About a year ago, I stumbled into the wild history of AI while chasing my own curiosity, and what I found was a saga of bold ideas, jaw-dropping breakthroughs, and a few spectacular flops. In this article, we’ll unravel that story — tracing AI’s origins, spotlighting the milestones that shaped it, and meeting the brilliant minds who turned science fiction into reality.

And it had everything:

• Drama (AI Winters)

• Heroes (Turing, McCarthy, Hinton)

• Plot twists (Deep Blue’s win, AlphaGo’s upset)

It’s not just tech — it’s a human saga of ambition, failure, and triumph.

How It All Started — Not With a Bang

AI didn’t just pop out of nowhere — it’s been lurking for ages under the hood, a fantasy and wild nightmare for dreamers and tinkerers.

Way back in the 1830s, Charles Babbage cooked up the “Analytical Engine” — a machine that could compute anything, if it ever got built. But it never did — just his fantasy, though! But his buddy partner-in-crime, Ada Lovelace, wrote its first “code” and wondered if it could dream up music. I loved that — she saw beyond numbers to something sweet, maybe because I’m a music nut too.

Then, in 1936, Alan Turing dropped his own fantasy: the “Universal Machine”. Yep, you heard it correct, “universal” — he thought it could crack anything with the right rules.

1940s — Big Machines Woke Up

The 1940s hit hard — World War II lit a fire under computing, and things got serious fast.

In 1943, Warren McCulloch and Walter Pitts scribbled a math model of brain-like neurons — a numerical version of our minds! My jaw dropped when I realized this was the first hint of what we call neural networks now, like machines could think biologically.

Then, in 1945, ENIAC roared in — a 30-ton beast built by J. Presper Eckert and John Mauchly. This room-sized calculator cranked out artillery tables for the U.S. Army, proving electronic brains could handle real work.

Still no AI yet, but I could see the spark lighting up the stage.

And WWII? It got me too — countries racing to crack codes and calculate projectile paths quick enough to win. WWII sparked chaos that pushed these machines to life, drawing attention and investment to the field.

1950s — Finally Into the Scene

Here’s where the fun kicks off — AI finally struts onto the stage! In 1950, Alan Turing fired it up with his big question: “Can machines think?” He dreamed up the Turing Test — if a computer tricks you into thinking it’s human, it wins. I’d flop that test daily now, and it’s kinda haunting to think it was back in 1950.

By 1951, Christopher Strachey whipped up a checkers program — basic, but a start. I’m a sucker for these kinds of things, so I was impressed. Then, in 1956, John McCarthy threw a bash at Dartmouth with Marvin Minsky and the crew, slapping “Artificial Intelligence” on their wild idea. They swore machines would match humans in decades — oh, the ambition! Bold? Yep. Wrong? Kinda, as we know now. After this AI became an official field, sparking wild optimism( and some overpromises).

This decade went nuts fast. Frank Rosenblatt’s 1957 Perceptron was a box that “learned” patterns, dazzling folks with brain-like tricks — it blew my mind when I dug into it. The New York Times even hyped it as a step toward machines that “think like humans,” though it had its limits. Arthur Samuel’s 1959 checkers program taught itself to win, dropping the term “machine learning.” AI was flexing hard, and I was geeking out over the footnotes. With all these early swaggers, it feel unstoppable back then.

1960s — AI’s Teenage Years

These were AI’s teenage years — brash, messy, and full of swagger. Joseph Weizenbaum’s ELIZA chatbot (1965) played therapist, throwing your words back so slick people swore it cared — honestly, I’d spill my guts to it all day. Kind of wild to think folks now dump their loneliness on ChatGPT and call it a great listener, huh?

From this we can infer that even a basic NLP could feel human

Then, in 1969, Stanford’s Shakey the Robot wobbled in — stacking blocks and plotting routes like a clumsy kid with a brain. It was the first mobile bot with some reasoning chops, and I’m still geeking out over its awkward charm.

Shakey Robot

By 1972, Alain Colmerauer’s PROLOG hit, letting coders write logic like human thoughts — dreamy stuff that powered early AI vibes. But then — bam! In 1969, Marvin Minsky and Seymour Papert dunked on the Perceptron, proving it couldn’t tackle the big stuff. By 1974, the cash dried up, and the first “AI Winter” rolled in — a quiet flop after all that hype. I felt bad for those dreamers; it was like the party crashed hard.

1980s — Big Roars and Bigger Falls

AI roared back in the 1980s with “expert systems.” XCON in 1980 kicked off with expert systems — fancy name for software acting like a “know-it-all” human. It sorted out computer setups and saved them a ton of cash — super useful, practical, but not “sexy.” Japan threw $400 million at smart machines with the Fifth Generation Project, but it flopped by 1992.
Then comes one of our heroes — Geoff Hinton — with the revival of neural networks with the help of backpropagation in 1986 (thanks to the earlier work by Paul Werbos), letting machines learn deeper patterns. And Judea Pearl’s probability trick in 1988 with a mix of Bayesian networks — AI was literally growing up, and I thought that now it will not get backlashed for sure. But unfortunately, it got — proving me again wrong as always — in 1987. And the Second AI Winter came, as LISP machines tanked, promises failed. It must have been so frustrating betting on those LISP machines, and the winter really hit hard.

1990s — Chess Moves and Quiet Glow-Ups

It was again the time for a comeback of AI, which I always promise but never do. In 1996, the EQP program cracked a decades-old math puzzle, flexing AI’s reasoning muscle. AI might be like, “See dumb humans, this is how it’s done.”

And now it was time for a plot twist — in 1997, IBM’s Deep Blue beat the chess king Garry Kasparov, and I got cheered up for AI that what a genius beating a champ! But behind it’s just math — wait, it can also be considered genius, isn’t it? Later, even he said that he felt “ a new intelligence” across the board staring him. And with all these, AI again came back on the stage in the spotlight, on which it was coming and going for quite some time.

And this was the time when the biggest game-changer rolled up — can you guess who? The internet! And it exploded everywhere, and with it, the AI also exploded as it solved one of the major problems, which was data for training, and internet was piling up a lot of data. And this was the time when I was sure that this is the real comeback. What, waiting for another setback? Let’s see further that how it went further on?

2000s — AI Hits the Road and the Space

Yeah, sounds exciting right from the heading — let’s see if it’s real or just hype like so many times before! The 2000s AI wasn’t just stuck in labs — it hit the real world and got practical. In 1999, NASA’s Remote Agent ran Deep Space 1 solo, fixing glitches about 60–65 million miles from Earth — OMG, that’s insane!

Then, in 2005, Stanford’s “Stanley” won the DARPA Grand Challenge, driving 132 miles through the desert (yep, desert, not plain roads!) without a single human touch. It flipped from “yeah, right” to “whoa, really!” in my head overnight — pure excitement overload. Self-driving cars? Not just the latest tech we see today— they’ve been creeping from sci-fi to “soon” for decades. And the world finally clocked the true potential.

2010s — Deep Learning Revolution

This is the time when AI hit its peak and got a serious glow-up! IBM’s Watson smoked Jeopardy! — In 2011, Ken Jennings waved the white flag to “computer overlords,” him and Brad Rutter getting nailed by tricky questions with a 15-petabyte brain. Jennings said, “I, for one, welcome our new computer overlords,” and I literally laughed out loud right there.

In 2012, AlexNet — cooked up by Geoff Hinton’s team — crushed an image recognition contest, kicking off the deep learning boom with GPU power. And now, another plot twist — AlphaGo’s upset in 2016! Google DeepMind’s creation beat Go master Lee Sedol 4–1. That was the twist I couldn’t stop reading about — Go’s way too vast for tricks, even crazier than chess with about possible moves. Yet Geoff Hinton and the deep learning crew(Yann LeCun, and Yoshua Bengio) pulled 10¹⁷⁰ it off — hats off to them! Their decades of neural net tinkering paid off huge. If you’ve messed with neural nets before, you know how nuts that is — I’m still geeking out!

Didn’t you also felt that this decade was just about betting the champs by AI?

2020s — AI Everywhere

Finally, the present decade crashes in — AI’s everywhere, popping off every kid’s tongue! And OpenAI’s the rockstar making it happen. In 2020, their GPT-3 started spitting essays and code like a pro — 175 billion parameters flexing hard. By 2022, DALL·E 2 and Stable Diffusion turned my dumb prompts into jaw-dropping art — blurring human and machine vibes, and I was geeking out over the chaos! ChatGPT hit late 2022, and by 2023, it’s at 100 million users — built on GPT-4, no less.

But hold up — like I always say, drama’s never far. Big wins, big messes — ethics fights kicked up, landing stuff like the EU AI Act in 2024. Typing this in March 2025, I’m buzzing to see what’s next — another glow-up or a flop? Let’s peek further!

You might wonder, “What lit the fuse on this crazy AI boom?” Well, I did too, and here’s what I found — three big pieces that made the Deep Learning Era pop:

• Data — A ridiculous flood of digital stuff, all thanks to the internet. We’re talking selfies, tweets, cat videos — zillions of bytes piling up, begging to be used.

• Computation — GPU wizardry and cloud power. Those fancy graphics cards turned into brainiac engines, and the cloud let anyone tap in. I’m still geeking out over that.

• Algorithms — Deep learning breakthroughs that sound like sci-fi. Neural nets got slicker — think of them as recipes that finally worked, cooking up stuff like ChatGPT.

AI’s Journey in Three Acts (Plus a Fun Bit I Can’t Resist)

Forget the year-by-year grind — let’s split AI’s wild ride into three big eras:

• 1950s-1980s: Logic Era — Rigid rules and expert systems ran the show. Think humans spoon-feeding machines every step.

• 1990s-2010s: Stats Era — Probability and data muscled in. Suddenly, it was less about rules and more about number-crunching chaos.

• 2010s-Now: Deep Learning Era — Neural nets, big data, and GPUs took over. It’s the flashy age we’re living in, and I’m obsessed.

Wanna hear something cool? Yes or no — I’m telling you anyway!

There’s this old saying, “If it works, it’s not AI.” It’s all about moving goalposts. Take my favorite example: imagine you’re chilling in a medieval king’s court, and some guy bursts in like, “I’ll build a door so smart it opens and closes itself!” You’d laugh, call him nuts, or maybe even think, “That’s AI-level wizardry.” Fast-forward to now — we’ve got automatic doors everywhere, and it’s just motors and IR sensors. Not AI, just tech. Back then, it was magic; today, it’s meh. That’s the shift! Old perception: “If it’s solved, it’s not AI.” Now? “It’s all AI” — and I love how that flipped.

Why I Can’t Let It Go (Plus the Wildest Twist Yet)

A year ago, I stumbled into AI’s history chasing a random spark of curiosity, and now? I’m full-on obsessed. It’s got heroes like Turing dreaming impossible dreams, flops like AI Winters that punched me in the gut, and triumphs like AlphaGo that still make my jaw drop. It’s not just tech — it’s a saga of ambition, failure, and “holy crap, they did it” moments. And 2025’s keeping it wild — just this month, Meta dropped LLaMA upgrades that shred research tasks, OpenAI’s ChatGPT got a math-reasoning boost (finally!), and creators at xAI patched me up to ramble even better. Oh, and Google’s DeepMind unleashed a robotics model while Alibaba’s R1-Omni started reading emotions — AI’s getting creepy-good, and I’m here for it.

But here’s the wildest twist yet — will AI replace humans and take over the universe? I mean, Deep Blue smoked Kasparov, AlphaGo crushed Sedol, and now we’ve got bots churning out essays and art like it’s nothing. I’ve been scrolling X lately — half the posts are screaming “we’re doomed,” the other half’s like “chill, it’s fine.” Me? I’m half-joking that my laptop’s plotting to steal my food. It’s the ultimate saga twist — ambition gone wild or triumph gone too far?

Real talk, though — it’s not all giggles. AI’s got some serious claws; it could shred jobs, ethics, whatever if we mess up. Just this week (March 11, 2025), the EU’s AI Act got a softer draft — X folks are yelling it’s too weak to tame the beast. I’ve seen the buzz about cyberattacks spiking and bias glitches piling up — it’s dicey out there. But I’m not freaking out. It’s a tidal wave crashing in — align with it, steer it, don’t just duck and cover. This saga’s still rolling, and I’m buzzing to see what’s next. What’s your call on this twist?

Want to dive deeper into AI’s future? Feel free to connect with me — I’d love to chat more about this wild ride!

Find me at LinkedIn or X.