I have spent my professional life working in artificial intelligence. Not the science fiction kind with robots plotting to conquer humanity but the very real systems that help large companies automate, make decisions, and sometimes even sound thoughtful. Over the years, I have watched this space grow from simple, almost clumsy attempts at automation to today’s wave of systems that can write stories, draft emails, and occasionally surprise us by seeming more creative than we are.

Because I work in this space, I have seen the shifts up close. I have seen how the expectations changed, how companies moved from hoping a machine could just answer basic questions to now wanting it to handle complex conversations with empathy and subtlety. From the inside, the changes look less like sudden leaps and more like steady building. And if you trace the roots of today’s generative AI boom back to where it really began, you arrive at a surprisingly quiet moment in 2017 when a small group of researchers at Google published a paper titled with almost casual certainty:

It turns out they were exactly right.

What is AI anyway

Before we get to that point, it helps to slow down and look at what we even mean by AI and machine learning. These words get thrown around so often they start to lose meaning.

Artificial intelligence, in its broadest sense, is just the idea of getting machines to perform tasks we consider smart. This could be recognizing faces in photos, recommending a new show to watch, or driving a car.

Machine learning is a subset of AI. Instead of programming the machine step by step with explicit rules, you feed it examples and let it figure out the patterns on its own.

Think of teaching a child the difference between a cat and a dog. You could list out rules like cats usually purr and dogs usually bark, but that is brittle and easily fails. A better way is to show the child hundreds of pictures labeled cat or dog and let them start to see the shapes, ears, tails, and patterns for themselves. That is what machine learning does. It finds patterns in data so it can make predictions or decisions without being explicitly programmed on every possible rule.

The Rise of Neural Networks

When people say neural networks, they mean systems loosely inspired by how our own brains work. Lots of tiny interconnected units called neurons pass signals to each other, strengthening or weakening their connections based on experience.

Early attempts at neural networks go back to the 1950s and 60s, but computers back then were too slow and data too scarce for anything meaningful. By the 1980s and 90s, as computers improved, researchers revived the idea and developed algorithms like backpropagation - a method where the network compares its output to the expected result, sees how wrong it was, and tweaks its internal settings to get closer next time.

This ability to learn from mistakes by adjusting weights layer by layer made it possible to build deeper networks. By the early 2010s, with huge amounts of data and powerful graphics processors to do the heavy math, neural networks started to shine in things like image recognition and speech.

The awkward first tries at language

Handling language turned out to be harder. Words rely heavily on context. The word "bank" could mean the side of a river or the place your salary goes into. Early models tried to process text sequentially using what were called Recurrent Neural Networks or RNNs. These systems, popular from the early 1990s through the 2010s, worked by taking in one word at a time and carrying a sort of memory forward to the next step. If you feed in "The cat sat on the", and it uses its memory to predict "mat".

The trouble was this memory faded quickly. Long sentences would confuse it, because by the time it reached the end it might forget important details from the start. It was like someone trying to recite a long story after only hearing it once, stumbling over parts and filling gaps with guesses.

To fix this, researchers developed Long Short Term Memory networks or LSTMs in 1997. These improved on RNNs by giving the network clever gates to decide what to keep and what to forget, letting it remember useful things for longer. For about a decade, LSTMs became the standard tool for dealing with sequences like text.

But they were still sequential: they read sentences word by word, like a kid sounding out syllables in a new book. That made them slow and often left them confused by long, twisty sentences.

The Turning Point: Attention

Then came that 2017 paper.

A team of researchers at Google published a paper called - Attention is All You Need.
Instead of clinging to the old step-by-step sequence style, they asked: why not let the machine look at the whole sentence at once, then figure out which parts should pay attention to which?

If that sounds simple, it is, at least in spirit.
Think of how we read. If you see:

"The chef who won last year’s contest is opening a new restaurant."

You instantly know "chef" is linked to "opening", even though other words sit in between. Your mind doesn’t need to go word by word, carrying a fragile memory. It just grasps the relationships. These researchers built a mathematical framework to let machines do something similar.

They called this mechanism attention, and the architecture that used it was named the Transformer.

Transformers didn’t just improve results slightly. They blew past previous models in both speed and quality. Because they could process all words in parallel, they trained faster. Because they explicitly modelled relationships across all positions in the input, they handled long sentences without forgetting the beginning.

The 2017 paper primarily targeted machine translation like English to German, but it laid down a blueprint that would soon be used for everything.

The flood that followed: BERT, GPT, Gemini and beyond

Once the transformer design was public, it became the backbone of nearly every major advance in natural language work.

In 2018, Google introduced BERT, short for Bidirectional Encoder Representations from Transformers. Unlike earlier models that mostly read left to right, BERT looked both ways in a sentence at once. This made it great at understanding search queries and other tasks where context from both directions matters.

Also in 2018, OpenAI released GPT-1, the first Generative Pretrained Transformer, trained to predict the next word based on everything it had seen before. It had around 117 million parameters, which simply means internal settings the model adjusts as it learns patterns from data.

In 2019, OpenAI introduced GPT-2, much larger with 1.5 billion parameters. This model drew attention because it could write surprisingly fluent paragraphs on almost any topic.

By 2020, Google rolled out T5, Facebook and Microsoft launched their own transformer variants, and the entire field was racing forward. In 2020 and 2021, GPT-3 arrived with 175 billion parameters, able to write essays, generate poetry, and even dabble in code. This was the version that powered the first ChatGPT demos that caught public fascination.

In 2023, OpenAI followed up with GPT-4, even more nuanced, handling multiple languages and working alongside image models. Meanwhile Google launched Bard, which evolved and was rebranded in 2024 as Gemini. Around the same time, Meta put out LLaMA and Anthropic launched Claude. Each was a different flavor but all were built on the same underlying transformer idea.

What makes this "Generative" AI

When people talk about generative AI, they mean systems that do not just sort or label data but actually create new things that resemble what they learned from. A generative text model reads massive amounts of language so it can produce new paragraphs that sound like a person wrote them. A generative image model, like DALL·E or Midjourney, studies millions of pictures and then draws scenes that never existed. A music model learns countless melodies and invents fresh tunes.

It still comes down to predicting the next piece - the next word in a sentence, the next pixel in an image - but because transformers let these models look at everything at once and decide what truly matters, the results are far richer and more natural than anything older systems managed.

That is why you can type in something like write a thank you note for a colleague’s help on a tough project or create a picture of your neighborhood with cherry blossoms in full bloom, and get a result that almost feels intentional. It does not really understand, it simply pays attention across countless examples, builds dense patterns, and guesses what fits best. And somehow, that is enough to seem remarkably human.

Attention Really is All You Need

Working in this space, I have watched these shifts from close quarters. In the early 2010s, companies were thrilled if a chatbot could simply confirm a booking without confusion. By 2016, they wanted virtual agents to handle a few back-and-forth questions. Today they expect systems to draft contracts, generate marketing campaigns, and even console upset customers with something close to empathy.

All of that traces back to that quiet moment in 2017 when a few researchers decided to abandon the slow memory chains and instead let machines look everywhere and pay attention to what matters. That one shift unlocked a flood of creative applications that now touch everything from healthcare to entertainment.

At the center of it all is a simple change. Machines stopped trudging word by word, hoping not to forget what came before. They learned to pay attention to everything at once and highlight what truly mattered.

Turns out, for machines trying to sound human, just like for humans trying to make sense of the world:

Attention really is all you need.