Okay, let's unpack this. I want you to imagine
a single document. It's just 10 pages long. Right.
It was uploaded to a server back in 2017, probably
by some guy in a t -shirt, you know, drinking
stale coffee. And yet that 10 -page PDF is effectively
the constitution, the blueprint, and maybe even
the big bang of the entire modern AI era all
rolled into one. It's not an exaggeration at
all. If you look at the landscape of technology
today, I mean... Here in 2026, almost everything
important AI stems from this one moment. It's
a dividing line. It is. There is before this
paper and there is after this paper, period.
We are, of course, talking about the research
paper titled Attention is All You Need by a Team
at Google. And I was looking at the stats you
pulled for this deep dive. This is actually insane.
It's mind boggling. As of 2025, this paper sits
in the top 10 most cited papers of the entire
21st century. Right. Over 173 ,000 citations.
And, you know, to put that in perspective, a
truly groundbreaking paper in, say, biology or
physics is lucky to get a few hundred citations.
A few hundred. This didn't just nudge the field.
It completely... reinvented the physics of how
computers process information. And that's exactly
why we're here today. Because everyone knows
the brand names. We all know ChatGPT, Gemini,
Sora. We use them. We talk to them. We let them
write our emails. Sure. They're household names.
But almost no one understands the engine underneath
the hood. Today, we are going to deconstruct
the transformer. Which is the architecture that
was introduced in this paper. Exactly. Our mission
is to move past the buzzwords. We're going to
look at the history, the technical details, to
really understand mechanically why this specific
architecture changed everything. And we should
probably say we're going to get into the weeds
a little bit. We're going to talk about vectors
and matrices. But we promise to keep it English.
We'll keep it grounded. But you can't really
understand the revolution without understanding
the machine itself. So to appreciate the aha
moment, we have to understand the before times.
Take us back to, say, pre -2017. If I wanted
a computer to translate a sentence from English
to German, how did it do it? Because we had Google
Translate before 2017. We did, but it was clunky.
The dominant technology at the time was something
called recurrent neural networks, or RNNs. Okay.
And specifically, a more advanced version called
LSTMS, long short -term memory networks. Long
short -term memory. That sounds like a contradiction.
It was a brilliant solution for its time, designed
to solve a very specific problem, memory. But
here was the massive constraint. It was strictly
sequential. Sequential, so one after the other.
Exactly. Imagine you're trying to read a book,
but you can't look at the whole page. Instead,
you have to look at the text through a tiny slit
in a piece of paper that only reveals one word
at a time. Okay, I'm picturing that, like reading
a scroll. You read the first word, you process
it, you try to remember it, then you slide the
slit to the second word. You process that, add
it to your memory of the first word, then the
third. You're forced to move left to right, step
by step by step. So if I'm the computer, I literally
cannot look at word number five until I have
finished processing word number four. Precisely.
And that created two massive problems that were
basically strangling the entire field of AI.
Okay. What was the first? Spade. You couldn't
parallelize the work? Hold on, let's dig into
that. Parallelize is a big word. Why couldn't
I just buy a thousand computers and have them
all read the sentence at the same time? Because
of the dependency. Think of it like a relay race.
Runner B cannot start running until Runner A
physically hands them the baton. It doesn't matter
if you hire Usain Bolt for the second leg. He
just has to stand there and wait. So if I have
a really long sentence or a whole book, the computer's
just chugging along one word at a time. Exactly.
Modern GPUs, graphics processing units, are designed
to do thousands of things at once. But RNNs forced
them to do one thing at a time. It was incredibly
inefficient. And the second problem. The second
problem was even worse. It's a concept called
the vanishing gradient. The vanishing gradient?
That sounds like a horror movie title. It's the
horror movie of linguistics, basically. It just
means information loss. Since you're processing
strictly in order, by the time the model gets
to the end of a long paragraph, it tends to forget
what happened at the beginning. Kind of like
a game of telephone. A very good analogy. The
model tries to cram the context of the first
word into this little package of numbers, a state
vector. Pass it to the second word, modify it,
pass it to the third. And by the end? By the
time you're 50 words in, the signal from word
number one is barely a whisper. So if I have
a sentence like... The girl who lived in the
blue house down the street and liked to eat apples
went to the store. By the time the computer went
to the store, it might have forgotten who the
girl was. Exactly. It knows someone went to the
store, but that connection, that thread back
to the subject, is weak. The context gets totally
diluted in the bottleneck. The world needed a
way to process the whole sentence at once. And
that brings us to the heroes of her story. The
team behind attention is all you need. Team Transformer,
as they apparently call themselves. It was eight
scientists at Google, Ashish Vaswani, Noam Chazir,
Niki Parmar, Jacob Uskarite, Leon Jones, Aidan
Gomez, Ucas Kaiser, and Ilya Polosukhin. And
here's a fun fact I found in the notes. Oh, yeah.
They contributed so equally to this thing that
the order of their names on the paper was randomized.
Which is incredibly rare in academia. You know,
usually the first name is the star and the last
name is the boss. But here it really was a collective
mind melt. They were just jamming on ideas. And
looking at where they are now in 2026, the band
has definitely broken up. Oh, completely. Every
single one of them left Google. They all went
on to found their own huge startups like Cohere,
Character .ai, Near, or join other major players.
It's basically the PayPal mafia, but for this
generation of AI. But before they split up, they
had to name this thing. And for a paper that
changed history, the naming process was surprisingly
casual. Transformer. Yeah, Jacob Ooskerite just
liked the sound of it. He thought it sounded
powerful. In fact, Our source material says that
some of the early design documents actually featured
characters from the Transformers toy franchise.
No way. Yes. Optimus Prime and Megatron were
apparently in the margins. And the title of the
paper itself, Attention is All You Need. A nod
to the Beatles. All you need is love. I love
that. It adds a bit of personality to what is
some very dense math. But the title was also
a huge controversial claim. Attention is all
you need. They're saying, hey, all that recurrent
stuff, all those LSTMs we've been using for a
decade. I'll throw them in the trash. Yeah. It's
a radical hypothesis. Yeah. Jacob Ooskerite suspected
they can just ditch recurrence entirely. And
our source notes that even his father, who's
a famous computational linguist, was skeptical.
He basically told his son, you can't just ignore
the order of words. Language is order. But they
did. So let's get into the machine. This is the
part everyone struggles with. How does the transformer
work if it doesn't read one word at a time? It
uses a mechanism called self -attention. So instead
of that slit -in -the -paper approach, imagine
looking at the entire page instantly. The model
looks at every single word in the sequence at
the same time, and it calculates how much attention
each word should pay to every other word. Okay,
let's unpack that with an example. Let's use
the sentence. The animal didn't cross the street
because it was too tired. Great sentence. Okay,
when a human reads that, we get to the word it
because it was too tired, and we instantly know
that it. refers to the animal. Right, because
streets don't get tired. Exactly. We use common
sense and context, but a computer doesn't have
common sense. It just has numbers. So self -attention
allows the model to connect the word it really
strongly to the word animal and very weakly to
the word street. It builds a web of relationships.
Okay, but how? The paper talks about this query
key and value system, the QKV. This is usually
where people's eyes glaze over. I want you to
explain this so my grandmother would get it.
Challenge accepted. The best analogy is a library
retrieval system. Okay. I'm in a library. Imagine
every word in that sentence is a person in a
library. Let's take the word it. It is trying
to figure out what it means. So it. creates a
query. Think of the query like a sticky note
on its forehead that says, I am a pronoun looking
for a noun that can get tired. Okay, so the query
is the intent. What am I looking for? Exactly.
Now, every other word in the sentence, animal,
street, cross, is standing there holding a book.
And on the spine of that book is a label. That
label is the key. So the key is like the description
of what that word offers. Yes. The word street
has a key that says, I am a rigid paved surface.
The word animal has a key that says, I am a biological
entity that experiences fatigue. I see where
this is going. My query looking for something
that gets tired scans all the keys. And it finds
a match. The query for it meshes perfectly with
the key for animal. That mathematical match,
it's called the dot product, creates a really
high score. It's a high compatibility rating.
It clashes with street, so that gets a low score.
Okay, so I found my match. I know it belongs
to animal. What happens then? Once you find the
match, you take the book off the shelf and open
it. The information inside, the actual meaning,
the essence of that word is the value. You then
absorb that value. So now the representation
of the word it is updated with the value from
the word animal. That is actually really helpful.
So instead of just next word, next word, every
word is constantly querying every other word
to see, hey, are we related? Do you have context
I need? Precisely. And it happens all at once.
Animal is querying tired. Cross is querying street.
It's like a massive simultaneous cocktail party
where everyone is talking to the people most
relevant to them. Now, in the paper, there's
a scary looking formula. The attention QKV equals
a bunch of math. Right. We won't read the calculus,
but the concept is just what we described. You
multiply the query in the key to get a raw score.
Then you use a function called softmax. And think
of softmax as like the referee. It looks at all
the raw scores and turns them into clean percentages.
So it's like you are 90 % related to animal,
5 % to street. Exactly. And 5 % to cross. Simple
enough. But then they add another layer, multi
-head attention. Because apparently paying attention
once isn't enough. Right. Think of it as having
multiple pairs of glasses or maybe multiple agents
looking at the same sentence. If you only have
one head, you might only focus on who is doing
what action. But language is more complex than
that. Exactly. So the transformer uses multiple
heads. One head might be tracking grammar -like
subject -verb agreement. Another head might be
tracking gender -connecting king to he. Another
might be tracking tone or style. So it's like
a committee. One person checks the grammar. Another
person checks the definitions. A third checks
the context. And they all report back at the
same time. This creates a much richer, much more
high -definition understanding of the sentence
than an RNN ever could. Okay, I'm with you on
the mechanics, but here's the part that still
bothers me. You said the model reads the whole
sentence at once, parallel processing. Yes. But
order matters. The dog bites the man is a news
story. The man bites the dog is a viral video.
They're totally different. If I throw all those
words into a bag at the same time, how does the
model know who bit who? This was the biggest
criticism Jacob Ouskarait's father had. You've
lost the order. And since they ditched recurrence,
they needed a way to artificially stamp the order
back onto the words. This is the concept of positional
encoding. And the source says they used sine
and cosine wave functions. Wait, hold on. Sine
waves. Like from an oscilloscope. Why would a
language model need a wave function? That sounds
like physics, not grammar. It does sound weird.
But just think about a wave. It repeats, but
it changes its values slightly at every single
step. Imagine you have a color gradient that
shifts very, very slowly from, say, red to blue
across the sentence. The first word is pure red.
The last word is pure blue. And every word in
between has a slightly different shade of purple.
So the word carries its meaning, but it also
carries this little color that tells the model
where it sits in the line. Exactly that. The
sine and cosine waves are just a mathematical
way to generate those unique colors or fingerprints
for every position. So the model looks at the
word man. It sees the meaning adult male, but
it also sees a mathematical stamp that says position
three. So even though it processes everything
at once. That stamp preserves the sequence. That
feels like a hack, a brilliant hack, but a hack.
In a way, it is. But it was an incredibly elegant
solution that didn't require sequential processing.
It gave us the best of both worlds, the speed
of parallel with a structure of order. So we
have self -attention, the cocktail party. We
have multi -head attention, the committee. and
positional encoding, the timestamps. That's the
engine. Now let's talk performance, because the
whole point of this was speed. And this is where
the numbers just get staggering. The source material
notes that the base models were trained on just
eight NVIDIA P100 GPUs. Only eight. In today's
world, we talk about clusters of 100 ,000 GPUs.
Eight sounds like a gaming PC setup. It was so
efficient. The base model took only 12 hours
to train. The big model took 3 .5 days. Okay,
compare that to the before times. How long did
the old stuff take? The source mentions that
the previous Google neural machine translation
system took nine months to develop. And the statistical
approach is before that, 10 years of engineering.
So we went from years to months to three and
a half days. That is the power of parallelization.
because you aren't waiting for the scroll to
unwind anymore. You can just throw more compute
at it and it actually works. You can scale it.
Exactly. And originally, they just wanted to
translate languages, right? The paper focuses
on English to German. They didn't know they were
building the foundation of HEI. That's the beautiful
irony. They were just trying to get a better
translation score. But they saw these early signs
that this wasn't just a translator. The team
decided to throw a curveball at the model. They
tried it on something called English constituency
parsing. Which is what? It's rigorous grammar
analysis. Identifying noun phrases, verb phrases,
the whole sentence tree structure. A totally
different task from translation. And they didn't
even tune the model for it. Nope. They just ran
it. It worked brilliantly. And didn't they have
it write a Wikipedia article? They did. As a
test, they had it generate a fake Wikipedia article
about Transformers. And it wrote this coherent,
factual sounding article. Very meta. It was a
proof of concept. They proved this architecture
could generate coherent text, not just swap words
around. They'd built a general purpose language
engine. Which brings us to the aftermath. The
paper was published in 2017. What happened next
is basically the history of the last decade.
It just triggered the AI boom. Almost immediately
you saw the evolution. In 2018, we got BERT from
Google, which totally revelationized Google search.
And then the generative pre -trained transformers.
The GPT series, yeah. OpenAI looked at this architecture
and basically said, what if we just make it bigger?
Much, much bigger. The source notes that the
sheer scale of the 2022 boom with ChatGPT was
unexpected, even to the researchers. Well, the
architecture allowed for what we call quadratic
scaling. Because it was parallel, you could just
stack more layers, feed it the entire internet,
and it just kept getting smarter. It didn't hit
a ceiling like RNNs did. But here is where it
gets really interesting for me. I want to connect
this with a listener who thinks, okay, cool text
generator. It's not just text anymore. We call
it a language model. But the transformer doesn't
seem to care what the language is. This is the
multimodal shift. And this is key. See, the transformer
treats everything as a token. A word is a token.
But a patch of pixels in an image. That can also
be a token. Explain that. How is a picture a
language? Think about the query key concept again.
If I have a picture of a beach, I have a patch
of blue pixels at the top. That patch is a token.
It queries the pixels around it. It asks, hey,
am I part of a blueberry or am I part of the
sky? And the surrounding pixels reply, well,
we are white and flicky clouds and below us is
an ocean. So the blue pixel says, OK, I am sky.
It uses the exact same attention mechanism to
understand the image. So that's why we have vision
transformers, and that's why we have Sora creating
video. Exactly. Sora treats chunks of video as
tokens. It predicts the next frame token, just
like ChatGPT predicts the next word token. And
even biology. You mentioned AlphaFold. AlphaFold
changed the world of biology by predicting protein
structures. It treats amino acids like they're
words in a sentence. It uses self -attention
to see how the protein folds up on itself. This
amino acid attracts that one over there. It's
just... Finding relationships in data. It's incredible
to think that a mechanism designed to translate
the cat sat on the mat into German ended up solving
protein folding and creating Hollywood -level
video. It really speaks to the universality of
the architecture. Attention. The ability to weigh
relationships between data points, no matter
how far apart they are, turns out to be a fundamental
law of information processing. So what does this
all mean for us? For the person listening who
isn't an AI researcher. It means we've moved
from a world of rigid rule -based computing to
a world of relationship -based computing. Unpack
that a bit. Before transformers, computers were
just very bad at context. They were literal.
They were linear. You made a typo. They crashed.
Now we have machines that can see the whole page
at once. They can understand that a word or a
pixel or a gene. only has meaning in relation
to the things around it. It's a connector. It's
a universal connector. The transformer gave us
a method to map the relationships between any
set of data points. And that is why it is the
engine of the 21st century. It really is the
constitution of the AI era. It defined the rules
of engagement for how machines learn. And we're
still just seeing the early applications of that.
Remember, the paper foresaw a question answering,
but I don't think even the authors realized how
quickly it would scale to things like robotics
and reasoning. It's a good reminder that sometimes
the biggest revolutions come from a 10 -page
PDF. And a group of people willing to challenge
the conventional wisdom. Remember, everyone was
saying, you need recurrence, you have to read
an order, and they just said, no, attention is
all you need. I want to leave the listener with
a final thought, something to chew on. We've
talked about how the transformer moved us from
sequential processing thinking linearly to parallel
processing, seeing the whole picture at once.
It makes me wonder about our own brains. We tend
to think linearly. We tell stories beginning
to end. We read left to right. But the transformer
proved that attention without strict order is
faster and more powerful for processing data.
That is a deep thought. It implies that linearity
might actually be a bottleneck in our own cognition.
Exactly. If attention was all we needed to solve
language, what other simple mechanism are we
overlooking? Is there a creativity is all you
need or a reasoning is all you need? Paper just
waiting to be written. What's the simple mechanic
that unlocks that next level of intelligence?
If we find it, it'll probably be another 10 -page
paper that nobody notices at first. And we'll
be here to deep dive into it when it drops. Thanks
for listening, everyone. Keep learning.