Artificial Intelligence didn’t begin with ChatGPT — and it’s not just about futuristic robots or sci-fi fantasies. The story of AI spans nearly a century, marked by breakthroughs in logic, computing, and neuroscience that have gradually brought machines closer to mimicking human reasoning.
In this post, we’ll take a journey through the real history of AI — not the hype, but the turning points that shaped the field from its earliest days to the generative AI explosion we’re living through now. I’ll also reflect on how some of the concepts I’ve studied — from machine learning to AI ethics — tie back to these foundational ideas.
Let’s go.
Table of Contents
🧮 The Birth of the Idea: Turing, Shannon, and the Dartmouth Spark
Our story begins in the early 20th century. Long before smartphones or neural networks, people like Alan Turing laid the groundwork with abstract ideas about computation. In 1936, Turing introduced the concept of a universal machine — what we now call the Turing Machine — which could simulate any other machine’s logic.
By 1950, he was asking the big question: Can machines think? His proposed answer — the now-famous Turing Test — became the first real framework for measuring machine intelligence.
Meanwhile, Claude Shannon was pioneering information theory, and John von Neumann laid out the architecture that modern computers still use today.
But the real milestone came in the summer of 1956, at the Dartmouth Conference. That’s where the term Artificial Intelligence was coined, and a bold proposal was made: every aspect of human intelligence could, in theory, be simulated by a machine.
It was a moment of genius — and hubris. The field of AI was born.
♟️ Early Wins and the First AI Winter
The years following Dartmouth were filled with excitement — and some surprisingly impressive early projects.
One standout was The Logic Theorist (1956), a program that could prove mathematical theorems, sometimes even better than the humans who originally wrote them.
Then there was ELIZA (1966), a text-based chatbot that simulated a therapist. While simple, it felt eerily human to many users, so much so that some formed emotional connections. That was one of the first ethical dilemmas AI ever presented: if a machine sounds intelligent, does that make it so?
But these systems couldn’t handle ambiguity or real-world complexity. Machine translation efforts failed spectacularly. By the 1970s, disillusionment set in, and funding dried up.
This period became known as the first AI winter, when progress slowed, skepticism rose, and trust was lost.
💻 The Expert Systems Era: Smarts Without Learning
In the 1980s, AI staged a comeback through expert systems — software that encoded the decision-making logic of human experts.
One of the most well-known was MYCIN, a medical system that diagnosed infections. It performed well but wasn’t used in hospitals because it couldn’t explain why it made its decisions. That trust gap proved critical.
Expert systems experienced a brief surge in corporate environments. But they had a fatal flaw: they didn’t learn. Every rule had to be programmed by hand, and updating them became unsustainable. As complexity grew, these systems collapsed under their own weight, triggering the second AI winter.
Still, they taught us key lessons about explainability, scale, and the limits of static logic.
📊 Machine Learning: Letting Data Do the Talking
Out of the second winter came a new idea: what if machines could learn from data instead of being told what to do?
This was the dawn of machine learning, and it shifted the game entirely.
Instead of hard-coding rules, you trained models on examples. Spam filters became adaptive. Fraud detection evolved to spot patterns, not just fixed thresholds. Recommendation engines got smarter.
Three factors made this possible in the 1990s and 2000s:
- More data, thanks to the internet
- Better algorithms, like support vector machines
- Faster processors, especially with GPUs
AI was no longer a lab curiosity. It was quietly reshaping industries — and setting the stage for something even bigger.
🔧 Deep Learning and the ImageNet Breakthrough
While traditional machine learning was powerful, it still required manual feature engineering — identifying what aspects of the data mattered.
Deep learning changed that.
Using neural networks with many layers, deep learning models could extract patterns from raw data. They didn’t need to be told what a cat looked like — they figured it out themselves.
The turning point came in 2012, when a model called AlexNet outperformed the competition in the ImageNet challenge, reducing error rates by half. The ingredients? Massive datasets, GPU acceleration, and new training techniques.
Suddenly, AI could:
- Recognize faces and objects
- Understand and transcribe speech
- Translate languages in real time
- Even detect tumours and drive cars
But deep learning had a dark side: it was accurate, but opaque. These “black box” systems couldn’t always explain their decisions — a challenge we’re still grappling with today.
🚀 Transformers and the Generative AI Revolution
Then came 2017.
Google published the now-legendary paper: “Attention Is All You Need.” It introduced the transformer architecture, and it changed everything.
Transformers could analyze entire sequences of text at once using self-attention, allowing them to understand language with greater context and accuracy than ever before.
This gave rise to:
- BERT (Google)
- GPT (OpenAI)
- And many others (Meta, Mistral, Anthropic)
By 2020, OpenAI had released GPT-3, and in late 2022, ChatGPT put generative AI in the hands of the public.
Now, AI could generate:
- Blog posts, emails, and reports
- Meeting notes and summaries
- Code, artwork, and even videos
Suddenly, AI wasn’t just something in the background. It was sitting at the table with us, helping us work, write, and create.
But this newfound power also raised urgent questions:
- What happens when AI makes things up?
- How do we deal with biased or misleading outputs?
- Who owns the content that AI generates?
🧑🤝🧑 Where We’re Headed: Two Possible Futures
Today, we’re standing at another inflection point — and two paths lie ahead.
1. Smarter, More Integrated AI
AI is becoming a layer in every tool we use — from Microsoft Copilot to Google Gemini. Multimodal models can now understand text, images, and audio together. Open-source models are gaining traction, making powerful tools more accessible.
This democratization is exciting, but it also makes responsible development and oversight more complicated.
2. Toward Artificial General Intelligence (AGI)
Some labs are pursuing AGI — machines that can reason across domains, much like humans. Others are skeptical, pointing out that today’s models still rely on pattern recognition, rather than genuine understanding.
Regardless of who’s right, one thing is clear: the risks are no longer theoretical. Deepfakes, misinformation, job displacement — they’re already here. That’s why policy, ethics, and public awareness matter more than ever.
💭 Final Thoughts: What Should We Build?
We’ve gone from Turing’s abstract machines to today’s generative models that can mimic language, vision, and reasoning.
But the future of AI isn’t just about what we can build — it’s about what we should make.
Every leap in capability brings new blind spots. Let’s focus not just on pushing the boundaries of possibility, but on building systems that are transparent, trustworthy, and genuinely beneficial.
Because AI isn’t just a technical challenge. It’s a human one.
🎧 **Transcript** >> Click here to view or download the full episode transcript