everybody's talking about artificial
intelligence these days AI machine
learning is another Hot Topic are they
the same thing or are they different and
if so what are those differences and
deep learning is another one that comes
into play I actually did a video on
these three artificial intelligence
machine learning and deep learning and
talked about where they fit and there
were a lot of comments on that and I
read those comments and I'd like to
address some of the most frequently
asked questions so that we clear up some
of the myths and misconceptions around
this in addition something else has
happened since that video was recorded
and that is this the absolute explosion
of this area of generative AI things
like large language models and chat Bots
have seemed to be taking over the world
we see them everywhere really
interesting technology uh and then also
things like deep fakes these are all
within the realm of AI but how do they
fit within each other how are they
related to each other we're going to
take a look at that in this video and
try to explain how all these
Technologies relate and how we can use
them first off a little bit of a
disclaimer I'm going to have to simplify
some of these Concepts in order to not
make this video last for a week so those
of you that are really deep experts in
the field apologies in advance but we're
going to try to make this simple and and
that will involve some generalizations
first of all let's start with AI
artificial intelligence is basically
trying to simulate with a computer
something that would match or exceed
human intelligence what is intelligence
well it could be a lot of different
things but generally we tend to think of
it as the ability to learn to infer and
to reason things like that so that's
what we're trying to do in the broad
field of AI of artificial intelligence
and if we look at a timeline of AI it
really kind of started back around on
this time frame and in those days it was
very premature most people had not even
heard of it uh and uh it basically was a
research project but I can tell you uh
as an undergrad which for me was back
during these times uh we were doing AI
work in fact we would use programming
languages like lisp uh or prologue uh
and these kinds of things uh were kind
of the predecessors to what became later
expert systems and this was a technology
again some of these things existed
previous but that's when it really uh
hit kind of a critical mass and became
more popularized so expert systems of
the 1980s maybe in the 90s and and again
we use Technologies like this all of
this uh was was something that we did
before we ever touched in to the next
topic I'm going to talk about and that's
the area of machine learning machine
learning is as its name implies the
machine is learning I don't have to
program it I give it lots of information
and and it observes things so for
instance if I start doing this if I give
you this and then ask you to predict
what's the next thing that's going to be
there well you might get it you might
not you have very limited training data
to base this on but if I gave you one of
those and then ask you what to predict
would happen next well you're probably
going to say this and then you're going
to say it's this and then you think you
got it all figured out and then you see
one of these and then all of a sudden I
give you one of those and throw you a
curveball so this in fact and then maybe
it it goes on like this so a machine
learning algorithm is really good at
looking at patterns and discovering
patterns within data the more training
data you can give it the more confident
it can be in predicting so predictions
are one of the things that machine
learning is is particularly good at
another thing is spotting outliers like
this and saying oh that doesn't belong
in it looks different than all the other
stuff because the sequence was broken so
that's particularly useful in cyber
security the area that I work in because
we're looking for outliers we're looking
for users who are using the system in
ways that they shouldn't be or ways that
they don't typically do so this
technology machine learning is
particularly useful for us and machine
learning really came along uh and became
more popularized uh in this time frame
uh in the the 2010s uh and again uh back
when I was an undergrad riding my
dinosaur to class we were doing this
kind of stuff we never once talked about
machine learning it might have existed
but it really wasn't hadn't hit the
popular uh mindset yet uh but this
technology has matured greatly over the
last few decades and now it becomes the
basis of a lot we do going forward the
next layer of our Vin diagram involves
deep learning well it's deep learning in
the sense that with deep learning we use
these things called neural networks
neural networks are ways that in a
computer we simulate and mimic the way
the human brain works at least to the
extent that we understand how the brain
works and it's called Deep because we
have multiple layers of those neural
networks and the interesting thing about
these is they will simulate the way a
brain operates but I don't know if
you've noticed but human brains can be a
little bit unpredictable you put certain
things in you don't always get the very
same thing out and deep learning is the
same way in some cases we're not
actually able to fully understand why we
get the results we do uh because there
are so many layers to the neural network
it's a little bit hard to to decompose
and figure out exactly what's in there
but this has become a very important
part and a very important advancement
that also reached some popularity during
the 2010s and as something that we use
still today as the basis for our next
area of AI the most recent advancements
in the field of artificial in
intelligence all really are in this
space the area of generative AI now I'm
going to introduce a term that you may
not be familiar with it's the idea of
foundation models Foundation models is
where we get some of these kinds of
things for instance an example of a
foundation model would be a large
language model which is where we take
language and we model it and we make
predictions in this technology where if
I see certain types of of words then I
can sort of predict what the next set of
words will be I'm going to oversimplify
here for the sake of Simplicity but
think about this as a little bit like
the autoc complete when you start typing
something in and then it predicts what
your next word will be except in this
case with large language models they're
not predicting the next word they're
predicting the next sentence the next
paragraph the next entire document so
there's a really an amazing exponential
leap in what these things are able to do
and we call all of these Technologies
generative because they are generating
new content um some people have actually
made the argument that the generative AI
isn't really generative that that these
Technologies are really just
regurgitating existing information and
putting it in different format well let
me give you an analogy um if you take
music for instance then every note has
already been invented so in a sense
every song is just a recombination some
other permutation of all the notes that
already exist already and just putting
them in a different order well we don't
say new new music doesn't exist people
are still composing and creating new
songs from the existing information I'm
going to say geni is similar it's a it's
an analogy so there'll be some
imperfections in it but you get the
general idea actually new content can be
generated out of these and there are a
lot of different forms that this can
take with other types of models are uh
Audio models
uh video models and things like that
well in fact these we can use to create
deep fakes and deep fakes are examples
where we're able to take for instance a
person's voice and recreate that and
then have it seem like the person said
things they never said well it's really
useful in entertainment situations uh in
parities and things like that uh or if
someone's losing their voice then you
could capture their voice and then
they'd be able to type and you'd be able
to hear it in their voice but there's
also a lot of cases where this stuff
could be abused um the chat Bots again
come from this space the Deep fakes come
from this space but they're all part of
generative Ai and all part of these
Foundation models and this again is the
area that has really caused all of us to
really pay attention to AI the
possibilities of generating new content
or in some cases summarizing existing
content and giving us uh something that
is bite-size and manageable this is what
has gotten all of the attention this is
where the chat Bots and all of these
things come in in the early days ai's
adoption started off pretty slowly most
people didn't even know it existed and
if they did it was something that always
seemed like it was about 5 to 10 years
away but then machine learning deep
learning and things like that came along
and we started seeing some uptake then
Foundation models gen Ai and the light
came along and this stuff went straight
to the Moon
these Foundation models are what have
changed the adoption curve and now you
see AI being adopted everywhere and the
thing for us to understand is where this
is where it fits in and make sure that
we can reap the benefits from all of
this
technology if you like this video and
want to see more like it please like And
subscribe if you have any questions or
want to share your thoughts about this
topic please leave a comment below