Generative AI, particularly Large Language Models (LLMs), works by repeatedly predicting the next word in a sequence, building upon the foundation of large-scale supervised learning. This technology enables AI to generate human-like text and has numerous practical applications.
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the ability of systems like chat GPT and
bod to generate text seems almost
magical and they do represent a big step
forward for AI technology but how does
text generation actually work in this
video we'll take a look at what actually
underlies the generative AI technology
and this will hopefully help you
understand what you can use it for and
also when you might not want to count on
it let's take a
look let's start by looking at where
generative AI fits within the AI
landscape there's a lot of Buzz and
excitement and also hype about Ai and I
think a useful way to think of AI is as
a collection or as a set of tools one of
the most important Tools in AI is
supervised learning which turns out to
be really good at labeling things don't
worry if you don't know what this means
we'll talk more about it on the next
slide and a second to that started to
work really well only fairly recently is
generative AI if you study AI you may
recognize that there are other tools as
well such as things called UNS
supervised learning and reinforcement
learning but for the purposes of this
course I'm going to touch briefly on
what is supervised learning and then
spend most of our time talk about
generative Ai and these two supervised
learning and generative AI are the two
most important Tools in AI today and for
most business use cases you should be
fine if you just not worry about the
other tools than these for now before
describing how generative AI works let
me briefly describe what is supervised
learning because it turns out gen of AI
is built using supervised learning
supervised learning is a technology that
is made computes is very good when given
an input which I'm going to call a to
generate a corresponding output which
I'm going to call B so look at a few
examples you given an email supervised
learning can decide if that email is
Spam or not so the input a is an email
and the output B is either zero or one
where zero is not spam and one is Spam
and this is how spam filters work today
as a second example probably the most
lucrative application not the most
inspiring but lucrative for some
companies the that work on was online
advertising where given an ad and some
information about a user an AI system
can gener an output B corresponding to
whether or not you're likely to click on
that ad and by showing slightly more
relevant ads this drives significant
revenue for the online ad Platforms in
self-driving calls and in driver
assistance systems supervised learning
is used to take us input a picture of
what's in front of your car and radar
info and label that with the position of
other cars given a medical x-ray it can
try to label that with a medical
diagnosis I've also done a lot of work
in manufacturing defect inspection where
you can have a system take a picture of
a phone as it rolls off the assembly
line and check if the phone has any
scratches or the defects or in speech
recognition the input a would be a piece
of audio and we would label that with a
text transcript or as a final example if
you run a restaurant or some of the
business where occasionally you have
reviews written about your business or
your products supervised learning can
read those reviews and label each one as
having either a positive or A negative
sentiment and this is useful for
reputation monitoring of the business so
it turns out the decade of around 2010
to 2020 was a decade of large scale
supervised learning and I want to touch
on this briefly because it turns out
this laid the foundation for modern
generative AI but what we found starting
around 2010 was that for a lot of
applications we had a lot of data but
even as we FedEd more data his
performance wasn't getting that much
better if we were training small AI
models this means for example if you are
building a speech recognition system
even as your AI listen to tens of
thousands or hundreds of thousands of
hours of data that's a lot of data it
didn't get that much more accurate
compared to a system that listen to only
a smaller amount of audio data but what
more and more researchers started to
realize through this period is if you
were to train a very large AI model
meaning an AI model on very fast very
powerful computers with a lot of memory
then performance as you FedEd more and
more data would just keep on getting
better and better in fact years ago when
I started and led the Google brain team
the primary mission that I set for the
Google brain team in the early days was
I said let's just build really really
large AI models and feed them a lot of
data and fortunately that recipe worked
and ended up driving a lot of AI
progress at Google large scale
supervised learning remains important
today but this idea of very large models
for labeling things is how we got to
generative AI today let's look at how
Gena of AI generates text using a
technology called large language models
here's one way that large language
models which are abbreviate l m can
generate text given an input like I love
eating this is called a prompt and LM
can then complete this sentence with
maybe bagels with cream cheese or if you
run it a second time it might say my
mother's meat low or if you run it the
third time maybe it'll say also with
friends so how does an LM a large
language model generate this output it
turns out that lm's a build by using
supervised learning that's a technology
to input a and output a label B it uses
supervised learning to repeatedly
predict what is the next word for
example if an AI system has read on the
internet a sentence like my favorite
food is a bagel with cream cheese then
this one sentence will be turned into a
lot of data points for it to try to
learn to predict the next word specially
given this sentence we now have one data
point that says given the phrase my
favorite food is a what do you think is
the next word in this case the right
answer is bagel and also given my
favorite food is a bagel what do you
think is the next word is with and so
on so this one sentence is turned into
multiple inputs a and outputs B for it
to try to learn from where the LM is
learning given a few words to predict
what the next word that comes out there
when you train a very large AI system on
a lot of data a lot of data for LS means
hundreds of billions of words and in
some cases more than a trillion words
then you get a large language model like
chat GPT that given a prompt is very
good at generating some additional words
in response to that prompt but now I'm
omitting some technical details
specifically next week what talk about a
process that makes LMS not just predict
the next word but actually learn to
follow instructions and also be safe in
what it outputs but at the heart of LMS
is this technology that's learned from a
lot of data to predict what is the next
word so that's how large language models
work they're trained to repeatedly
predict the next word and it turns out
that many people perhaps including you
are already finding these models useful
for day today activities at work to help
with writing to find basic information
or to be a thought partner to help think
things through let's take a look at some
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