AI, Machine Learning, Deep Learning Explained
Key Points
- Artificial Intelligence (AI) aims to make computers behave like humans, while Machine Learning (ML) adds the ability for computers to learn from data and make predictions through processes like supervised learning.
- Deep Learning (DL) goes a step further by feeding raw data into models that automatically discover patterns and relationships without needing explicit feature engineering.
- Neural networks, the core of DL, mimic biological neurons by connecting inputs, hidden layers, and outputs, allowing complex information exchange similar to brain synapses.
- As AI progresses rapidly, understanding these hierarchical relationships and the way neural networks learn is essential for addressing emerging concerns and ethical considerations.
Full Transcript
# AI, Machine Learning, Deep Learning Explained **Source:** [https://www.youtube.com/watch?v=NMZ0Tgc2jFQ](https://www.youtube.com/watch?v=NMZ0Tgc2jFQ) **Duration:** 00:09:22 ## Summary - Artificial Intelligence (AI) aims to make computers behave like humans, while Machine Learning (ML) adds the ability for computers to learn from data and make predictions through processes like supervised learning. - Deep Learning (DL) goes a step further by feeding raw data into models that automatically discover patterns and relationships without needing explicit feature engineering. - Neural networks, the core of DL, mimic biological neurons by connecting inputs, hidden layers, and outputs, allowing complex information exchange similar to brain synapses. - As AI progresses rapidly, understanding these hierarchical relationships and the way neural networks learn is essential for addressing emerging concerns and ethical considerations. ## Sections - [00:00:00](https://www.youtube.com/watch?v=NMZ0Tgc2jFQ&t=0s) **Hierarchical Relationship of AI Technologies** - The speaker outlines how artificial intelligence encompasses machine learning, which in turn includes deep learning and neural networks, explaining each level's data‑driven learning process. ## Full Transcript
is AI to machine learning like deep
learning is to neural networks H that's
a very good question but before we dive
into the details on deep learning and
neural networks I kind of think we
should really level set on the real
relationship as we start down this
hierarchy so let's get started
artificial intelligence just to
summarize with just plainly what it kind
of means we want computers
to behave like
humans a grand old stick figure there
all right so that's what artificial
artificial intelligence is really all
about there we can take it a step
further here is with machine learning we
want to provide some data those ones and
zeros and we would like the machine
to have a light bulb here moment
of some conclusion that we wanted to to
come to I feed you data you understand
that data and give me a concept and
through techniques like supervised
learning we always have to kind of tune
this data to say okay you're getting
close let me give you another set of
data so you can learn and then I want
you to predict the answer that I know I
want you to see this process usually
involves you constantly tuning and
working with the data well let's take it
a step step lower when we get into deep
learning which is uh uh where we still
want to provide the data just raw data
but instead these are methods that the
computer or models whatever you may say
will come up with their own conclusions
all right they will go ahead and learn
on their own we just keep providing data
and through different types of
techniques they'll be able to actually
uncover relationships and another
question came to mind what are all the
concerns about with AI and it learning
fast and growing so fast let's kind of
take a look at that particular situation
here here I have two neurons that you
have in the brain if we're familiar with
let's take you all the way back to high
school biology where we all learn that
um cells have synapses that communicate
with each other and they fire off
synapses impulses that really allow them
to communicate well that how our brain
works and if you remember from AI our
one artificial intelligence the one
principle that we do have here is to
make machines function like humans well
that's where neural networks come into
play they actually simulate the way that
the brain works by doing a neuron
Network so I'm simulating this here on
how we may have
inputs and we'll have outputs which is
the act ual result you're looking for
and in between we can have any number of
what we like to call Hidden
layers all right now generally in a lot
of these you'll have all the nodes are
connected to each other just like we
have in the normal synapses of the brain
they all are aware of each
other and can communicate as
well and this can come in various forms
depending on the actual context of the
problem that you're trying to do all
right if you wanted to have an output
where you just need a probability score
that output may just be one particular
node or if you have a multiple
classification that you need to do it
can be multiple nodes as you see as I've
I've envisioned here and this is just a
a form of a simple Network that you can
do but we're talk more about now how
these are all you architect these and
you resemble these through through your
sdks through programming you are
actually simulating this particular
diagram at the bottom here and you feed
it data and data flows through and you
get your resulting answer this is an
actual implementation of what a neural
network really looks like here so let's
talk about some of the simple neuron
networks that you can do so now that you
know the foundation of the definition of
this you when you want to engage in in
deploying uh uh a deep learning through
neural networks you really have to
understand what type do I want to do
what's required all right so on this
side we'll talk about three different
types of simple newal networks that come
up very often the first the feed forward
Network this is where you have inputs
that go to outputs it goes completely
through the actual uh uh Network there
and through continuously going through
there is a certain feedback where it
kind of starts to learn how things works
next back propagation algorithm BPA all
right each layer each node is going to
be connected to each hidden layer um and
everyone can communicate with each other
here and by going through and
learning going through the actual data
and making decisions it's going to start
to learn which
path is correct and it's going to assign
we could call it
weights to which one was correct and so
as you go through a second iteration it
knows I'm going to keep trying this path
and it'll keep trying different notes to
really get back to where it has a
correct decision that it needs to make
the third convolutional neuron Network
this is more on the side where you do a
lot of
classification type of decisions that
you want to make let's take for instance
image processing all right um uh one of
the these neural networks may have more
hidden layers and of course depending on
what you're trying to do you can assign
more input nodes more hidden layer nodes
that you want in there and more hidden
layers so think about CNN which would be
something like as I'm processing an
image I may have one hidden layer for
the colors one for the edges one for
different aspects of the photo until I
come out to the output of yes this is
this this probability is that or
whatever classification that I kind of
want so taking that in consideration
you're moving on from machine learning
you get into neural networks you decide
what you're going to deploy for your
particular problem what are some of the
common use cases here and what I
actually found is it is more common than
you think some of our outline four use
cases here which we work with and are
very aware of every day the first
is we'll call this one computer Vision
as you said being able to identify uh
images text documentations uh um that's
all it it being able to see and identify
a particular piece of content here very
critical here now we've all uh been at
home and we all use certain smart
devices to maybe ask different questions
all right and that's where speech
recognition the ability for you to say
words and it to interpret it into the
right context which leads me to my third
use case here natural language
processing and I think we're all are
aware where we'll have um an
actual uh a
computer being able to
understand speech okay being able to
interpret uh translate to different
language
um you ask a question and it understands
the right context all right that comes
through these big neural networks of
being able to actually learn uh through
and we've all done uh shopping on
e-commerce at any given day and I think
you are aware on what this can be
recommendation layers all right
recommendation engines all right based
upon the data from you buying purchasing
and what others have bought uh it can
offer you recommendations on what you
may like or if you're in your favorite
streaming service all right you've
watched this type of movie this type of
genre I can give you recommendations on
what I think to keep you engaged again
uh from there so now we learn these
popular Concepts from artificial
intelligence to machine learning to deep
learning which we know a form of that is
through expressed through uh neural
networks here so as you you pick your
next architecture and want to actually
get involved this information you can
use to kind of make decisions on how you
want to gain insights from Deep
learning