Right now, whether you are using chat GPT to
write an email, or relying on Google Translate
to read a menu in Tokyo, or even watching an
AI generate a hyper realistic video from a text
prompt, you are interacting with a hidden, wildly
powerful engine. Yeah, absolutely. It's called
the transformer. So welcome to the deep dive.
Thanks for having me. Today, our mission is to
really look under the hood of this architecture.
We are pulling from an incredibly comprehensive,
frankly kind of dense Wikipedia article. Oh,
very dense. Yeah, detailing the history and the
mechanics of the transformer. Our goal today
is to skip the overwhelming jargon. bypass the
hype and give you a real shortcut to understanding
the core technology driving this massive artificial
intelligence boom we're living through. Because
understanding how these tools actually function,
like the literal mechanics underneath the interface,
it gives you a massive edge in a world that is
just suffering from severe information overload
right now. Oh, 100%. Once you grasp the underlying
logic of this specific architecture, I mean,
an entire decade of rapid, seemingly magical
technological innovation suddenly makes perfect,
rational sense. OK, let's unpack this. Because
to really appreciate the brilliance of the transformer,
we first have to understand the sheer frustration
of the technology that came before it. Right.
The AI world before 2017 was not moving at this
breakneck speed. No, not at all. And there was
a very specific physical bottleneck holding everything
back. Yeah, so the era before 2017 was completely
dominated by recurrent neural networks. More
RNNs. Exactly, RNNs. And a specialized variation
called long short -term memory networks. LSTMs,
right? Yes, LSTMs. And these concepts, they were
originally conceptualized way back in the 1990s.
Wow, okay. Yeah, they were the standard for sequence
modeling. So things like translating a sentence
from French to English. But the fatal flaw built
into their very design was sequential processing.
Meaning what exactly? Meaning they operated on
one token or you know one word at a time strictly
from the first to the last. I always picture
this like trying to read a dense complicated
novel through a tiny cocktail straw. That is
a great analogy. Word by word, right? You look
through the straw and you see the word they,
then you move it and see quick, and by the time
you get to the very end of a long, twisting sentence,
you've completely forgotten how it started. Yeah,
and the technical term for that exact phenomenon
is the vanishing gradient problem. The vanishing
gradient. Right. Because an RNN processes the
input sequentially, updating its internal state
step by step, it has to compress all that historical
context into a single fixed size output vector.
Right. OK. So by the time the model reaches the
end of a long paragraph, its internal state lacks
any precise extractable information about the
words at the very beginning. The mathematical
signal just dilutes and, well, vanishes. So if
I feed it a massive document to translate, it's
essentially running out of memory by the final
punctuation mark. Exactly the information is
completely overwritten and smoothed out and you
know researchers were highly aware of this bottleneck.
They knew it was a problem. Oh yeah early models
like something called the RNN search model they
attempted to fix this by introducing early attention
mechanisms. Okay so attention was around before
the transformer. It was. This early attention
allowed the model to maintain a kind of memory
bank of the source sentence and sort of search
through it while generating the translation.
But the underlying architecture was still built
on recurrent networks. And that meant it suffered
from the ultimate sin of modern computing. It
could not be parallelized. Right. Because step
two physically cannot happen until step one is
finished. Exactly. You can't split the job across
a massive farm of modern graphic processing units,
you know, GPUs. Because the GPUs are just sitting
there waiting for the previous word to be processed.
And that sequential lock is what kept AI training
so slow. The hardware was fully capable of doing
millions of calculations at the same time, but
the RNN software was forcing it to work in single
file. Which brings us to the breakthrough. Yes.
The sheer limitation of reading sequentially
led some researchers to propose a radical hypothesis.
What if we stop reading in order? Right. What
if we just read the entire book all at once?
And that question birthed the 2017 Google paper
titled Attention is all you need, it proposed
dropping recurrence completely. I love the trivia
our source includes about this paper, by the
way. Oh, a family connection. Yes. One of the
authors, Jacob Ouzkarait, he suspected that attention
without any recurrence would be enough for translation.
Right. And this idea went so hard against the
conventional wisdom of the time that even his
father, Hans Uuskarait... Who happens to be a
very famous computational linguist, by the way.
Right. Even his dad was highly skeptical that
you could just drop a recurrence entirely. It
just sounded absurd. It really was a massive
leap of faith. I mean, to abandon the only method
that had reliably worked for sequence modeling,
the paper proposed an entirely new model that
relied purely on an attention mechanism to draw
global dependencies between input and output.
What's fascinating here is the specific mathematical
engine they invented to do this. Called scale
.productAttention. Exactly. This is the absolute
core of the engine, so let's really break this
down. From what I understand, the model learns
three specific weight matrices for every single
token. Right. The query, the key, and the value.
Q, K, and V. Yeah, so for every token in a sequence,
the model generates those three vectors, and
it uses them to mathematically ask a very specific
question. It asks, how much attention should
this word pay to every other word in the sequence?
Instead of a filing cabinet with like exact labels.
I like to think of it like asking a giant crowd
for a highly specific recommendation. Oh, I like
that. How does that work? So, Curie is me shouting
to the crowd, who here likes 1980s sci -fi movies?
Okay. And the keys are the graphic t -shirts
everyone in the crowd is wearing. Right, right.
The model uses a dot product, which is basically
a mathematical measurement of similarity. Yes,
it measures alignment. Right. To calculate how
closely everyone's t -shirt matches my question.
So, someone is wearing a retro Blade Runner shirt.
the math gives them a very high attention weight.
Exactly. Then the value is the actual movie recommendation
that person gives me. So the system aggregates
all the advice from the crowd, prioritizing the
people with the highest attention weights. That
is a highly accurate way to visualize a dot product.
It calculates the alignment between two vectors.
A higher dot product means the query and the
key are highly aligned, so the model pulls more
of that specific value into its understanding
of the word. Wow. And the transformer doesn't
just do this once. It uses multi -head attention.
Multi -head attention. Right. It runs this exact
crowdsourcing process you just described multiple
times simultaneously through different lenses.
So it's asking different types of questions to
the crowd at the exact same time. Precisely.
In one attention head, the query might be looking
for syntactic relationships, you know, like how
pronouns relate to their nouns. While in another
head... operating in parallel, the query might
be looking for semantic meaning, like how a verb
connects to its direct object. Or maybe another
head is just paying attention to the punctuation.
Exactly. And then it concatenates all these different
perspectives together. This is what allows the
model to capture incredibly complex, long -range
dependencies across the entire text instantly.
OK, wait. I need to push back logically here.
Go for it. If the model is reading the whole
sentence simultaneously, in pure parallel. How
does it actually know the order of the words?
Ah, that's the big question. Because to a purely
parallel system, aren't the sentences man bites
dog and dog bites man just the exact same collection
of words? Without a sequence, how does it know
the story? You hit the nail on the head. The
creators recognized that pure attention has zero
built -in sense of sequence. So to fix this,
they introduced positional encoding. Before the
text even hits that massive attention mechanism,
they mathematically inject a unique positional
signature into the token itself. So they basically
stamp every single word with GPS coordinates.
That's a perfect way to put it. The original
paper used complex sine and cosine functions
of different frequencies to create a fixed size
vector representing the absolute and relative
position of the token. And newer models. Newer
models use alternative methods like ROPEE. That
stands for rotary positional embedding. ROPEE.
Which literally rotates the vector representation
by a specific angle based on its position in
the sequence. But the underlying result is the
same. The model processes everything at once,
but the mathematical signature of the data tells
the model where everything is located relative
to everything else. Here's where it gets really
interesting, because when we keep saying words,
we're using a human term. Right. The transformer
doesn't actually know what an English word is.
No, it has no concept of a word. It goes through
a process called tokenization. Right. The architecture
natively consists of operations over numbers,
matrix multiplications, and dot products. It
takes the input text, chops it up into core segments,
and then a tokenizer breaks it down further into
tokens from a strictly fixed vocabulary. And
a token isn't always a whole word. Exactly. A
token could be a whole word, but often it's just
a chunk of a word. a subword like ing or pre,
those tokens are then mapped to integer identifiers
and finally embedded into high dimensional vectors.
And what comes out the other end is equally wild,
the unembedding layer. Yes. Because once the
transformer has done all its massive multi -headed
parallel processing, you have these rich, dense,
contextualized vectors. but you need to turn
them back into something a human can actually
read. Right, so it passes those final vectors
through a linear softmax layer. It takes the
vector, multiplies it by a weight matrix of shape
dm by the absolute size of the vocabulary to
produce logits, and then applies the softmax
function to normalize those. Okay wait, I'm gonna
stop you right there because that is pure textbook
territory. Oh fair enough, I get carried away.
Let's unpack that math into something a little
more visual. What is a logit and what is softmax?
Okay, okay. Imagine a giant scoreboard that contains
every single token in the AI's entire vocabulary.
Millions of subwords. Exactly. The model assigns
a raw mathematical score to every single item
on that scoreboard based on its processing. Those
raw scores? Those are the logits. But raw scores
are messy, right? Yeah. Like one word might have
a score of 5 ,000 and another might be negative
400. Right. which is incredibly hard to interpret.
So it runs all those raw scores through a mathematical
filter called the softmax function. Softmax essentially
forces all those wild numbers to fit into a neat
pie chart of percentages that perfectly add up
to 100%. Oh, I see. So it converts dense linear
algebra into a giant list of probabilities, saying,
you know, I am 99 % sure the next token should
be Apple. And maybe 0 .5 % sure it should be
banana, and 0 .001 % sure it's xylophone. Exactly.
It creates a clear, actionable probability distribution.
It ranks the vocabulary, picks the winner, and
that's the word that appears on your screen.
You got it. So now that we understand the engine,
massive parallel processing using queries, keys,
and values stamped with positional encodings
and filtered through a probability scoreboard.
Right. How do different companies actually build
cars around this engine? Our source outlines
a transformer family tree. But what really matters
here is the problem you are trying to solve,
right? You can't use the exact same architecture
to solve every problem. Exactly. The original
2017 transformer was an encoder -decoder model.
But researchers quickly realized you could rip
those two parts away from each other depending
on the specific task. OK, give me an example.
Well, if your goal is pure comprehension, Say
you are building a search engine that needs to
deeply understand the context of a document.
You build an encoder -only model, like Google's
BERT. Because an encoder -only model processes
the entire input all at once, looking forward
and backward simultaneously. Exactly. I always
compare training a model like Bert to playing
a massive high -speed game of Mad Libs. Oh, yeah.
You take a huge amount of text, you randomly
blank out certain words, you mask them, and you
force the AI to guess the blank spaces based
on the surrounding context. It learns the deep
structure of language by constantly trying to
fill in the blanks. Yeah, the loss function measures
how well it guesses those mass tokens, and it
subtly adjusts its internal weights after every
single guess. Fascinating. But now, if your goal
is completely different, if you want an AI to
actually write an essay or generate code, an
encoder -only model isn't the right tool. You
need a decoder -only model. And this is the GPT
series, Generative Pre -trained Transformer.
Which brings up a massive logical puzzle for
me. What's that? If the entire point of a transformer
is that it reads everything in parallel all at
once, how does a decoder model write a story
without cheating? If it can see the whole context
simultaneously, wouldn't it look at the end of
the sentence before it generates the beginning?
That is the exact problem with pure parallel
processing when generating text. To solve it,
decoder -only models use something called causal
masking. Causal masking. Right. When the model
is predicting token number 5, it physically cannot
pay attention to tokens 6, 7, or 8. The mask
matrix literally sets the attention weights for
any future tokens to negative infinity. The math
forces them to be completely ignored. Wow, okay.
So it effectively blinds the model to the future,
forcing it to generate text strictly step -by
-step. Autoregressively, yes. And then, if you
want the best of both worlds, like translating
a massive document where you need to deeply understand
the entire source text before generating a completely
new output sequence. Then you use an encoder
-decoder mix, like the T5 models. Right. Looking
at the timeline in our source, The speed of this
evolution is just staggering. Unbelievable, really.
In 2016, Google Translate revamped its system
using an early sequence -to -sequence model.
It took them nine months to develop, and it obliterated
their statistical approach that had taken 10
years to build. Ten years of work replaced in
nine months. Yeah. Then bird arrives in 2018,
GPT starts scaling up, and suddenly we have the
explosion of chat GPT in late 2022. The boom
was unprecedented. But it's important to note,
it hit a severe physical wall very quickly because
of the underlying math. Oh, right. Because the
self -attention mechanism requires every single
token to be compared against every other token.
This means the compute time scales quadratically.
Meaning, if you double the length of your prompt,
it doesn't get twice as hard for the model to
process. It gets four times harder. Exactly.
If you try to feed a model an entire 500 page
book, the computation becomes astronomically
expensive. Yeah. engineers had to get incredibly
creative to trip the math and make these tools
efficient enough to, you know, run on your phone.
Yeah, and the source outlined some brilliant
workarounds. A major one is KV caching. How does
that work? When a model like GPT is generating
text autoregressively, word by word, The query
vector changes at each step because it is actively
looking for the new next word. But the key and
value vectors for all the words it has already
processed in the prompt, those are static. They
don't change. So instead of throwing them away
and recalculating the entire prompt from scratch
for every single new syllable it spits out, KV
caching just saves those computed vectors directly
into memory. Exactly. It stores the historical
context, massively reducing redundant math. And
they paired the software trick with intense hardware
optimizations like flash attention. Flash it.
Yeah. Flash attention attacks the quadratic bottleneck
by recognizing a physical limitation of GPUs.
It's not just about doing the math. It's about
the physical traffic jam. of moving data back
and forth between the GPU's slow main memory
and its super fast processing cache. It's like
having the world's fastest chef in a kitchen
but forcing him to walk down the street to the
grocery store every time he needs a single onion.
Exactly. Flash Attention reorganizes the algorithm
so it performs matrix multiplications and blocks
that fit perfectly within that fast local cache.
That's so smart. It calculates the attention
weights without constantly writing intermediate
results back to the slow memory. It's a communication
avoiding algorithm that drastically speeds up
the physical processing time. I also have to
raise a fascinating optimization mentioned in
the notes. Speculative decoding. Oh, that's a
good one. This is such a clever structural idea.
You basically use a smaller, faster, much cheaper
model to quickly guess the next few words in
a sequence. Right. Like a junior assistant hurriedly
writing a rough draft. Then you pass those guesses
to the massive expensive model. because it's
a transformer, the big model can read and verify
all those guesses in parallel instantly. If the
big boss agrees with the draft, you just saved
a massive amount of generation time. If it disagrees,
it just throws out the bad guesses. and computes
the right answer itself. It trades parallel verification
speed for autoregressive decoding time. It leans
entirely into the exact strengths of the architecture.
And if we connect this to the bigger picture,
the realization that this architecture is incredibly
flexible is really what launched the current
era. Right, because the transformer didn't stay
confined to text. Not at all. We saw the move
to multimodality, treating everything like a
language. Researchers realized that a transformer
has no idea what a token actually represents.
Like we said earlier, it only processes numbers.
So what happens if a token isn't a subword? What
if you take a high -resolution image, break it
down into a grid of tiny square patches, turn
those visual patches into vectors, and feed them
into a transformer? You get vision transformers.
This is the technology underlying incredible
image generators, like Dali. or video generators
like Sora. It treats groups of pixels the exact
same way it treats syllables. And you can do
the exact same thing with audio. Models like
Whisper turn human speech into a visual spectrogram
image, break that image into patches, and process
it with attention. That is wild. It even conquered
complex biology. AlphaFold used an attention
-based architecture to solve the 50 -year -old
grand challenge of protein folding. Oh, wow.
Biology, too. Yeah. It treated individual amino
acids as tokens in a sequence, and the attention
mechanism figured out how they interact to predict
a complex 3D biological structure. You know,
I want to point out an absolutely mind -blowing
fact from the source material regarding just
how deeply this architecture understands relationships.
The chess thing. Yes. A transformer learned to
play chess at a grand master level. an ELO of
2895. And it did this purely through static board
evaluation. Which breaks every single rule of
traditional chess engines. Right. Programs like
Stockfish rely on massive search trees. They
evaluate a board by calculating millions of future
possibilities, branching out move after move.
But this transformer didn't search ahead at all.
It didn't look into the future. No. It took the
64 squares and the pieces on them, converted
that state into tokens, and processed them through
its attention mechanisms. It just looked at the
current board, understood the complex relationships
and tensions between the pieces, and instantly
knew the best move. It's credible. It understood
the geometry of the board so deeply that it didn't
need to calculate the future. It just recognized
the pattern. It recognized the global dependencies
of a chessboard the exact same way it recognizes
the grammar of an English sentence. It really
is a stunning testament to how universally powerful
the attention mechanism really is. So what does
this all actually mean? If we zoom out. The transformer
changed the world by completely abandoning the
human way of reading sequentially. It embraced
massive parallel processing using queries, keys,
and values to calculate attention, allowing AI
to hold and understand deep context, not just
in a paragraph of text, but in a grid of pixels,
a wave of audio, or a chain of biology. So the
next time you type a prompt into an AI, you can
visualize the sheer scale of what is happening
under the hood. before he answers you, there
is a massive mathematical cocktail party happening
inside the GPU. Thousands of queries and keys
are mingling, measuring their similarity, pulling
values, and passing through multiple multi -head
lenses to understand the exact context of what
you just asked. It really is beautiful when you
break it down into the mechanics. Yeah. But I
want to leave you with a final somewhat provocative
thought to mull over. OK. If the exact same transformer
architecture just dot products, matrices, and
attention weights. If it can learn the grammar
of human language and the grammar of pixels in
a video, the grammar of amino acids in a biological
protein, it raises an important question. Is
everything in our universe ultimately just a
sequence of tokens? And if so, what other secrets
of the universe are just waiting for the right
attention mechanism to decode them? That is a
fascinating thought to take with you. Thank you
so much for taking this deep dive with us. Keep
questioning, keep learning. and we'll catch you
next time.