You’ve probably heard people talk about Artificial Intelligence like it’s this shiny new thing that just popped out of Silicon Valley a couple of years ago. Maybe you think it started with ChatGPT. Honestly? Not even close. If you’re asking when did AI start, you have to look back much further than the 2020s. We’re talking decades of failed experiments, weird math, and a bunch of pipe dreams that started in the 1950s.
It’s easy to get caught up in the hype of generative bots, but the foundation was laid by people who didn't even have a computer as powerful as your toaster. They were working with punch cards and room-sized machines.
The 1956 Dartmouth Workshop: The Official Birth
Most historians and computer scientists point to a single event: The Dartmouth Summer Research Project on Artificial Intelligence in 1956. This is basically the "Big Bang" moment. John McCarthy, a math professor who later went to Stanford, actually coined the term "Artificial Intelligence" specifically for this workshop.
He didn't do it alone. He brought together guys like Marvin Minsky, Claude Shannon, and Nathaniel Rochester. They spent about eight weeks in New Hampshire just... thinking. They were incredibly optimistic. They genuinely believed that if they put a few smart people in a room for a summer, they could solve things like language processing and "self-improvement" in machines. They were wrong about the timeline—dead wrong—but they gave the field its name and its first real heartbeat.
Before the Name: Alan Turing and the 1940s
But wait. If you want to be a stickler for details, the conceptual work started even earlier. You can't talk about when did AI start without mentioning Alan Turing. In 1950, he published a paper called Computing Machinery and Intelligence.
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This is where the famous "Imitation Game" (now known as the Turing Test) came from. Turing wasn't interested in whether a machine could "think" in a biological sense. He thought that was a silly question. Instead, he asked: "Can a machine imitate a human well enough to fool us?" That shifted the goalposts from philosophy to engineering. It gave researchers a target to hit, even if that target is still being debated today.
The Era of "Old School" AI
The 60s were wild for AI. People were convinced that we’d have robot maids and universal translators by 1980. There was this program called ELIZA, created by Joseph Weizenbaum at MIT between 1964 and 1966. ELIZA was basically the great-grandmother of Siri.
It worked by "pattern matching." If you told ELIZA, "I'm feeling sad," it might look for the word "sad" and reply, "Why are you feeling sad?" It didn't understand a lick of what you were saying. It was just a script. But people loved it. They would pour their hearts out to this program, even knowing it was just code. This revealed something huge about human psychology that still affects AI design today: we really want to believe there's a "someone" inside the machine.
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The First AI Winter: When the Hype Died
Everything crashed. Hard. By the mid-70s, the government and big investors realized that the 1956 Dartmouth dreams weren't coming true. Computers were too slow. Memory was too expensive.
This period is known as an "AI Winter." Funding dried up. If you were a scientist, you stopped using the words "Artificial Intelligence" on your grant applications because it was seen as a joke. You called it "Informatics" or "Machine Learning" just to keep the lights on. It’s a bit of a reality check for us now. We think progress is a straight line up, but history shows it’s more like a series of mountain peaks and deep, dark valleys.
Expert Systems and the 1980s Comeback
In the 80s, AI got a second wind through something called "Expert Systems." Companies like Digital Equipment Corp started using programs that mimicked the decision-making of a human expert.
If you had a specific set of rules (If X happens, then do Y), the computer was great at it. It wasn't "smart" in a general sense, but it was useful for things like diagnosing medical issues or configuring computer parts. It was the first time AI actually started making money for businesses. But, like a bad sequel, this led to another AI winter in the late 80s when these systems proved too brittle and expensive to maintain.
The Big Shift: Big Data and Neural Networks
So, how did we get from those clunky systems to the AI we use today? The pivot happened when we stopped trying to program every single rule into the computer.
Instead of telling a computer what a "cat" looks like (pointy ears, whiskers, tail), we started showing it millions of pictures of cats and letting the machine figure out the patterns. This is "Neural Networks," a concept that actually dates back to the 40s but didn't work until we had two things:
- Massive amounts of data (thanks, Internet).
- Insane processing power (thanks, GPUs).
When IBM's Deep Blue beat Garry Kasparov at chess in 1997, it was a huge PR win, but it was still mostly "brute force" calculation. The real revolution was "Deep Learning" in the 2010s. That’s when things like Google Translate and facial recognition suddenly started working well enough to be useful.
Common Misconceptions About AI's Origins
Most people think AI started as a branch of computer science. Actually, it started as a mix of philosophy, mathematics, and even biology. Early researchers were trying to model the human brain's neurons.
Another big myth? That AI has always been about "thinking." For a long time, it was just about "doing." Logic-based AI (Symbolic AI) dominated for decades. It was only recently that the "probabilistic" approach—predicting the next word or pixel—took over.
Why the Start Date Actually Matters
Knowing when did AI start isn't just for trivia night. It helps us understand that the "intelligence" we see today is the result of nearly 80 years of trial and error. We are currently in a "Summer" period, but history suggests that if the current tech doesn't solve the massive energy and cost problems it faces, another "Winter" could be lurking around the corner.
Actionable Steps for Navigating AI History and Future
- Look past the chat box. If you want to understand where AI is going, study "Symbolic AI" vs. "Connectionism." Most of today’s AI is the latter, but the future likely involves a hybrid of both.
- Check your sources. When reading about AI history, look for mentions of the Lighthill Report (1973). It’s the document that basically killed AI funding for a decade and explains why we fell behind for so long.
- Focus on data quality. History shows that the "logic" of AI is only as good as the information fed into it. Whether you're a business owner or a student, the "Garbage In, Garbage Out" rule from the 60s still applies.
- Diversify your tools. Don't rely on just one LLM. Different models use different training methodologies that trace back to different schools of thought from the 90s and 2000s.
The story of AI is less about machines becoming "alive" and more about humans getting better at math and data organization. It’s been a long, weird journey from a summer camp in New Hampshire to the phone in your pocket.