Imagine taking the mathematical concept of a
king. You subtract the underlying math of a man.
You add the mathematical concept of a woman.
And the final equation just perfectly equals
the word queen. Right. And I mean, that isn't
some weird riddle. It's literally algebra. semantic
algebra being performed right now by the algorithms
powering the phone in your pocket. Yeah, which
is just wild to think about. It sounds like straight
up science fiction. It really does. But it's
actually the foundational mechanism of how machines
have learned to, well... process our world. And
you interact with these systems every single
day. I mean, we all do, whether it's the autocorrect
fixing your texts or maybe a chat bot retrieving
a lost password for you. Or even the route optimization
tool, you know, the one constantly recalculating
your commute based on traffic patterns. Exactly.
But we rarely stop to ask the really obvious
question, which is how did a machine literally
just a box of silicon and wire actually learn
to speak? Yeah. How did it learn to predict the
exact word you were about to type? Right. So
today our mission for this deep dive is to journey
through the massive decades long evolution of
language models. And we're using a really comprehensive
Wikipedia article on language models as our core
guide for this. We are. We're going to trace
the path all the way from the 1950s era of strict,
rigid grammar rules right up to the massive multi
-billion parameter neural networks that are basically
reshaping the modern economy. It's a huge shift.
Okay, let's unpack this. Where do we even begin
teaching a machine to understand something as
incredibly messy as human language? Well, we
really have to start in the 1950s, specifically
with the pioneering work of a linguist named
Noam Chomsky. Oh, right, Chomsky. Yeah, so the
initial philosophy for teaching machines back
then was heavily, heavily rule -based. It was
built around Chomsky's theory of formal grammars.
Formal grammars, meaning what, exactly? Well,
the prevailing thought of the era was highly
structured. Researchers basically believe that
if you could just teach a computer the exact
comprehensive structural rules of a language
like the precise syntax Exactly the syntax the
grammar the correct way to conjugate literally
every verb Yeah, they thought if it knows all
the rules it could theoretically generate and
understand any text I mean, I guess that makes
complete sense on paper on paper sure But when
you actually think about it that approach treats
the computer like an incredibly strict rigid
English teacher. Oh, absolutely. You know the
type, right? The one who stands at the chalkboard
diagramming sentences and like fails you if you
use a dangling modifier or end a sentence with
a preposition. Yeah, the ones who don't let you
start a sentence with and. Exactly. But the problem
is language. as you and I actually use it in
the real world, is chaotic. It's totally chaotic.
It's full of slang, broken rules, sarcasm, idioms.
Like, if I tell a rule -based computer from the
50s that someone kicked the bucket, the machine
is going to literally start looking around for
a plastic pail. Right. It has no context for
the metaphor, which is the exact reason that
rule -based approach eventually hit an absolute
wall. It just couldn't scale. Not at all. Researchers
realized you just couldn't write enough individual
rules to capture the infinite, messy variability
of human communication. It was just too brittle.
So what did they do? So by 1980, the field's
entire philosophy shifted. IBM actually started
conducting what our source text calls Shannon
-style experiments. Shannon -style. That sounds
like they started looking at the problem from
a totally different angle. If they aren't teaching
the rules anymore, what are they doing? they
started observing human behavior instead. In
these Shannon -style experiments, researchers
essentially watched human subjects predict and
correct text. Oh wow, so like watching a person
guess what comes next. Exactly. They observed
how a person naturally almost subconsciously
guesses what the next letter or word in a sentence
is going to be based purely on context. It's
just feeling it out based on what they've read
before. Right. And IBM used those observations
to figure out how to improve language modeling
for machines. They moved completely away from
trying to be a perfect grammarian and shifted
to pure statistical models. That is a massive
philosophical pivot. It changed everything. This
led to the breakthrough of something called word
engrams. An engram model basically calculates
the probability of the next word appearing based
solely on a fixed window of previous words. OK,
so let me make sure I've got this. If the machine
only looks at one previous word to make its guess,
it's called a big -gram model. You got it. And
if it looks at the two previous words, it's a
trigram model. Exactly right. And then it scales
all the way up to an n -gram model, which just
looks at n minus one words. Yeah, the window
just gets larger. And our source also mentions
they had to introduce special tokens for this.
Tokens. Like what? Like little s inside angle
brackets. They use those just to give the machine
a strict mathematical signal of where a sentence
formally starts and ends. Okay, so the machine
is no longer actually reading, it's just calculating
odds. Pure odds. If it sees the word peanut,
And it knows from scanning a vast database of
books that the word butter follows peanuts, say,
a huge percentage of the time. It simply assigns
a super high mathematical probability to butter
being the next word. Okay, so if the 1950s approach
was the strict English teacher... The 1980s endergram
approach is basically like a savvy casino gambler.
Oh, I like that. Like the machine doesn't actually
know what peanut butter is, it doesn't care about
the rules of culinary grammar, and it certainly
has never tasted a sandwich. Definitely not.
It's just sitting there at the blackjack table
playing the mathematical odds on what card, or
in this case, what word shows up next, based
entirely on historical data. The casino analogy
captures the mechanism beautifully. because it
is a pure game of statistical probability. But
any gambler will tell you that relying entirely
on historical odds introduces a pretty fatal
vulnerability. Which is? What happens when the
mathematical model encounters a sequence of words
it has absolutely never seen before in its training
data. Oh. Wait, think about how your brain works
for a second. If I say a totally brand new, bizarre
sentence to you listening right now, something
completely random like, uh, the purple giraffe
tap danced on my toaster. Which I've definitely
never heard before. Right. But you still understand
the sentence. You know what those words mean,
even if they've literally never been combined
in the history of the English language. Exactly.
We can process that novelty. But for the gambler
machine, because giraffe and toaster have never
appeared next to each other in its historical
database, the mathematical probability of that
combination is literally zero. And that breaks
the entire algorithm. In computer science, this
is known as the zero probability problem. It
just crashes. Pretty much. Language models calculate
the likelihood of a whole sentence by multiplying
the probabilities of each word sequence together.
Okay. So if even one of those sequences has a
probability of zero, the entire equation multiplies
out to zero. The model essentially throws its
hands up and just fails. Because anything multiplied
by zero is just zero. Exactly. The gambler goes
completely bust just because it saw a purple
giraffe. What's fascinating here is how researchers
engineered a way around this mathematical dead
end. How do you fix a zero? They developed techniques
collectively called smoothing. The most basic
form of this is called add one smoothing. Add
one? Yeah, the engineers essentially forced the
algorithm to pretend it has seen every possible
word combination at least once. It assigns a
baseline count of 1 to all unseen n -grams, so
the math never hits absolute zero. Oh, wow. It's
literally like spotting the gambler a free chip
just to keep them in the game. That's a great
way to put it. But our source mentions the math
gets much more sophisticated than just adding
a one, right? Like, there are techniques called
good -turing discounting or back -off models.
Yeah, it gets incredibly complex. Those more
advanced smoothing techniques operate on a brilliant
principle of, well, redistribution. Redistribution
of what? The odds. Exactly. The model takes a
tiny, tiny fraction of a percent of probability
away from the highly common word combinations
it has seen a million times. OK. And it redistributes
that probability into a sort of rainy day fund
for the unseen combinations. So it smooths out
the whole distribution curve. So clever. It is.
But even with smoothing, they faced another massive
hurdle, which was called data sparsity. Data
sparsity, meaning like no matter how many books
you feed the machine, human language is just
so vast that the data is always going to be spread
way too thin to make highly accurate predictions.
Right. If you're only looking at consecutive
words, you're missing out on so much context.
So how do they fix that? To solve the sparsity
issue, researchers created the skip gram model.
Instead of only calculating the odds of words
that appear strictly right next to each other,
the model was suddenly allowed to, well, skip
gaps. OK, I want to make sure you all listening
really grasp this mechanism, because the skipgram
model is a pretty wild concept. Let's use the
mandatory example provided right in our source
text to illustrate this. Yeah, it's a perfect
example. Take the classic sentence. The rain
in Spain falls mainly on the plain. In the older
traditional enneagram model, the machine only
looks at consecutive pairs, right? So it sees
the rain in Spain. It's very narrow. Super narrow.
But when researchers implemented a one -skip,
two -gram model, the machine is suddenly allowed
to jump over one word to form its pairs. It starts
capturing subsequences that skip a beat. Oh,
so from that exact same sentence, the machine
suddenly extracts pairs like the N, rain -spain,
infalls. Spain, mainly, falls on. You're pulling
way more data out of the same text. Wait, let
me make sure I'm visualizing this correctly.
Is the skipgram model essentially like reading
a sentence but covering up every other word with
your hand, just to see if the overall context
still provides a relationship? Yeah, that's a
highly effective way to visualize it. By doing
that, rain and falls get mathematically linked,
even though the word in was sitting right between
them. Exactly. By forcing the machine to understand
relationships across a slightly wider alternating
distance, it basically overcomes data sparsity.
It extracts exponentially more relational data
like more connective tissue out of the exact
same sentence. That is incredibly clever engineering.
It really was a huge leap. But I mean, I'm looking
at this timeline, and even with skip grams and
smoothing, These 1980s and 90s models still fundamentally
viewed words as isolated, discrete puzzle pieces.
They did? They were still just calculating the
odds of symbols appearing near each other. How
did the machine eventually learn that words actually
have, you know, meanings? Well, that conceptual
leap brings us to the 2000s, which really marked
one of the most significant paradigm shifts in
computer science. What changed? The field basically
realized that counting discrete words just wasn't
enough anymore. They moved towards what are called
continuous representations. And this birthed
the era of word embeddings. Word embeddings.
Yeah, when I read this part of the source material,
it genuinely started to feel like we were entering
the matrix. It is a profoundly abstract concept
for sure. Researchers figured out how to translate
words into real, valued vectors inside a massive,
multi -dimensional mathematical space. Okay,
for you listening, try to imagine a gigantic,
invisible 3D graph floating in space, and every
single word in the human vocabulary is plotted
as a specific physical coordinate on that graph.
And the true genius of word embeddings is exactly
how those coordinates are assigned. How do they
decide where to put them? Words that share similar
meanings or contexts are mapped physically closer
together in this mathematical vector space. Oh.
So the coordinate for the word dog is physically
sitting much, much closer to the coordinate for
puppy than it is to the coordinate for, say,
refrigerator. That is insane. They literally
figured out how to map semantic meaning to spatial
math. Yes. And the architecture goes way beyond
simple synonyms, too. This continuous mathematical
space actually preserves complex, common relationships
between words like plurality, tense, and even
gender. Right. Our source highlights a really
famous equation that proved this concept actually
worked. Yeah, let's break that down. Let's say
the letter V stands for the vector, the exact
mathematical coordinate of a word. The model
discovered that the vector for king minus the
vector for male plus the vector for female approximately
equals the vector for queen. So what does this
all mean? V female plus V queen. Let me walk
through this. The machine locates the exact mathematical
coordinate for the concept of a king. Right.
It subtracts the mathematical distance representing
maleness, adds the distance representing femaleness,
and when it looks at whatever coordinate it lands
on. The absolute nearest neighbor hovering in
that exact space happens to be the word queen.
The machine is doing algebra with human concept.
It is executing semantic algebra. It captures
compositionality. I mean, the algorithm doesn't
possess a human brain. It has absolutely no lived
experience of royalty or gender. Obviously. But
it has mapped out the relationship between those
concepts perfectly, using literally nothing but
distance and direction in a vector space. That
concept just blows my mind every time I think
about it. It's beautiful math. But practically
speaking... If you are plotting hundreds of thousands
of words into multi -dimensional space and running
algebraic equations on all of them simultaneously
to predict a sentence, you need astronomical
computing power. Well, you do. Massive amounts.
And the early computing architectures designed
to process these continuous space embeddings
were called recurrent neural networks, or RNNs.
RNNs? Yeah. They were crucial because they solved
a massive bottleneck known in the field as The
Curse of Dimensionality. The Curse of Dimensionality.
That sounds like the title of a terrible sci
-fi thriller. It kind of does. But it's a very
real mathematical nightmare. What is it? It basically
dictates that as your vocabulary grows, say,
from 10 ,000 words to 100 ,000 words, the number
of possible sequence combinations increases exponentially.
Right. Because every new word adds millions of
new potential sentences. Exactly. It quickly
becomes a number so massive that no computer
could ever calculate it using the old anagram
methods. So RNNs bypass this curse by representing
words not as fixed sequences, but as non -linear
combinations of weight. inside a neural network.
Ah, so they found a way to mathematically squish
the infinite possibilities down into something
actually manageable. Essentially, yes. But even
those RNNs had their limitations, right? Because
they still had to process information in a specific
linear way. Which brings us to 2017. Here's where
it gets really interesting. 2017 is the year
the entire trajectory of artificial intelligence
changed. Completely. What happened? This is when
researchers introduced the transformer architecture.
It completely replaced the recurrence mechanism
in RNNs with a brand new mechanism called self
-attention. Self -attention. Let's break that
down for the listener because the difference
in mechanism is literally everything here. Let's
do it. An older RNN model was kind of like reading
a book linearly. Right. It reads word by word
left to right. And by the time it reaches the
end of a really long paragraph, it sort of strains
to remember what the subject was at the very
beginning. It loses the thread. Exactly. But
transformers, utilizing this new self -attention
mechanism, operate entirely differently. It's
like having the superpower to instantly scan
an entire page at once. Yes. It immediately draws
invisible highlighter lines connecting every
single word to every other word simultaneously,
regardless of where they sit on the page. So
it instantly knows the word bank at the bottom
of the page refers to the word river at the top
of the page. So it knows we're talking about
water, not a financial institution. Exactly.
The technical term for what you're describing
is parallelization. Parallelization. Yeah, because
transformers process all the data simultaneously
rather than sequentially left to right. They
can handle massive context when They don't forget
the beginning of the paragraph anymore. Right.
And more importantly, parallel processing allowed
for scalable training on an unprecedented volume
of data. For the first time, researchers could
realistically scrape and process the entire public
Internet. Which is just wild. They literally
fed the machine the Internet. They did. And this
is the birth of large language models, or LLMs,
the foundation for modern models like GPT and
BERT. The scale is just staggering. We evolved
from simple statistical algorithms to deep neural
networks consisting of billions and now trillions
of parameters. And when you scale up the data
and the parameters to that extreme, our source
text notes that these models started showcasing
what scientists call emergent behaviors. Yeah,
unexpected capabilities. They suddenly started
doing things they were never explicitly programmed
to do, right? They demonstrated phenomena like
few -shot learning, meaning the model could learn
a completely new task, like translating a new
language, after being shown just a tiny handful
of examples. Wow. They also displayed compositional
reasoning, where they were combining multiple
distinct concepts to solve complex logic problems
they had never seen before. OK, but let's pause
for a second and look at the reality of what
we've built here. We've constructed a massively
powerful parallel process in brain that read
the entire public internet to learn how to speak.
Yes, we did. But you and I both know the internet
is. Well, it's the internet. It's a mess. It
contains absolute brilliance, yes, but it also
contains vast inaccuracies, weird strange rabbit
holes, and terrible toxic advice. Oh, definitely.
And the model inherently digests and inherits
all of that raw data. So how do engineers take
a machine trained on the total chaos of the internet
and make it factual, helpful, and safe for a
user to actually interact with? Well, that is
the multi -billion dollar engineering challenge
of our current era. I bet. And the primary solution
the industry has implemented is a process called
RLHF. our LHF, which stands for Reinforcement
Learning from Human Feedback. OK, let's dig into
the mechanics of that. How do you reinforce a
trillion parameter neural network? Well, it utilizes
policy gradient algorithms to fine tune the LLM.
The model generates a potential output. which
is dictated by its internal policy, and that
output is then graded against reward signals
derived from human preference judgments or automated
systems. Wait, okay, let me simplify that. Think
of it like a video game high score, right? The
machine spits out an answer to a prompt. A human
evaluator looks at it and essentially gives it
a thumbs up for being factual and helpful, or
a thumbs down for being inaccurate or unhelpful.
The algorithm then slightly tweaks its internal
policy, its rulebook for choosing words, so it's
math - automatically more likely to chase that
thumbs up high score the next time it gets a
similar prompt. That's a great analogy. It iteratively
optimizes the model's output distribution. This
is how developers drastically improve factuality
and align the model's responses with what users
actually need. Got it. But of course, once you
train it with RLHF, you have to definitively
prove the system is actually smart. So researchers
rigorously evaluate these models against comprehensive
standardized benchmarks. Right. Our source mentions
a few of these specific testing frameworks like
MMLU, the GLUE benchmark, and the SQUAD question
answering test. Yes. These are basically the
standardized SATs for artificial intelligence,
right? Testing everything from basic reading
comprehension to advanced physics. Exactly. But
if we connect this to the bigger picture, relying
so heavily on these standardized benchmarks introduces
a pretty major controversy in the computer science
community. A controversy over tests. Yeah. It's
a phenomena known as hill climbing. Wait, if
developers are constantly tweaking these models
specifically to beat the MMLU or the GLUE benchmark,
isn't there a massive risk that they are just
teaching to the test? You hit the nail on the
head. Like, are these models actually achieving
genuine generalization and getting smarter? Or
are they just like clever high school students
who figured out how to ace a standardized test
without actually understanding the underlying
material? That is precisely what hill climbing
means, and you've hit on the exact debate dividing
AI researchers right now. Are we witnessing robust
capability improvements in machine intelligence,
or are we just iteratively engineering incredible
test -takers that are basically overfitting to
the evaluation metrics? That's a huge distinction.
It is. The source material makes it remarkably
clear, actually. While these language models
sometimes match or even exceed human performance
on specific tasks, it is absolutely not clear
whether they serve as plausible cognitive models.
Meaning, even though they can speak to us perfectly,
they do not think like we do. Not at all. The
text points out a really fascinating paradox.
When analyzing recurrent neural networks, researchers
found that the models sometimes learn intricate
mathematical patterns that humans are completely
blind to. Really? Yeah. And conversely, those
exact same models will utterly fail to learn
simple contextual patterns that a human typically
grasps with effortless ease. Okay, wow. Let's
summarize this incredible journey for everyone
listening because we covered a lot of ground.
We really did. We started in the 1950s with Noam
Chomsky trying to force rigid grammar rules into
a machine, sort of like a strict teacher. Right.
When that failed, we pivoted to the 1980s, where
IBM's statistical guesses and the gambler's n
-grams took over, eventually learning to stip
words to find connections with skip -grams. And
solving the zero problem along the way. Exactly.
Then came the 2000s, where words were transformed
into mathematical embeddings, doing semantic
algebra in a 3D vector space. The king minus
man plus woman equals queen. Yes. And finally,
the neural revolution gave us self -attending
transformers trained on the entirety of the internet
and fine -tuned by human feedback to chase a
high score of helpfulness. It is a phenomenal,
decades -long evolution. It's combining linguistics,
mathematics, and raw computational architecture.
And it matters to you. It matters deeply to everyone
listening. Absolutely. Every single time you
use a chat bot to help draft a difficult email
or rely on search information retrieval to find
an obscure fact, or just let your phone's route
optimization guide you around a traffic jam,
you are interacting with the ghosts of this exact
evolution. You're using the math. You are utilizing
the skip grams, the multi -dimensional vectors,
and the self -attention mechanisms built over
70 years. This raises an important question,
though. Oh. One that lingers at the very end
of our source material, and it's something I
think we all need to reckon with. Lay it on us.
If these machines don't learn the way human beings
learn, I mean, if their internal architecture
allows them to see vast statistical patterns
we are blind to, and yet they completely miss
the obvious intuitive things that a human toddler
can grasp instinctively, what does it mean for
our future? Man. It's an unsettling thought to
leave on. It is. Because as humans, we expect
shared perception. When we look at an X -ray,
we see the broken bone and we expect the machine
to see the exact same reality we do. Right. We
expect it to process like us. But we are increasingly
relying on an entirely alien form of intelligence
to process and filter the world's information
for us. We spent 70 years teaching the machine
to speak our language, but it turns out under
the hood, it's not speaking our language. at
all. It's just doing the math. Just doing the
math. Something to ponder the next time your
phone auto completes your thought before you've
even finished typing it. Thanks for joining us
on this deep dive. We'll see you next time.