It is really funny how quickly we just get used
to magic. Oh, completely. We take it entirely
for granted. Right. Like every single day, you
probably dictate a text message to your phone
while you're just walking down the street. Or
you type half a question into a search engine,
and it just instantly finishes your sentence
for you. Exactly. It knows what you want before
you do. Yeah. You interact with it constantly.
But you probably rarely stop to think about the
massive invisible machinery making all of that
happen. So welcome to the deep dive. Today, our
mission is to decode that exact machinery. It
is quite the rabbit hole. It really is. We are
looking at a comprehensive overview of natural
language processing, or NLP, to figure out exactly
how computers learn to understand, process, and
even generate human language. And, you know,
the journey to get to this point is just full
of surprises, dead ends, and some truly profound
questions about what it actually means to communicate.
Oh, absolutely. We're going to see how the scientific
community had to, well, completely change its
mind several times over the decades to finally
crack this code. It is a wild ride. And to get
our heads around the core challenge here, I want
you to imagine trying to teach a highly logical,
incredibly literal minded alien how to appreciate
a poem. That is a great analogy. Because for
a computer, the math part is easy, right? Crunching
numbers, storing massive spreadsheets, no problem
at all. But human language is a nightmare. It
really is. It's messy, it's full of sarcasm and
double meanings, and the rules are broken as
often as they are followed. If we connect this
to the bigger picture, it is crucial to understand
that NLP isn't just about building a better spell
check. In the field of computer science, mastering
natural language is actually considered an AI
complete problem. Wait, AI complete? That sounds
intense. Yeah, it does. What it means is that
to fully solve language, you know, to get a machine
to truly comprehend it the way you or I do, you
essentially have to solve artificial intelligence
itself. Because language doesn't exist in a vacuum.
Exactly. To understand a sentence? You need real
-world knowledge. You need an understanding of
context, and you need logical reasoning. You
can't just program a dictionary into a machine.
You literally have to program an understanding
of the entire human experience. Which is exactly
why the early attempts to solve this problem
were just, well, so overly optimistic. Oh, they
were incredibly naive. Right. To understand how
we got to the seemingly magical AI you have in
your pocket today, we really have to start with
how early computer scientists in the 1950s thought
language worked. Basically, they treated it like
rigid algebra. Yeah, the roots of NLP go all
the way back to Alan Turing in the 1950s. I mean,
he proposed the Turing test, which inherently
relied on a machine's ability to interpret and
generate natural language to prove its intelligence.
Right. The ultimate benchmark. But the real burst
of early confidence, though, that came in 1954
was something called the Georgetown experiment.
Oh, the Georgetown experiment, where the researchers
managed to get a computer to automatically translate
over 60 Russian sentences into English. Exactly.
And based on that tiny, highly controlled success,
the authors boldly claimed that machine translation
would be a completely solved problem within like
three to five years. Wow. Three to five years
to solve language. It's a classic case of underestimating
the complexity of a problem. They thought language
was just a matter of swapping words and applying
a few structural rules. Like, you look up the
Russian word, you find the English equivalent,
you swap them, and you apply a grammar rule.
Boom. Done. Right. But real progress was agonizingly
slow because language isn't just a simple cipher.
And by 1966, a famous evaluation called the ALPAC
report came out. Oh yeah, the ALPAC report. That
was devastating. It brutally concluded that 10
years of really expensive research had basically
failed to fulfill any of those grand expectations.
And that report didn't just hurt feelings. It
crushed the entire industry. Funding for machine
translation in the US was dramatically slashed,
and serious research was largely paused until
the late 1980s. Yeah, that whole era is often
referred to as symbolic NLP. The philosophy was
basically to hand code complex sets of rules
to manipulate symbols. Which sounds good on paper.
Sure, but to understand the fundamental flaw
in this approach, you just have to look at John
Searle's famous Chinese room thought experiment
from the source text. Okay, lay that out for
us. So imagine a person locked in a room who
doesn't speak a single word of Chinese. They
are given a batch of Chinese writing and a giant
rule book in English. And that rule book tells
them exactly how to match certain Chinese symbols
with other Chinese symbols to form answers. So
do the people outside the room slipping questions
under the door? or it looks like the person inside
is perfectly fluent in Chinese. Exactly. They
are getting totally coherent answers back. But
the computer or the person in the room is just
emulating understanding by blindly applying rules
to the data it confronts. There's no actual comprehension
happening. Not at all. The machine is just moving
shapes around a board. But they did have some
wild successes in the 1960s, even with severely
limited computer memory. I mean, there's a program
called SHRDLU, which could understand commands
about manipulating toy blocks in a restricted
vocabulary. Yeah, SHRDLU is a big deal. And then
there was ELIZA, created by Joseph Weissenbaum
between 1964 and 1966. And that simulated a psychotherapist.
ELIZA is such a fascinating milestone because
it highlights the human side of the equation.
OK, let's unpack this. If ELIZA was just following
these rigid mathematical rules without any real
comprehension, Why did people feel so deeply
connected to it? Like, users reportedly shared
incredibly intimate personal details with this
program, treating it like a real living therapist.
Well, because Eliza was a brilliantly designed
illusion. It used almost no information about
actual human thought or emotion. Weizenbaum designed
it so that when the patient, you know, the user,
exceeded the program's very small knowledge base,
the system would simply fall back on generic
deflections. Oh, I see. Yeah. If you typed, my
head hurts, Eliza would just turn it around and
say, why do you say your head hurts? Oh, wow.
It mimicked the active listening of a therapist.
Precisely. It didn't need to understand pain.
It just needed to reflect the statement back
as a question. This made humans project empathy
onto the machine. That's incredible. But underneath
it all, rule -based systems hit a massive wall.
Because language is just too weird. You can hand
code rules for the most common sentence structures,
sure, but human language has an infinite number
of rare, weird exceptions. Absolutely. You simply
cannot manually write an algebraic rule for every
single edge case. The complexity becomes totally
intractable. Which is why the scientific community
had to completely pivot. Because human language
had too many exceptions for handwritten rules,
scientists in the late 1980s and 1990s realized
they needed a completely different approach.
They stopped trying to teach computers grammar,
and they started teaching them probability. Here's
where it gets really interesting. This is the
statistical revolution, and it officially ended
that long AI winter where funding had totally
dried up. Yeah, finally. Instead of linguists
writing out complex syntax trees by hand, computer
scientists started using machine learning algorithms.
And two crucial things fueled this shift. First,
Moore's Law meant computers were finally getting
the processing power and memory they needed to
handle massive amounts of text. Right. But second,
there is a huge philosophical shift away from
the dominant linguistic theories of the time,
particularly those championed by Noam Chomsky.
Ah, Chomsky. He argued for something called the
poverty of the stimulus. Exactly. The basic idea
was that human children don't get enough exposure
to language data to learn all its complex rules
from scratch, so the foundation of language must
be hardwired into our brains. Yeah, that was
the prevailing thought. And because of that theoretical
underpinning, scientists were actually discouraged
from using real -world corpus data, which is
just massive collections of text, because the
theory essentially said general learning algorithms
wouldn't work on language. But the computer scientists
decided to try the data anyway. And it worked
beautifully. It really did. IBM research had
a massive breakthrough in machine translation
by feeding their algorithms existing multilingual
texts. And the texts they used are so perfectly
mundane. It was the translated proceedings of
the Parliament of Canada and the European Union.
Which is so clever when you think about it. Because
the law required those governmental proceedings
to be translated into all official languages,
it created a perfect, naturally occurring data
set. Yes. The algorithm could look at a sentence
in English, look at the exact same sentence translated
into French, and statistically calculate the
probability that a specific word translates to
another specific word. Then the 2000s hit, the
World Wide Web exploded, and suddenly we had
endless raw unannotated data. Oh, it was a gold
mine. We moved right into unsupervised and semi
-supervised learning. The machine could just
absorb millions of web pages and learn the statistical
likelihood of which words follow other words.
To put this in perspective for you, symbolic
NLP, that 1950s approach we talked about, is
like trying to navigate a new city using only
a rigid paper map and a dictionary. You are constantly
stopping to look up rules, and if a street is
closed, You were completely lost. You're stuck.
Right. But statistical NLP is like learning a
language by moving to the country, binge -watching
foreign TV shows, and absorbing the patterns
until it just sounds right. What's fascinating
here is that these statistical language models
turned out to be far more robust than the old
rule -based systems. I mean, a rule -based system
breaks if you misspell a single word because
the rigid rule just no longer applies. It crashes.
Exactly. But a probabilistic model, it has seen
that exact spelling a thousand times in its web
data. It calculates that there is a 99 % chance
you meant to type hello instead of hello. It
knows what you probably meant. Yeah. With the
statistical approach, the larger your probabilistic
model becomes, the more accurate it gets. Counting
words and calculating probabilities was a huge
leap, but those statistical methods still required
a lot of human handholding. engineers had to
do elaborate feature engineering which basically
means they had to manually tell the system what
linguistic features to look for right like pay
attention to verbs here pay attention to adjectives
there exactly but the next evolution starting
in the 2010s removed that human handholding entirely.
Yeah, the transition really started gaining ground
when neural networks began outperforming the
best statistical algorithms. In 2003, a multilayer
perceptron neural network beat out older models.
Then in 2010, a researcher named Tomas Mikolov
applied a recurrent neural network to language
modeling. And that eventually led to a breakthrough
tool called Word2Vec. Yes, Word2Vec. We hear
the phrase neural networks all the time, but
the way they a language is just wild. We entered
the era of deep learning and representation learning.
The machine was no longer just counting how often
two words appear next to each other, it was figuring
out the hidden representations and relationships
between them. Word2vec is a perfect example of
this. Instead of just seeing words as isolated
text, it turns words into multi -dimensional
mathematical vectors. It maps them in a vast
mathematical space. Okay, how does that work?
So the distance between the vector for king and
queen becomes mathematically similar to the distance
between man and woman. The network discovers
these underlying semantic relationships purely
on its own without a human ever defining what
gender or royalty even is. Wow. It made all those
intermediate manual steps, like manually aligning
words, totally obsolete. Completely. But wait,
before the neural network can map all these concepts,
it still has to break the raw text down into
pieces it can actually ingest. You can't just
swallow a whole book at once. No, it can't. That
process is called tokenization. It means dividing
text into fragments tokens and giving them numerical
identifiers. For a machine to process a sentence,
it has to turn the words into numbers. Wait,
for English, isn't tokenization ridiculously
easy? You just look for the blank spaces between
the words? Well, yeah, it is easy for English.
But think about languages like Chinese, Japanese,
or Thai. Oh, right. They do not mark word boundaries
with spaces. A sentence is just a continuous
string of characters. To segment text in those
languages, the system needs a deep, complex knowledge
of the vocabulary and the morphology of language.
You can't just look for a blank space. The algorithm
has to mathematically deduce where one word concept
ends and another begins based on surrounding
context. And it has to be even harder when we
move from perfectly typed text to just talking
to our phones. When I speak to my voice assistant,
I am definitely not leaving neat little spaces
between my words. Speech recognition is incredibly
difficult because of a phenomenon called co -articulation.
Coarticulation. Yeah. When humies speak naturally,
the sounds representing successive letters actually
blend together. The end of one word slurs right
into the beginning of the next. Your human brain
separates them effortlessly based on context.
But converting that continuous unbroken analog
sound wave into discrete textual characters is
a massive hurdle for a machine. That makes total
sense. Add in the wide variety of human accents,
regional dialects, and just random background
noise. And it's a monumental computational task.
But the payoff for cracking this is huge. You
pointed out in our notes how these neural approaches
are revolutionizing health care right now. Oh,
absolutely. NLP is unlocking unstructured text
in electronic health records. Doctors write notes
in shorthand full of obscure jargon, messy syntax
and acronyms. Right. Chicken scratch, basically.
Yeah. And traditional database. can't read that.
But neural NLP can read those unstructured notes,
analyze them to spot early warning signs for
patient care, and simultaneously strip out sensitive
terms to protect patient privacy. It's turning
locked away messy text into actionable medical
insights. So we've got the computer perfectly
transcribing our blended speech, and it has successfully
tokenized the words into numbers. But identifying
a word is very different from actually understanding
what it means in the real world. Extremely different.
This is where we hit the absolute wildest quirks
of human communication. Meaning is incredibly
messy. Let's start with just figuring out the
base form of a word. When a machine sees words
like running, ran, and runs, it needs to know
they all stem from the same concept. Right. You
have two approaches for this. Stemming. and lemmatization.
Stemming is the older, rougher method. It operates
like a butcher knife, just chopping the ends
off words based on rigid rules, so closing becomes
close. Sometimes it chops off so much you are
left with a non -word. Which isn't ideal. Not
at all. Lemmatization is much smarter. It acts
more like a librarian. It actually references
a comprehensive dictionary to analyze the word's
morphology and return the true base word, which
is called the lemma. It knows that the lemma
of better is good. Oh, wow. Then you have WordSense
This is the classic problem of words having multiple
meanings. If I say book a flight versus read
a book, the machine has to look at the surrounding
context to know if book is functioning as an
action verb or a physical noun. Exactly. And
that leads directly into Named Entity Recognition,
or NER. This is how a system figures out if a
word is a proper name, a location, or an organization.
You might think the monrain could just look for
the capital letters. Which works great in English,
but not globally. Right. In German... Every single
noun is capitalized, whether it's a specific
person's name or just the word for apple. Arabic
doesn't use capitalization at all. And in Spanish
and French, they don't capitalize names when
they are serving as adjectives. Wow. I didn't
even think of that. Yeah. So the machine cannot
rely on punctuation. It has to infer the entity
entirely from the complex mathematical context
surrounding it. And then there's discourse and
corpherence resolution. The sort text gives this
great example. He entered John's house through
the front door. The system has to know that the
front door specifically acts as a bridge to John's
house, not just some random door down the street.
Exactly. What does this all mean? Correference
resolution sounds exactly like trying to keep
track of all the characters and their varying
nicknames in a dense thousand page Russian novel.
That is a perfect way to describe it. Humans
use these bridging relationships effortlessly.
We know the front door belongs to the house that
was just mentioned. But for a machine, every
single pronoun, every he, she, it or thee is
a puzzle piece. It's just a blank. Yeah. It has
to be mathematically resolved against the local
text. It's an intricate web of dependencies.
And once you resolve all those entities and pronouns,
you can do incredibly useful things like sentiment
analysis. That's where you categorize the emotional
tone of text as positive, negative, or neutral.
It is exactly what companies use to automatically
analyze millions of customer reviews to see if
people actually like your new product or hate
it. Right. But understanding all that context
is just half the battle. Once you crack the code
of meaning and context, the machine doesn't just
sit there processing language quietly. You start
generating it at scale. And the evolution of
text generation is just fascinating to look at.
Back in 1984, a rule -based system named Ractor
generated an entire book called The Policeman's
Beard. is half constructed. Catchy title. Right.
But it was entirely nonsensical, just stringing
words together based on those rigid grammar rules
we talked about earlier. Decades later in 2018,
a neural network generated a novel called One
the Road, which was 60 million words long. But
again, it was basically semantics free. It sounded
vaguely like a road trip novel, but it meant
nothing. It lacked a coherent narrative arc.
But by 2019, things shifted dramatically. A machine
generated a science book called Lithium Ion Batteries,
published by Springer. Oh, that was a huge milestone.
Yeah. Unlike the previous examples, this wasn't
hallucinated nonsense. It was grounded in actual
factual text summarization. The machine read
vast amounts of chemistry research and synthesized
it into a coherent, structured book. We are moving
past just parsing sentences into something that
looks a lot like cognition. We are trying to
emulate intelligent comprehension. The source
dives into George Lakoff's theory of conceptual
metaphor, which argues that to truly master language,
a computer must understand intent. For instance,
if I say that is a big tree, I am clearly talking
about physical size. Right. But if I say tomorrow's
a big day, I am talking about importance. But
wait, I have to challenge this. If a computer
doesn't actually have a physical body, it doesn't
experience time, and it has never felt anxiety
about an upcoming event, how can it ever truly
understand the importance of a big day? It's
just lines of code. This raises an important
question, and the answer lies entirely in mathematics.
The algorithm doesn't feel anxiety or importance.
Instead, the source text outlines a formula for
this called the Relative Measure of Meaning,
or RMM. It calculates the intent mathematically
by looking at the mathematical neighborhood of
the words. Okay, how does that mathematical neighborhood
actually work? It takes the probable measure
of meaning based on the massive data sets it
was trained on and multiplies it by a probability
function that scans the specific tokens located
immediately before and after the phrase being
analyzed. So if big is next today, the algorithm
scans the wider sentence and might see words
like wedding, graduation, or nervous. It calculates
that in this specific mathematical neighborhood,
the word big lives much closer to the concept
of important event, rather than physically giant
24 hours. By assigning these relative measures
of meaning, the algorithm uses complex probability
to mathematically approximate human cognitive
intuition. It's just staggering to think about.
We've gone from the naive optimism of those 1950
scientists handwriting rigid algebra rules and
getting crushed by the LPAC report to feeding
mundane Canadian Parliament records into early
statistical models. Quite the leap. And all the
way to massive deep neural networks that turn
words into multidimensional vectors. just to
calculate the mathematical weight of a human
metaphor. It's a testament to the sheer power
of massive data, computational scaling, and the
willingness of the scientific community to abandon
their old assumptions when they hit a wall. I
really want to thank you for joining us on this
deep dive into the source material. We interact
with this invisible machinery constantly. And
I hope the next time your smartphone automatically
filters out a spam email or correctly guesses
the end of your text message, you have a newfound
appreciation for the decades of complex mathematics.
making it happen. It is incredible. But as we
close, I want to leave you with one final thing
to ponder. Ooh, lay it on us. We've seen how
computers have completely abandoned human -made
chromatical rules. They aren't learning syntax
trees. They are now mastering language purely
through statistical patterns hidden in massive,
uninterpretable neural networks. So when that
machine accurately summarizes a medical record
or writes a coherent science book, does it actually
understand us? Or have we simply built a perfect
mathematical mirror, one that brilliantly reflects
our own thoughts back at us without a single
drop of real comprehension.