Generative AI, like other general-purpose technologies such as electricity or the internet, is highly versatile and applicable to a wide range of tasks, making it difficult to define by a single use case. Its capabilities primarily fall into three categories: writing, reading, and chatting.
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what is generative AI good for one of
the reasons that question is a bit hard
to answer is because AI is a general
purpose technology unlike a lot of
Technologies like a car which you know
good for transportation or microwave
oven good for heating up food AI isn't
useful just for one thing it's useful
for a lot of things and that almost
makes it harder to talk about but let's
take a look at what a general purpose
technology really means similar to
Electric AI is useful for many tasks if
would ask you what is electricity good
for or what is the internet good for
these are other general purpose
Technologies and it's almost difficult
to think what is electricity good for
because it's so pervasive and it's used
around us for so many different things
in fact as you saw earlier supervised
learning is useful for many tasks like
span filtering advertising speech
recognition and many others and
generative AI is like this too in the
last video you saw a few of the tasks
that an LM can carry out answering
certain questions and helping with
writing for example let's discuss more
broadly a framework for what kinds of
tasks LS can do first off gbi generates
text so not surprisingly perhaps is
useful for writing I routinely use gent
AI tools as a brainstorming companion so
if you're trying to name a product you
can ask it to brainstorm some names and
it comes up with some creative
suggestions L can also be good at
answering questions and if you give them
access to information specific to your
company they can help members of your
team find information that they need in
this case about the availability of
parking at the office in addition to
writing gent of AI is also good for what
I'm going to call reading toss where
you're going to give it a relatively
long piece of information and have it
generate a short output for example if
you run an online shopping e-commerce
company and you get a lot of different
customer emails jent of AI can read the
customer emails and help you very
quickly figure out is this email a
complaint or not which can be used for
helping to Route complaints to the
appropriate Department to be handled
quickly so given I love my new llama
t-shirt fabric is so soft that's not a
complaint but if someone emails I wore
my llama t-shirt to friend's wedding now
they're mad at me for stealing the show
well maybe that is a complaint but J of
AI can help you route emails to the
right department and I call this a
reading toss because it's looking at the
relatively long piece of text that is a
customer email and then generating a
relatively short output just yes or no
is this a complaint or not and while
supervised learning can also be used for
this particular task we'll see later
that General of AI is allowing these
sorts of reading tasks this and other
examples that we'll see later this week
to be built much more quickly and
inexpensively lastly genive AI is also
used for many chatbot types of tasks
whereas chat gbd and B and B chat are
general purpose chat Bots gen of AI
technology large language models is also
enabling many special purpose chat Bots
to be built in this example here's what
a chatbot might be like for taking
online orders where a user can say a
like a cheeseburger for delivery and the
chatbot acknowledges and puts the order
through for the user now in talking
through these tasks I find that there
sometimes useful to distinguish between
two different types of om based
applications one is examples like this
brainstorming one where it be quite
natural for you to type a prompt like
this into to CH or B or B chat or one of
the other free or paid large language
models on the internet and get a result
back so I'm going to call an application
like this a web interface based
application in contrast in the example
of recognizing of an email is a customer
complaint this fits more into a
company's email routing workflow and it
doesn't really make sense for anyone to
cut and paste customer emails one at a
time into a web interface to get back
answers as to which ones are actually
complaint emails so this is an example
of an LM that would make sense when it's
built into a larger software automation
that in this case helps with a company's
automated email routing so I'm going to
call this a LM based software
application the second writing example
of answering HR questions it turns out
this also will make more sense as a
software based LM application because
it'll need access to information about
your specific company's parking policy
for employees whereas a general lar
langage model on the internet probibly
doesn't have that information we'll talk
more later in this course about how this
technology is built and most of the
specialized chat Bots will also be
software based L applications so in the
rest of this course I'm going to use
these two symbols to distinguish between
web interface use cases and software
based um applications and for many
people it may be easier to get started
with some of the web interface use cases
because you just go to a website like
trbd or bar or Bing and type in a prompt
and get result back but I think both the
web interface based applications and the
software based applications are
important and will be very useful for
for individuals and for companies I
founded the framework of writing reading
and chatting as a useful way to think
about the many different tasks that LM
large language model can do in the next
three videos we'll dive more deeply into
many different examples of writing
reading and chatting TSS and I hope that
you find some of them potentially useful
for your own work so I look forward to
seeing you in the next video where we'll
talk more about writing and then I look
forward to enjoy my burger
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