The Complete History of AI: From Alan Turing to ChatGPT

Most people think artificial intelligence started with ChatGPT in November 2022. That is like saying music started with Spotify. The app is how most of us experienced it for the first time, but the story behind it is decades deep, full of brilliant people, broken promises, and two complete collapses that nearly killed the entire field.

If you want to understand where AI is going, you need to understand where it came from. Not the sanitized highlight reel, but the real story, including the parts where everything fell apart.

I put together this guide because the teams I train across Michigan's Great Lakes Bay Region keep asking the same question: how did we get here? This article is my answer. We are going to trace the entire arc of artificial intelligence, from a theoretical paper in 1943 to the technology reshaping every industry on the planet right now.

The Dreamers (1943 to 1956)

The ideas behind AI are older than most people realize. Long before anyone had a personal computer, researchers were asking a deceptively simple question: can a machine think?

The First Artificial Neuron

In 1943, two researchers named Warren McCulloch and Walter Pitts published a paper proposing that the human brain could be understood as a computational system. They created the first mathematical model of an artificial neuron. It was purely theoretical, built on logic and equations rather than hardware, but it planted the seed for every neural network that exists today. The deep learning models behind ChatGPT, Claude, and Gemini are all descendants of that 1943 idea.

Alan Turing and the Imitation Game

In 1950, the British mathematician Alan Turing published a paper called Computing Machinery and Intelligence. Instead of getting stuck on the philosophical debate about whether machines can truly think, Turing flipped the question. He proposed what he called the Imitation Game: if a machine can communicate well enough to fool a human into believing it is also human, does it matter how it arrives at its answers?

That idea became known as the Turing Test, and it is still referenced in AI research 75 years later. Turing did not have the technology to prove his theories. Computing machines had not advanced far enough. But he conceptualized artificial intelligence before anyone had even given it a name.

The Dartmouth Conference: Where AI Got Its Name

The real starting gun fired in the summer of 1956. A group of scientists gathered at Dartmouth College in New Hampshire for a workshop that would change history. John McCarthy, Marvin Minsky, Claude Shannon, and several others spent weeks debating one ambitious premise: every aspect of learning or any other feature of intelligence can be described precisely enough that a machine can simulate it.

At that workshop, John McCarthy coined the term artificial intelligence for the very first time. The group did not build a thinking machine that summer. But they formally established AI as an academic field, set the research agenda for decades to come, and lit a fuse that is still burning.

The First Steps (1960s to 1970s)

With AI officially defined as a field, researchers in the 1960s started building things. And some of what they built was surprisingly effective, even by modern standards.

ELIZA: The First Chatbot

In 1966, an MIT professor named Joseph Weizenbaum created ELIZA, a program designed to mimic a psychotherapist. It used basic pattern matching. If you typed something containing the word "mother," ELIZA would respond with something like "tell me more about your family." There was no real understanding happening at all.

But here is the part that still resonates today: people genuinely believed they were talking to a real person. Weizenbaum was so disturbed by how easily humans formed emotional connections with a simple text program that he spent the rest of his career warning about the dangers of trusting machines. Nearly 60 years later, we are having the exact same conversation about chatbots, just with far more capable technology.

Shakey: The First Thinking Robot

Around the same time, researchers at the Stanford Research Institute built Shakey, the first mobile robot that could perceive its environment, plan its actions, and carry them out. It was slow and clunky, but it could actually reason about what it was doing, and that had never been achieved before. Shakey represented a genuine leap from theory to physical reality.

The First AI Winter

Researchers were riding high on early successes, and predictions got wildly optimistic. In 1970, Marvin Minsky predicted that machines would match human intelligence within three to eight years. Governments were pouring in funding, expecting military and economic breakthroughs.

But the problems turned out to be exponentially harder than anyone anticipated. Language understanding, computer vision, common sense reasoning, none of it worked at scale. The gap between what researchers promised and what they could deliver grew embarrassingly wide.

By 1974, the US and British governments pulled funding for undirected AI research. The era known as the first AI Winter had begun. For nearly a decade, AI became something of a dirty word in academic circles. Researchers avoided using the term entirely just to get their work funded.

The Rise, the Fall, the Rise Again (1980s to 1990s)

The first AI Winter did not last forever. By the early 1980s, a new approach reignited the field, and this time the money was even bigger.

Expert Systems: AI Becomes Big Business

Expert systems were programs built on hundreds or thousands of hand-coded rules. If a customer needs X, recommend Y. If a patient shows symptom A, test for disease B. The first major commercial expert system, called XCON, launched in 1980 and reportedly saved its company millions of dollars per year by automatically configuring computer orders.

Suddenly, businesses could see real value in AI. In 1981, Japan announced an 850 million dollar national AI initiative called the Fifth Generation Computer Project. The United States and United Kingdom scrambled to launch competing programs. By the late 1980s, AI was a billion dollar industry.

The Second AI Winter

But expert systems had a fatal flaw: they could not learn. Every single rule had to be written by hand. They were expensive to build, expensive to maintain, and they broke down the moment they encountered a situation that nobody had programmed for. The maintenance costs alone became unsustainable.

By the early 1990s, the hype collapsed again. The second AI Winter arrived, and once more, funding dried up and public interest evaporated.

What Was Happening in the Background

This time, however, something fundamentally different was developing quietly behind the scenes. Researchers were advancing machine learning, an approach where instead of writing every rule by hand, you let the computer learn patterns from data. A technique called backpropagation, first described years earlier, was gaining traction for training neural networks. The computers of the 1980s and early 1990s were not powerful enough to make it practical at scale, but the theoretical foundation was being laid.

Then in 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov in a six-game match. Deep Blue was not machine learning in the modern sense. It used brute-force computation to evaluate millions of positions per second. But it proved that machines could outperform humans at complex cognitive tasks, and it captured the world's imagination. The stage was being set for something much bigger.

The Breakthrough (2000s to 2017)

Everything we have covered so far, the theories, the winters, the incremental progress, it all converges in this chapter. This is where AI went from promising to unstoppable.

The Three Forces That Changed Everything

Three things came together in the 2000s and 2010s that created the conditions for modern AI.

First, the internet created oceans of data. Text, images, video, audio, more training data than researchers had ever dreamed of having access to. Machine learning algorithms need data to learn from, and suddenly there was an almost infinite supply.

Second, graphics processing units (GPUs), originally built for rendering video game graphics, turned out to be perfect for the parallel mathematical operations required by neural networks. A single modern GPU could handle calculations that would have taken rooms full of 1990s-era computers.

Third, researchers figured out how to build neural networks with many layers, a technique that became known as deep learning. Each layer could learn increasingly abstract representations of the data, allowing the network to identify patterns that were invisible to simpler approaches.

AlexNet and the Deep Learning Revolution

In 2012, a deep neural network called AlexNet entered the ImageNet Large Scale Visual Recognition Challenge, a major annual competition for image classification. AlexNet did not just win. It crushed the competition by a margin so large that the entire field pivoted to deep learning almost overnight. That single result convinced the research community that deep neural networks were not just another approach. They were the approach.

AlphaGo: AI Surpasses Human Mastery

In 2016, Google DeepMind's AlphaGo defeated Lee Sedol, the world champion at Go, a board game with more possible positions than there are atoms in the observable universe. Chess had been solved through brute-force computation, but Go required something different: intuition, pattern recognition, and long-term strategic planning. AlphaGo demonstrated that AI was no longer just matching humans at specific tasks. It was surpassing them.

The Paper That Made Everything Possible

Then came the moment that truly changed the trajectory of artificial intelligence.

In June 2017, eight researchers at Google published a paper called Attention Is All You Need. They introduced a new architecture called the Transformer. The core innovation was a mechanism called self-attention, which allowed the model to look at every word in a sentence simultaneously and understand the relationships between all of them at once.

Previous AI systems processed language one word at a time, sequentially, like reading through a keyhole. The Transformer could see the entire page at once. This made it dramatically faster to train because it could process data in parallel, and it scaled beautifully. The bigger you made the model, the more capable it became.

The Explosion (2018 to Present)

After the Transformer paper, the pace of development accelerated faster than almost anyone predicted.

The Foundation Model Era Begins

In 2018, Google released BERT and OpenAI released the first GPT. Both were built on the Transformer architecture, but they used it differently. BERT was designed to understand language by reading in both directions simultaneously. GPT was designed to generate language by predicting the next word in a sequence.

OpenAI kept scaling. GPT-2, released in 2019, could write surprisingly coherent paragraphs and was initially withheld from full public release due to concerns about misuse. GPT-3, released in 2020, shocked the research community with capabilities that nobody had explicitly programmed. It could write code, translate languages, answer complex questions, draft legal documents, and generate creative writing, all from a model trained to do one simple thing: predict the next word.

ChatGPT Changes Everything

On November 30, 2022, OpenAI released ChatGPT. It was built on GPT-3.5 and fine-tuned using a technique called Reinforcement Learning from Human Feedback (RLHF) to make it better at following instructions and having natural conversations.

ChatGPT reached 100 million users in two months, making it the fastest growing consumer application in human history. Nothing had ever scaled that quickly. Not Facebook. Not Instagram. Not any app in any category, ever.

The ripple effects were immediate. Google declared a code red internally and fast-tracked its own AI chatbot, initially called Bard and later rebranded as Gemini. Microsoft invested billions in OpenAI and integrated the technology into Bing and its Office suite through Copilot. Anthropic launched Claude. Meta released Llama as an open source model. Within months, generative AI went from a niche research topic to the most discussed technology on the planet.

Where We Are Right Now

We are now in what researchers call the era of foundation models: large AI systems trained on broad datasets that can be adapted to a wide range of tasks. Instead of building a separate model for every specific problem, organizations can use a general-purpose model and customize it through prompting, fine-tuning, or connecting it to their own data.

The capabilities are expanding across every domain. Text, images, video, code, music, scientific research, drug discovery, protein folding. The models are getting more capable with each generation, and the pace of development shows no signs of slowing down.

What 80 Years of AI History Teaches Us

If you have read this far, you now know more about the history of artificial intelligence than the vast majority of people using it every day. Here are the patterns worth paying attention to:

Hype cycles are real, and they have consequences. AI has been through two full boom-and-bust cycles before the current one. Both times, overpromising led to funding collapses that set the field back years. The technology we have today is dramatically more capable than anything from those earlier eras, but the pattern of inflated expectations followed by disillusionment is worth remembering.

The breakthroughs came from persistence, not hype. The most important advances in AI history, neural networks, backpropagation, deep learning, the Transformer, were developed by researchers who kept working during the winters when nobody was watching and nobody was funding their work. The foundational breakthroughs did not come from billion dollar hype cycles. They came from patient, unglamorous research.

AI is built on specific technology, not magic. Every AI system you interact with today is built on a specific architecture (the Transformer) with specific strengths and specific limitations. Understanding the foundation helps you make better decisions about what AI can and cannot do for your work.

The humans in the story matter. From Alan Turing's imagination to Joseph Weizenbaum's warnings to the eight Google researchers who wrote the Transformer paper, AI has always been shaped by the people building it and the choices they make. That has not changed.

The Complete AI Timeline