What is Artificial Intelligence? In 5 minutes.
artificial intelligence for people in a
hurry the easiest way to think about
artificial intelligence is in the
context of a human after all humans are
the most intelligent creatures we know
off AI is a broad branch of computer
science the goal of AI is to create
systems that can function intelligently
and independently humans can speak and
listen to communicate through language
this is the field of speech recognition
much of speech recognition is
statistically based hence it's called
statistical learning humans can write
and read text in a language this is the
field of NLP or natural language
processing humans can see with their
eyes and process what they see this is
the field of computer vision computer
vision falls under the symbolic way for
computers to process information
recently there has been another way
which I'll come to later humans
recognize the scene around them through
their eyes which create images of that
world this field of image processing
which even though is not directly
related to AI is required for computer
vision humans can understand their
environment and move around fluidly this
is the field of robotics humans have the
ability to see patterns such as grouping
of like objects this is the field of
pattern recognition machines are even
better at pattern recognition because
they can use more data and dimensions of
data this is the field of machine
learning now let's talk about the human
brain the human brain is a network of
neurons and we use these to learn things
if we can replicate the structure and
the function of the human brain we might
be able to get cognitive capabilities in
machines this is the field of neural
networks if these networks are more
complex and deeper and we use those to
learn complex thing
that is the field of deep learning there
are different types of deep learning and
machines which are essentially different
techniques to replicate what the human
brain does if we get the network to scan
images from left to right top to bottom
it's a convolution neural network a CNN
is used to recognize objects in a scene
this is how computer vision fits in an
object recognition is accomplished
through AI humans can remember the past
like what you had for dinner last night
well at least most of you we can get a
neural network to remember a limited
past this is a recurrent neural network
as you see there are two ways a eye
works one is symbolic based and another
is data based for the database side
called a machine learning we need to
feed the Machine lots of data before it
can learn for example if you had lots of
data for sales versus advertising spend
you can plot that data to see some kind
of a pattern if the machine can learn
this pattern then it can make
predictions based on what it has learned
while one or two or even three
dimensions is easy for humans to
understand and learn machines can learn
in many more dimensions like even
hundred or thousands
that's why machines can look at lots of
high dimensional data and determine
patterns once it learns these patterns
it can make predictions that humans
can't even come close to we can use all
these machine learning techniques to do
one of two things
classification or prediction as an
example when you use some information
about customers to assign new customers
to a group like young adults then you
are classifying that customer if you use
data to predict if they're likely to
defect to a competitor then you're
making a prediction there is another way
to think about learning algorithms used
for AI if you train an algorithm with
data that also contain
the answer then it's called supervised
learning for example when you train a
machine to recognize your friends by
name you'll need to identify them for
the computer if you train an algorithm
with data where you want the machine to
figure out the patterns then it's
unsupervised learning for example you
might want to feed the data about
celestial objects in the universe and
expect the machine to come up with
patterns in that data by itself if you
give any algorithm a goal and expect the
Machine through trial-and-error to
achieve that goal then it's called
reinforcement learning a robot's attempt
to climb over the wall until it succeeds
is an example of that so there you go
thanks for watching and if you like my
videos please subscribe