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Get ready to demystify Natural Language Processing! Discover how this cutting-edge technology is revolutionizing the way we interact with the world.
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[MUSIC]
0:00
Welcome to the world of AI,
0:09
get ready to peek behind the magic
curtain powering your favorite apps and
0:10
gadgets through this introduction to
natural language processing or NLP.
0:13
Have you ever wondered how Siri knows the
fastest route to the movies when you ask
0:18
for directions or how Netflix seems
to know exactly which shows you'll
0:22
binge watch next, the secret is NLP.
0:26
It's the technology that
allows these apps and
0:29
services to understand human language,
whether typed or spoken,
0:32
without NLP you'd have to speak
to your devices like a robot.
0:36
Let's take Netflix as an example,
0:40
when you watch a Korean thriller one
weekend then a sci-fi series the next,
0:42
Netflix uses NLP to analyze your viewing
history and make smart recommendations.
0:46
It's like your own personal TV guide who
knows your tastes better than you do, or
0:51
think about autocorrect on your phone.
0:55
We all know how annoying it is when it
replaces a correctly spelled word with
0:58
something totally random.
1:02
But thanks to NLP, phones can now
understand slang, typos and abbreviations
1:04
to fix your texting mistakes,
no more ducking auto-carrot fails.
1:08
Ready to uncover more
real world NLP magic?
1:14
Let's dive in and demystify how this game
changing technology understands human
1:17
language to power the apps and
services we use every day.
1:21
First things first,
what is natural language processing,
1:25
natural language processing
abbreviated as NLP is a subset of
1:28
artificial intelligence that intersects
with computer science and linguistics.
1:31
Its primary objective is to enable
computers to understand, interpret,
1:36
generate, and respond to human language
in a valuable and meaningful way.
1:40
This understanding could range from simple
tasks such as identifying the language
1:44
of the text to complex ones
like understanding sentiments,
1:48
translating languages, and
even engaging in human like conversations.
1:51
NLP encompasses a variety of
techniques and methods to analyze and
1:55
represent natural language at
different levels of abstraction,
1:59
from morphological and syntactic analysis
to semantic and discourse analysis.
2:03
By processing and analyzing large
amounts of natural language data,
2:08
NLP aims to extract information and
knowledge, or derive patterns and
2:12
insights in a way that is similar
to how humans understand language.
2:16
The ultimate goal of NLP
is to design algorithms and
2:20
build systems that allow computers to
perform natural language related tasks,
2:23
thereby bridging the communication
gap between humans and machines.
2:27
Through the advancements in NLP, machines
can now assist in performing a plethora of
2:31
tasks including but
not limited to automated customer service,
2:36
sentiment analysis, language translation,
and content recommendation.
2:40
Now that we've explored what natural
language processing is and its impressive
2:44
capabilities in today's world, let's step
back in time to see where it all began.
2:49
The journey of NLP is not just a tale
of technological advancement, but
2:54
also a story of human curiosity and
ingenuity.
2:58
From its earliest days of simple text
translation to today's sophisticated
3:01
chatbots and complex language models,
3:06
the evolution of NLP is as
captivating as its current state.
3:08
So let's embark on a historical adventure
to uncover the roots of NLP and
3:12
trace its path through the decades.
3:16
Imagine if the conversations
we have today with Siri or
3:18
Alexa were happening back when rock and
roll first hit the radio waves,
3:21
that's how far back the story of
natural language processing starts.
3:24
In the 1950s, the first steps towards
machines understanding human language were
3:28
taken with the Georgetown-IBM experiment,
3:33
which made headlines for translating
sentences from Russian to English.
3:36
Around the same time the famous
Alan Turing proposed a test,
3:40
now known as the Turing Test to see if a
machine could be considered intelligent by
3:43
having conversations
indistinguishable from humans.
3:48
As the 1960s rolled in
linguist Noam Chomsky's
3:51
ideas helped shape how computers
dealt with human language,
3:54
even though the actual language turned
out to be quite a puzzle for machines.
3:57
Yet we saw programs like Eliza
in the 1960s that could mimic
4:01
a therapist in a conversation showing
a glimpse of what was possible.
4:04
The following decades were all about
building rules for computers to understand
4:08
language and then teaching them
to learn these rules themselves.
4:12
By the 1980s with machine learning coming
into play computers started getting better
4:16
at understanding spoken words and
translating languages.
4:20
The 1990s refine these methods with
computers getting better at figuring out
4:24
the role of each word in a sentence.
4:28
But it was the internet
explosion in the 2000s,
4:30
that really gave NLP a playground of data
to learn from, leading to tools that could
4:33
tell if a movie review was positive or
dig out information from heaps of text.
4:37
The 2010s were a game changer with the
advent of deep learning, which allowed for
4:42
even more advanced understanding and
generation of language by machines.
4:46
Models like BERT and
4:51
GPT showed us that computers could get
a lot better at handling language.
4:52
Today, in the 2020s, NLP is not just
about technology it's also about thinking
4:56
through the responsibilities
that come with it.
5:00
Thanks to large language models,
chatbots like ChatGPT by OpenAI, Clawed
5:03
by Anthropic, and Barred by Google are
helping improve how we talk to machines.
5:07
They're bringing us closer to a time when
chatting with a computer will be as easy
5:12
as chatting with a friend.
5:16
Did you enjoy learning
about the history of NLP?
5:18
Join me in the next video where I will
break down the building blocks that make
5:21
up natural language processing,
I'll see you there.
5:25
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