History of Artificial Intelligence | AI Fundamentals Course | 1.2

We’re about to travel back decades (even centuries) to trace the roots of one of the most revolutionary fields of our time:  Artificial Intelligence.

AI didn’t just pop out of Silicon Valley a few years ago.  Nope.  It’s been brewing in the minds of philosophers, mathematicians, scientists, and dreamers for a very long time.  From ancient myths to modern machine learning models, AI’s story is a wild ride full of ideas, breakthroughs, hype, winters, and impressive comebacks.

So buckle up, because we’re about to break down the major historical milestones in the development of AI, in a way that actually makes sense.

Ancient Greece & Mythology

Let’s kick things off way back.  The idea of creating intelligent, human-like machines isn’t new at all.  Ancient civilizations were already fantasizing about it.

• Greek Mythology:  Stories from Homer and Hesiod talk about intelligent statues and golden robots.  The god Hephaestus supposedly built mechanical servants.  Creepy?  Maybe.  Futuristic?  Definitely.
• The Jewish Golem Legend:  In Jewish folklore, a golem was a clay creature brought to life to protect the people.  It was an early version of an artificial human.  Just don’t pull the wrong string – it could go haywire.

These stories didn’t involve silicon chips or algorithms, but they reflected a basic human desire:  to build things in our image that can “think” and “act” for us.

1600s – 1800s:  Philosophers & Mathematicians Lay the Groundwork

Fast forward a few thousand years to the thinkers who started making the dream a bit more realistic.

Rene Descartes & the Mind-Body Question

Descartes proposed that the human body was like a machine & that thought was a separate process.  This opened up philosophical debates about whether machines could one day “think”.

Gottfried Wilhelm Leibniz

In the late 1600s, Leibniz imagined a universal language of logic – an idea that would later inspire programming languages & AI logic systems.

Charles Babbage & Ada Lovelace (1800s)

• Charles Babbage designed the Analytical Engine, the first concept of a programmable machine.
• Ada Lovelace is considered the world’s first computer programmer.  She theorized that Babbage’s machine could do more than math – it could even compose music if programmed correctly.

They didn’t have the hardware, but they had the vision.

1930s – 1940s:  The Birth of Computing & Theoretical AI

Alan Turing:  The OG of AI

In 1936, Alan Turing published a paper that laid the foundation of computer science.  He described a theoretical machine (now called a Turing Machine) that could simulate the logic of any computer algorithm.  Then, during World War II, Turing helped crack the Nazi Enigma code using an early electromechanical computer.  But it was in 1950 that Turing really hit the AI scene with his paper “Computing Machinery and Intelligence”.

• He posed the now-famous question:  “Can machines think?”
• Introduced the Turing Test – if a machine could converse with a human without the human realizing it’s a machine, it’s intelligent.

Spoiler:  We’re still chasing the goal today.

1956:  The Official Birth of AI

The Dartmouth Conference

This is the moment most historians call the official birth of AI.

In 1956, John McCarthy, Marvin Minsky, Allen Newell, Herbert Simon, and others gathered at Dartmouth College for a summer workshop.

• McCarthy coined the term “Artificial Intelligence”.
• They proposed that “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”.

That statement was bold – and perhaps a bit premature – but it sparked a movement.

1950s – 1960s:  The First AI Programs

After Dartmouth, things moved fast.

Logic Theorist (1955)

• Built by Newell & Simon.
• The program could prove mathematical theorems & was called the “first artificial intelligence program”. 

General Problem Solver (1957)

• Another Newell & Simon project.
• Could solve a wide range of logic problems, using rules & a goal-directed approach.

ELIZA (1966)

• Developed by Joseph Weizenbaum.
• One of the first chatbots.
• Simulated a therapist by reflecting questions back at users.  For example:
  • User:  “I’m sad.”
  • ELIZA:  “Why are you sad?”
• Not truly intelligent, but very convincing for its time.

People started to think machines could be “almost human”.  But this optimism wouldn’t last forever.

1970s:  The First AI Winter

What Happened?

Researchers made bold predictions in the ‘60s & early ‘70s – like AI would soon outperform humans in everything.

Spoiler:  It didn’t.

Problems:

• Computers were still too slow & expensive.
• AI systems couldn’t handle real-world problems well.
• Funding dried up when the hype didn’t deliver.

Governments & companies pulled back support.  The AI party was over – for now.

1980s:  Expert Systems & a Bit of a Comeback

AI bounced back in the ‘80s thanks to something called expert systems.

What are Expert Systems?

These were rule-based programs designed to mimic the decision-making of human experts.

Ex:  MYCIN

• Created to diagnose bacterial infections.
• Used “if-then” rules to simulate how a doctor might think.

Why the Comeback?

• These systems had real-world use in business & healthcare.
• Corporations saw the value & started investing.

But…it didn’t last forever.  As systems grew, they became hard to maintain.  The magic faded again, and we headed into…

The Second AI Winter (Late 1980s – 1990s)

Once again, the hype train crashed.

Reasons:

• Expert systems couldn’t scale.
• They couldn’t learn from new data.
• Maintenance was expensive.
• Funding dried up again.

This second “winter” was colder & lasted longer.  But in the shadows, something new was brewing.

1997:  Deep Blue Beats Garry Kasparov

This was a huge moment.  IBM’s Deep Blue defeated world chess champion Garry Kasparov in a six-game match.  Why it mattered:

• It showed that AI could beat the best human in a complex game.
• Deep Blue didn’t “think” like a human – it used brute force & strategy.
• But it was proof that machines could win in elite-level cognitive challenges.

And that turned heads again.

2000s:  The Rise of Data & Machine Learning

Welcome to the internet era, where two key things changed the game:

• Massive Amounts of Data
  • We started generating at an unimaginable scale:  emails, social media, photos, video, online behavior.
• Cheaper, Faster Computing
  • Moore’s Law and cloud computing made it easier to process all that data.
• Result?  A new wave of AI powered  by machine learning.
  • Instead of hand-coding rules, developers trained models to “learn” from data.

2012:  Deep Learning Changes Everything

Big Moment Alert

In 2012, a neural network called AlexNet crushed the ImageNet competition – an actual challenge where computers try to identify objects in images.

AlexNet used deep learning, a type of machine learning that mimics the human brain using layers of artificial neurons.

Why It Was Huge:

• It cut the error rate in half.
• It reignited global interest in AI.
• It showed that deep learning could outperform traditional methods.

This kicked off the modern AI boom.

2016:  AlphaGo Beats the Go Master

You might’ve heard about chess, but how about Go?

It’s a 2,500 year old Chinese board game known for its complexity.  Humans were unbeatable…until AI showed up.

Enter:  AlphaGo by DeepMind (Google’s AI Company)

• It beat world champion Lee Sedol in 2016.
• It didn’t just win – it made creative moves no human had ever thought of.

It was a turning point.  AI could now master tasks that required intuition & creativity.

2018 – Today:  AI Everywhere

We are now living in the AI Renaissance.  Let’s look at some recent milestones.

GPT Models (OpenAI)

• GPT-2 (2019):  Started generating realistic text.
• GPT-3 (2020):  175 billion parameters, shocking fluency.
• ChatGPT (2022 – 2023):  Brought AI to the masses, sparking worldwide conversation about AI’s role in work, education, and society.

DALL-E & Midjourney

• AI can now generate images from text prompts.  Want a “cat riding a skateboard in space”?  No problem.

AI in Real Life

• Healthcare:  Diagnosing diseases from X-rays.
• Finance:  Fraud detection, stock predictions.
• Transportation:  Self-driving cars.
• Entertainment:  Netflix & YouTube recommendations.
• Education:  Personalized learning tools.

Looking Ahead:  What’s Next?

We’re now talking about:

• AGI (Artificial General Intelligence):  Machines that can perform any intellectual task a human can.
• Ethics & Regulation:  How do we ensure AI is used responsibly?
• Job Disruption:  Will AI take jobs or create new ones?
• AI & Creativity:  Can AI truly create – or is it just remixing?

The future is wide open, and we’re all part of it.

Milestones That Mattered

Here’s a quick list of highlights you can come back to:

From Fantasy to Reality

What started as ancient dreams of mechanical beings has evolved into a tech revolution touching every part of our lives.  AI isn’t science fiction anymore – it’s science fact.  And understanding how we got here gives us the tools to navigate where we’re going next.

Whether you’re studying AI, working with it, or just curious about the future, one thing is clear:  this journey is just getting started.

Let’s keep learning – because the best AI stories haven’t been written yet.