Schrödinger's AI is your invitation to look inside. Right now, AI feels like a mystery , wired like a brain, yet running on pure math.
Each article is a new layer of the box. We start with the first spark of an idea and move all the way to the models reshaping everything we thought we knew .
Part 2: Fascinating History of AI: From Turing to Today
"The Father of Computer Science and His Vision for AI "
Every big idea starts as a question. For Artificial Intelligence, it was Alan Turing's in 1950:
Can machines think?
At the time, computers were little more than room-sized calculators, yet Turing dared to imagine machines that could learn, and even converse with us, an idea he expressed through his iconic "Turing Test".
At the same time, Warren McCulloch and Walter Pitts created mathematical models of artificial neurons, this planted the first seeds of what we now call neural networks.
In the 1950s, Turing's dream got its name. At a summer workshop at Dartmouth College, a group of scientists, John McCarthy, Marvin Minsky, and others coined the term "Artificial Intelligence".
"Early Experiments in AI "
The early decades were full of playful experiments. In the 1960s, Joseph Weizenbaum's ELIZA program fooled people into thinking they were chatting with a therapist.
At SRI, researchers built Shakey the Robot, a clunky, box-shaped machine that could see its surroundings and move around, primitive by today's standards, but groundbreaking then.
In the 1970s, systems like MYCIN suggested that computers might one day reason through medical problems.
"The Fall and Rise of Early AI "
And then the momentum slowed. By the late '80s, enthusiasm had outpaced progress. The "AI Winter" set in, funding dried up, and many researchers moved on. AI was seen as overhyped, a field that promised too much and delivered too little.
But the dream never really died. In the 1990s, it flickered back into the public eye when IBM's Deep Blue defeated Garry Kasparov, the reigning chess champion. It was a symbolic win, proof that machines could compete with human brilliance in at least one domain.
" The Godfather of AI "
Even during the quiet years of the AI Winter, a few researchers refused to give up. Among them was Geoffrey Hinton, the British-Canadian computer scientist who saw promise in neural networks long dismissed as a dead end.
In 2006, Hinton and his students published groundbreaking work that reignited interest in deep learning, demonstrating that neural networks could finally be trained effectively with the right techniques and data.
That spark would soon catch fire. Six years later, Hinton's team stunned the AI world with AlexNet, the model that cracked open the deep learning revolution.
"The Deep Learning Revolution"
The true turning point, though, came after AlexNet. Faster computers, massive amounts of data, and a revival of neural networks gave AI a new lease on life.
Deep learning quickly became the talk of the tech world. Breakthroughs in image recognition, speech understanding, and natural language processing moved AI from labs into practical applications.
Early GANs began generating realistic images, while autonomous vehicles started navigating roads with the help of AI.
Around the same time, AI entered daily life in smaller but tangible ways, powering Netflix recommendations, vacuuming floors with Roombas, and answering questions through Siri and Alexa. It was a period of rapid expansion, laying the groundwork for the next defining moment: AlphaGo's victory over Lee Sedol.
"AlphaGo vs. Lee Sedol "
2016 was the year that shaped the course of modern AI.
AlphaGo, developed by Google's DeepMind, defeated world champion Lee Sedol in the ancient game of Go. Unlike chess, Go is so complex that intuition often matters more than calculation. Watching a machine pull off moves that baffled even the best human players was a shock. It wasn't that a program had won, it was how it had won.
The documentary: AlphaGo - The Movie, captures this story beautifully; it moved me deeply, not just for the raw power of AI, but for me it was the ride of emotions throughout.
"The Generative AI Revolution"
What followed was an era defined by AI.
In June 2020, OpenAI's GPT-3 stunned the world, demonstrating that a language model could write essays, poems, and even code.
In November 2022, OpenAI launched ChatGPT, well we all know ChatGPT, it was based on GPT-3.5. This tool quickly became a global sensation, reaching over 100 million users within two months, marking one of the fastest-growing consumer applications in history. News anchors writing their script, people are seeking financial advice, students are using it for their assignments.
Around the same time, OpenAI introduced DALL·E 2, an image generation model capable of creating highly realistic images from text. This opened new avenues in creative industries, allowing for the generation of images with greater realism and accuracy.
In March 2023, OpenAI released GPT-4, a multimodal model capable of processing both text and images.
" AI's Nobel Moment"
Decades of dedication resulted in international recognition, when Geoffrey Hinton was awarded the 2024 Nobel Prize in Physics, shared with John Hopfield, honouring their foundational discoveries that made modern machine learning with artificial neural networks possible.
"A Story Still Unfolding"
Today, we live in a world where AI shapes industries, powers personal assistants, creates art, diagnoses diseases, writes, draws, aids doctors, and even drives cars.
I believe the story of AI is far from over; it feels like we're only at the very start. And if history has taught us anything, it's this: the path ahead won't be straight, but the vision of machines that can think alongside us is here to stay.
The cat is neither alive nor dead and honestly, that's the most exciting place to be. There are a lot more layers to uncover.
Previous: Part 1: Layers of Artificial Intelligence
Next: Part 3: The ABCs of Machine Learning
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