Artificial Intelligence
October 09, 2019
What is Artificial Intelligence?
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
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
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's 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 their 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 attempt
to climb over the wall until it succeeds
ByeBye