Nlp is not a new field it has been there
in academia for many years.
In last 7-10 years we have
started seeing real industry use cases
of NLP. In this video I'm going to talk
about 5 reasons why NLP is booming
right now.
Training machine learning model for NLP
problem takes humongous amount of data
and a lot of computer resources. If there
is a small company they sometimes don't
afford it and they don't also have data.
In last few years what happened is big
companies like Google, Facebook, Amazon
they started training
big
models on
on the datasets that they have
available. These companies have a lot of
our datasets uh available with them and
also they have computer resources and
budget.
As a result
we are seeing some APIs and some
pre-trained models coming out such as
fast tech Fasttext which is
Facebook's API and pre-trained models,
then Tensorflow Hub and GPT3 and I'm
going to quickly show you all of those.
So here I am on the Tensorflow website,
Tensorflow hub specifically and if you
look at the models,
you will see different models related to
text problems which is NLP and if you
click on that you will see models like
BERT for example. Universal center
encoder. There are so many pre-trained
models
and these are the models
Google train on the humongous dataset
that they had and they also
spent all this compute resources in
training those models and now they're
giving it for free. They just prepare a
recipe and say hey let
just use it,
and these models you can now use uh with
a technique called transfer learning and
you can take the model and for the given
problem that you're facing, you can fine
tune the model. So
you're getting a lot of goodies for free
from these big companies.
Second one is fast text which is from
Facebook and if you click on download
models you will see so many models here.
Like like see Wiki word vectors, these
are like
embeddings word embeddings. We will look
at these in details in the future. But
the point I want to make is a lot of the
ingredients that you need for your NLP
program, all these ingredients are
offered to you for free by these big
companies. There is GPT3 now GPT3 is the
largest neural network ever. It is a
language model,
it is created by this company called
OpenAI. They spend 4 million dollar
just training this model
and now they are giving it for free. If
you want to train similar model, you have
to spend 4 million dollars. You're
getting this 4 million dollar kind of
gift for free almost.
So because of
all these companies giving away their
free models, now small organizations can
also afford
onboarding NLP to solve various problems.
The second reason is open source
ecosystem previously many years back you
have to write all the code in C++
yourself. But nowadays
with Python, you write 4 line of code
and your NLP program is ready. So it's
very easy. Students, people working in
academia small companies they can just
quickly develop uh the pro programs that
solves the NLP problems very easily.
There are libraries such as spaCy, Gensim,
NLTK. We talked about all these
libraries in the first video. These
libraries
are freely available, they are very
powerful the whole open source community
is backing them. They are building new
features day by day and that is ramping
up the NLP
on boarding throughout the industry. The
third one is cheap hardware and cloud
resources.
Previously like 10 years back or 15
years back, if you want to train a model
you have to get a heavy Nvidia GPU or
big machine and it will be very very
costly.
Nowadays all these hardwares are cheaper
and most of the time people are using
renting this hardware on cloud, they
don't want to buy it. So
AWS, Azure, Google Cloud you just
rent these hardware resources, train your
model and you're done. Previously you
know many years back there was no cloud,
so you have to pretty much
invest and buy this hardware yourself.
The fourth one is learning resources you
are learning NLP by watching these
videos on YouTube for free. When I was in
college
the only way to learn something is to go
to
one college that was near my home
and that's it. There was no internet. So
now for you
you can go to YouTube, you can attend
many bootcamps, you know 6-month
bootcamp let's say data science bootcamp,
you can fast learn things and just train
yourself and start working as a data
scientist.
Udemy, Coursera so many
e-learning websites you can buy cheaper
courses.
So many
articles like towards data science, so
learning has become very easy. You have
Discord channels, you have Kaggle
competitions, you have various zoom calls
where you can clarify your question. So
connecting with experts in the world has
become very very easy because of
easy availability of internet, things
like zoom, communities like Kaggle, Stack
Overflow,
and the whole ecosystem
supports fast learning.
So there is a person who doesn't know
NLP, they can refer to
all these resources. You know you watch
my
YouTube playlist on NLP and you kind of
you learn the basics and then you start
working on projects.
The fifth reason is huge investments
made by big tech.
Again Google, Meta, Amazon these companies
have made huge investment
not only in publishing these big
libraries like Tensorflow and Pytorch.
They are also building hardware. For
example Google has built Google home,
Amazon has built Amazon
Amazon echo dot and all these devices,
they are voice conversion assistance. So
they are using NLP
So
once this big tech started investing big
amount of money in NLP and this build
this ecosystem,
the tier 2 and tier 3 organization you
know they always follow the lead,
whatever the big tech companies are
doing or rest of the world will try to
do that because otherwise there will be
a FOMO fear of missing out. So now
small scale organization they want to
onboard
NLP, they want to on board AI
because they are saying that this big
tech companies are ripping big benefits,
they are earning a lot of money by
investing in this futuristic
technologies. And that's another reason
NLP is ramping up fast. So I hope you
like this video. If you did please give
it a thumbs up. So these are the five
reasons that I can think about why NLP
is booming faster and we will see more
and more advancements in coming years.