solve problems just like humans. But they were too ambitious.

1974-1980 After all the excitement, reality hit. The field entered what we now call the "First AI Winter"!

• Machine Translation Failures: Computers couldn't translate languages properly. The famous example: "The spirit is willing but the flesh is weak" became "The vodka is good but the meat is rotten"!
• Marvin Minsky's Neural Network Criticism: One of AI's founding fathers said neural networks couldn't even solve simple XOR problems
• Speech Understanding Disappointments: Computers still couldn't understand human speech reliably

Marvin Minsky

The XOR Problem

The lesson? Promising too much too soon can really backfire! Researchers had to learn to be more realistic about what AI could actually achieve.

Late 1980s-1990s The second AI Winter was even worse than the first. This time, AI didn't just lose funding - it lost respectability!

• Theoretical computer scientists: "AI lacks mathematical rigor - it's not real science!"
• Practical engineers: "AI solutions don't actually work in the real world!"
• Funding agencies: "We've been burned too many times - no more AI grants!"
• Conferences: AAAI attendance dropped from 5,000 to under 1,000!

Understand why these winters happened and what we can learn from them:

Both winters were caused by overpromising and underdelivering. Researchers got so excited about possibilities that they forgot about practical limitations.

Many ideas from the early days (like neural networks) were actually good - they just needed more powerful computers and better algorithms to work properly.

It takes years to build trust and only a few failed promises to destroy it. The AI community learned this the hard way - twice!

The story doesn't end here though - those sub-fields that distanced themselves from AI? They kept working and eventually created the AI revolution we see today!

Here's the amazing ending to our story: Remember those conferences that saw attendance drop to under 1,000? Well, the video mentions that modern AI conferences like NeurIPS now see massive growth!

• Machine Learning: Became incredibly powerful with big data
• Computer Vision: Now recognizes images better than humans
• Natural Language Processing: Gave us ChatGPT and modern AI assistants
• Neural Networks: The thing Minsky criticized? They're now the foundation of modern AI!

After surviving two brutal AI winters, the field was ready for its comeback story. The 1990s marked a turning point where AI finally started delivering on some of its promises.

This decade introduced us to a fascinating phenomenon that continues today - once AI solves a problem, people suddenly decide it wasn't "real" AI after all! (Like a magic tricks)

1996 Before 1996, AI faced serious skepticism. Critics said machines could never be truly creative or intelligent. Then something remarkable happened...

This was a watershed moment for logic-based AI. For the first time, a machine had made a genuine mathematical discovery that advanced human knowledge.

This reluctance to acknowledge machine intelligence would become a recurring theme. People were ready to accept AI's capabilities but not quite ready to call it "intelligent."

1997 Chess had been the true test of AI since the field began. If a machine could beat the world's best human chess player, surely that would prove machine intelligence, right?

• 1996 First Match: Kasparov wins 4-2 against Deep Blue's predecessor "Deep Thought"
• 1997 Rematch: Improved Deep Blue defeats Kasparov 3.5-2.5
• Historic Moment: First time a machine beat a human world champion at chess
• Kasparov's Quote: "I felt human-level intelligence across the room"

But here's where our story gets interesting - instead of celebrating machine intelligence, critics immediately said: "That's not real AI, it's just brute force search!" The goalposts had officially started moving.

Here's one of the most important concepts in AI history. This phenomenon explains why AI researchers joke they'll "never be out of jobs!"

• Step 1: "Machines will never be able to [X]"
• Step 2: AI achieves [X]
• Step 3: "Well, that's not REAL intelligence, it's just [algorithm/brute force/pattern matching]"
• Step 4: Focus shifts to new impossible task [Y]

This ensures that AI always appears to be just beyond our current capabilities, no matter how much we achieve!

2005 Picture this: A 132-mile race through the Navajo desert with no human drivers allowed. This was the moment autonomous vehicles proved they could work in the real world!

Stanley (Winner) - Stanford

Sandstorm (2nd Place) - CMU

• Winner: Stanford's "Stanley" led by Sebastian Thrun (using probabilistic reasoning)
• Expected Winner: Carnegie Mellon (had long history in self-driving cars)
• Plot Twist: CMU's "Highlander" failed due to engine problems
• Legacy Impact: Thrun joined Google, contributing to modern self-driving technology

2010s If chess was AI's first major victory, then Jeopardy! was its coming-of-age party. This time, AI had to understand natural language, context, and even wordplay!

Unlike chess, which is pure logic, Jeopardy! required Watson to:

• Understand natural language and word games
• Process vast amounts of knowledge instantly
• Deal with ambiguity and context
• Make strategic decisions about wagering

This victory demonstrated that AI was moving beyond games into areas that required human-like language understanding and reasoning!

AI's journey can be understood through three distinct eras, each with its own dominant approach:

Focus on goal achievement through search algorithms and function representation through neural networks. Neural networks faced limitations due to computational constraints and reproducibility issues.

For about 30 years, logic-based or symbolic AI dominated, using combinatorial algorithms and rule-based systems.

The rise of probabilistic AI, with Judea Pearl's groundbreaking work on Bayesian networks earning him a Turing Award.

You might wonder - why did AI suddenly explode in the 2010s?

• 💡 Algorithms: Many fundamental algorithms were actually developed in the 1990s - they just needed better conditions
• ⚡ Compute Power: GPUs (graphics cards) provided massive parallel processing power needed for neural networks
• 📊 Data: The internet age provided vast datasets necessary for training complex AI models

Today's AI can do things that would have seemed like pure magic just a decade ago. Let me show you some mind-blowing applications that are now routine:

• 🎨 Style Transfer: Combine the content of one image with the artistic style of another
• ✏️ Doodle to Painting: Transform simple sketches into detailed, realistic artwork
• 🌈 Image Colorization: Automatically add realistic color to black and white photographs
• 📝 Image Captioning: Generate accurate, descriptive captions for any image

Each of these applications seemed impossible a few years ago, yet now they're available as smartphone apps. This rapid progression shows how quickly AI capabilities are advancing.

Despite all the amazing progress, AI is definitely "not solved yet." Let me give you some perspective on current limitations and realistic concerns:

• 🚫 Killer Robots: Fears of AI enslaving humans are unrealistic science fiction
• 💼 Job Displacement: More realistic concern - certain jobs (like truck driving) may be automated
• 🏃 Robot Locomotion: Robots still struggle with basic movement and balance
• 👁️ Perception: Understanding 3D environments remains challenging
• 🎯 Strategy: Complex real-world planning and adaptation need work

The key is maintaining realistic expectations while appreciating genuine progress. We're living through a remarkable period of AI advancement, but there's still a vast frontier of research ahead!

This is nearly 80 years of AI history, from room-sized computers to modern neural networks that surpass human performance.

• 🎢 Cyclical Progress: AI advances in waves - winters and springs, setbacks and breakthroughs
• 🎯 Moving Goalposts: "If it works, it's not AI" - success paradoxically diminishes perceived intelligence
• ⏰ Timing Matters: Good ideas often need to wait for supporting technology
• 🔄 Rediscovery: "Failed" approaches often become tomorrow's breakthroughs
• 📏 Realistic Expectations: Overpromising leads to winters; steady progress builds trust