🔥Artificial Intelligence Tutorial AI Tutorial for Beginners 2023 AI Simplilearn

picture this a machine that could
organize your cupboard just as you like
it or serve every member of the House a
customized cup of coffee makes your day
easier doesn't it these are the products
of artificial intelligence but why use
the term artificial intelligence well
these machines are artificially
Incorporated with human-like
intelligence to perform tasks as we do
this intelligence is built using complex
algorithms and mathematical functions
but AI may not be as obvious as in the
previous examples in fact AI is used in
smartphones cars social media feeds
video games banking surveillance and
many other aspects of our daily life the
real question is what does an AI do at
its core here is a robot we built in our
lab which is now dropped onto a field in
spite of a variation in lighting
landscape and dimensions of the field
the AI robot must perform as expected
this ability to react appropriately to a
new situation is called generalized
learning the robot is now at a crossroad
one that is paved and the other Rocky
the robot must determine which path to
take based on the circumstances this
portrays the robot's reasoning ability
after a short stroll the robot now
encounters a stream that it cannot swim
across using the plank provided as an
input the robot is able to cross this
stream so our robot uses the given input
and finds the solution for a problem
this is problem solving these three
capabilities make the robot artificially
intelligent in short AI provides
machines with the capability to adapt
reason and provide Solutions
well now that we know what AI is let's
have a look at the two broad categories
in AIS classified into
weak AI also called narrow AI focuses
solely on one task
for example alphago is a maestro of the
game go but you can't expect it to be
even remotely good at chess
this makes alphago a weak AI
you might say Alexa is definitely not a
weak AI since it can perform multiple
tasks well that's not really true when
you ask Alexa to play despacito it picks
up the keywords play and despacito and
runs a program and is trained to Alexa
cannot respond to a question it isn't
trained to answer for instance try
asking Alexa the status of traffic from
work to home
Alexa cannot provide you this
information as she is not trained to and
that brings us to our second category of
AI strong AI
now this is much like the robots that
only exist in fiction as of now Ultron
from Avengers is an ideal example of a
strong AI
that's because it's self-aware and
eventually even develops emotions this
makes the ai's response unpredictable
You Must Be Wondering well how is
artificial intelligence different from
machine learning and deep learning we
saw what AI is machine learning is a
technique to achieve Ai and deep
learning in turn is a subset of machine
learning machine learning provides a
machine with the capability to learn
from data and experience through
algorithms deep learning does this
learning through ways inspired by the
human brain this means through deep
learning data and patterns can be better
perceived Ray Kurzweil a well-known
futurist predicts that by the year 2045
we would have robots as smart as humans
this is called the point of Singularity
well that's not all in fact Elon Musk
predicts that the human mind and body
will be enhanced by AI implants which
would make us partly cyborgs so here's a
question for you which of the below AI
projects don't exist yet a an AI robot
with citizenship B a robot with a
muscular skeletal system C AI that can
read its owner's emotions D AI that
develops emotions over time give it a
thought and leave your answers in the
comments section below since the human
brain is still a mystery it's no
surprise that ai2 has a lot of
unventured domains for now ai is built
to work with humans and make our tasks
easier
however with the maturation of
Technology we can only wait and watch
what the future of AI holds for us by
the end of this lesson you will be able
to Define artificial intelligence
describe the relationship between
artificial intelligence and to data
science
Define machine learning
describe the relationship between
machine learning artificial intelligence
and data science
describe different machine learning
approaches identify the applications of
machine learning
let's understand how the field of
artificial intelligence emerged
let's first understand the reason behind
the emergence of AI data economy is one
of the factors behind the emergence of
AI it refers to how much data has grown
over the past few years and how much
more it can grow in the coming years
when you look at this graph you can
clearly understand how the volume of
data has grown you can see that since
2009 the data volume has increased by 44
times with the help of social websites
the explosion of data has given rise to
a new economy and there is a constant
battle for ownership of data between
companies to derive benefits from it
now that you know that data has grown at
a rapid Pace in the past few years and
is going to continue to grow let's
understand the need for AI as you know
the increase in data volume has given
rise to Big Data which helps manage huge
amounts of data data science helps
analyze that data so the science
associated with data is going toward a
new paradigm where one can teach
machines to learn from data and drive a
variety of useful insights giving rise
to artificial intelligence
now you may ask what is artificial
intelligence
artificial intelligence refers to the
intelligence displayed by machines that
simulates human and animal intelligence
it involves intelligence agents the
autonomous entities that perceive their
environment and take actions that
maximize their chances of success at a
given goal artificial intelligence is a
technique that enables computers to
mimic human intelligence using logic it
is a program that can sense reason and
act
let's look at some of the areas where
artificial intelligence is used
artificial intelligence is redefining
industries by providing greater
personalization to users and automating
processes one example of artificial
intelligence in practice is self-driving
cars
self-driving cars are
computer-controlled cars that drive
themselves in these cars human drivers
are never required to take control to
safely operate the vehicle these cars
are also known as autonomous or
driverless cars
let's see how Apple uses AI iPhone users
can experience the power of Siri the
voice
it simplifies navigating through your
iPhone as it listens to your voice
commands to perform tasks for instance
you can ask Siri to call your friend or
to play music Siri is fun and is
extremely convenient to use
another example is Google's alphago
which is a computer program that plays
the board game go
it is the first computer program to
defeat a world champion at the ancient
Chinese game of Go
Amazon Echo is another product it's a
home controlled chatbot device that
responds to humans according to what
they are saying it responds by playing
music movies and more
if you've got compatible smart home
devices you can tell Echo to dim the
lights or turn appliances on or off you
can use Ai and chess and here is an
example of a concierge robot from IBM
called IBM Watson the IBM Watson AI has
typically been in the headlines for
composing music playing chess and even
cooking food
let's move ahead and look at some Sci-Fi
movies with a concept of artificial
intelligence
the films featuring AI reflect the
ever-changing spectrum of our emotions
regarding the machines we have created
humans are fascinated by the concept of
artificial intelligence and this is
reflected in the wide range of movies on
AI
recommendations systems are used by a
lot of e-commerce companies let's see
how they work
Amazon collects data from users and
recommends the best product according to
the user's buying or shopping pattern
for example when you search for a
specific product in the Amazon store and
add it to your cart Amazon recommends
some relevant products based on your
past shopping and searching pattern
so before you buy the selected product
you get recommendations based on your
interest and there is a possibility that
you may also buy the relevant product
with a selected product if not you have
the chance to compare the selected
product with a recommended products
now let's move ahead and understand the
relationship between artificial
intelligence machine learning and data
science
even though the terms artificial
intelligence AI machine learning and
data science fall in the same domain and
are connected to each other they have
their specific applications and meaning
let's try to understand a little about
each of these terms artificial
intelligence systems mimic or replicate
human intelligence machine learning
provides systems the ability to
automatically learn and improve from the
experiences without being explicitly
programmed data science is an umbrella
term that encompasses data analytics
data mining machine learning artificial
intelligence and several other related
disciplines let's look at the flow
diagram and try to understand the
relationship between AI machine learning
and data science
interestingly ml is also an element of
artificial intelligence
so the first step is data Gathering and
data transformation this step basically
comes under data science data
transformation is the process of
converting data from one format or
structure into another format or
structure data transformation is
important to activities such as data
management and data integration
after Gathering data we would want to
use the data to make predictions and
derive insights in order to get
predictions out of the data set we use
machine learning techniques such as
supervised learning or unsupervised
learning on an overview level supervised
and unsupervised learning are the
machine learning techniques used to
extract predictions from a given data
set
now you must be thinking where deep
learning comes into the picture deep
learning is a subfield of machine
learning involved with algorithms
it uses artificial neural networks which
are modeled on the structure and
performance of neurons in the human
brain
deep learning is most effective when
there isn't a clear structure to the
data that you can just exploit and build
features around now the next step in the
flow diagram is to get insights from
predictions being made in order to do so
you need to use data analysis which
actually is the process under data
science
now when you are done with all of these
you must want your data to perform some
actions this is where AI comes into the
picture artificial intelligence combines
predictions and insights to perform
actions based on the human decision and
automated decision
now let's move ahead and understand the
relationship between artificial
intelligence machine learning and data
science
let's look at the relationship between
artificial intelligence and machine
learning artificial intelligence is the
engineering of making intelligent
machines and programs machine learning
provides systems the ability to learn
from past experiences without being
explicitly programmed machine learning
allows machines to gain intelligence
thereby enabling artificial intelligence
let's Now understand the relationship
between machine learning and data
science data science and machine
learning go hand in hand data science
helps evaluate data for machine learning
algorithms data science covers the whole
spectrum of data processing while
machine learning has the algorithmic or
statistical aspects
data science is the use of statistical
methods to find patterns in the data
statistical machine learning uses the
same techniques as data science data
science includes various techniques like
statistical modeling visualization and
pattern recognition machine learning
focuses on developing algorithms from
the data provided by making predictions
so what is machine learning
machine learning is the capability of an
artificial intelligence system to learn
by extracting patterns from data it
usually delivers quicker more accurate
results to help you spot profitable
opportunities or dangerous risks
now you must be curious to understand
the features of machine learning machine
learning uses the data to detect
patterns in a data set and adjust
program actions accordingly pattern
detection can be defined as the
classification of data based on
knowledge already gained or on
statistical information extracted from
the patterns
it focuses on the development of
computer programs that can teach
themselves to grow and change when
exposed to new data by using a method
called reinforcement learning it uses
external feedback to teach the system to
change its internal workings in order to
guess better next time it enables
computers to find hidden insights using
iterative algorithms without being
explicitly programmed
machine learning uses algorithms that
learn from previous data to help produce
reliable and repeatable decisions it
automates analytical model building
using the statistical and machine
learning algorithms that tease patterns
and relationships from data and express
them as mathematical equations
let's understand the different machine
learning approaches
so what is the actual difference between
traditional programming and machine
learning in traditional programming data
and program is provided to the computer
it processes them and gives the output
however the machine learning approach is
very different in machine learning
algorithms are applied on the given data
and output
the result of the applied algorithm and
calculations is a learning model that
helps machine to learn from the data
in traditional programming you code the
behavior of the program but in machine
learning you leave a lot of that to the
machine to learn from data
now let's first understand the
traditional programming approach
traditionally you would hard code the
decision rules for a problem at hand
evaluate the results of the program and
if the results were satisfactory the
program would be deployed in production
if the results were not as expected one
would review the errors change the
program and evaluate it again this
iterative process continues till one
gets the expected result
what is the machine learning approach in
the new machine learning approach the
decision rules are not hard-coded the
problem is solved by training a model
with the training data in order to
derive or learn an algorithm that best
represents the relationship between the
input and the output this trained model
is then evaluated against test data if
the results were satisfactory the model
would be deployed in production and if
the results are not satisfactory the
training is repeated with some changes
machine learning techniques
machine learning uses a number of
theories and techniques from data
science here are some machine learning
techniques classification
categorization clustering Trend analysis
anomaly detection visualization and
decision making let's look at these
techniques
classification is a technique in which
the computer program learns from the
data input given to it and then uses
this learning to classify new
observations
classification is used for predicting
discrete responses classification is
used when we are training a model to
predict qualitative targets
categorization is a technique of
organizing data into categories for its
most effective and efficient use it
makes free text searches faster and
provides a better user experience
clustering is a technique of grouping a
set of objects in such a way that
objects in the same group are most
similar to each other than to those in
other groups it is basically a
collection of objects on the basis of
similarity and dissimilarity between
them
Trend analysis is a technique aimed at
projecting both current and future
movement of events through the use of
Time series data analysis it represents
variations of low frequency in a Time
series The High and medium frequency
fluctuations being out
anomaly detection is a technique to
identify cases that are unusual within
data that is seemingly homogeneous
anomaly detection can be a key for
solving intrusions by indicating a
presence of intended or unintended
induced attacks defects faults and so on
visualization is a technique to present
data in a pictorial or graphical format
it enables decision makers to see
analytics presented visually
when data is shown in the form of
pictures it becomes easy for users to
understand it
decision making is a technique or skill
that provides you with the ability to
influence managerial decisions with data
as evidence for those possibilities
now I am sure you have a better
understanding of the overview of machine
learning so let's look at some real-time
applications of machine learning
artificial intelligence and machine
learning are being increasingly used in
various functions such as image
processing robotics data mining video
games text analysis and Healthcare let's
look at each of them in more details so
what is image processing it is a
technique to convert an image into a
digital format and perform some
operations on it so as to induce an
enhanced image or to extract some
helpful information from it let's look
at some of the examples of image
processing Facebook does automatic face
tagging by recognizing a face from a
previous user's tagged photos another
example is optional character
recognition which scans printed docs to
digitize the text self-driving cars are
another big example of image processing
autopilot is an optional drive system
for Tesla cars when autopilot is engaged
cars can self-steer adjust speed detect
nearby obstacles apply the brakes and
Park
now let's see how robotics uses machine
learning robots are machines that can be
used to do certain jobs some of the
examples of Robotics are where a
humanoid robot can read the emotions of
human beings or an industrial robot is
used for assembling and Manufacturing
products so let's look at some real-time
applications of machine learning let's
see what data mining is
it is the method of analyzing hidden
patterns in data let's look at some of
the applications of data mining it is
used for anomaly detection to detect
credit card fraud and to determine which
transactions vary from usual purchasing
patterns
it is also used in Market Basket
analysis which is used to detect which
items are often bought together
it can be used for grouping where it
classifies users based on their profiles
machine learning is also applied in many
video games in order to give predictions
Based on data in a Pokemon go battle
there is a lot of data to take into
account to correctly predict the winner
of a battle and this is where machine
learning becomes useful a machine
learning classifier will predict the
result of the match based on this data
let's move on to one of the most popular
applications of machine learning which
is text analysis it is the automated
process of obtaining information from
text one example of text analysis is
Spam filtering which is used to detect
spam in emails another example is
sentimental analysis which is used for
classifying an opinion is positive
negative or neutral it detects public
sentiment in Twitter feed or filters
customer complaints it is also used for
information extraction such as
extracting specific data address keyword
or entities
there are many applications of machine
learning in the healthcare industry
identifying disease and diagnosis drug
Discovery and Manufacturing Medical
Imaging diagnosis and so on
some of the companies that use machine
learning have revolutionized the
healthcare industry our Google Deep Mind
Health bio beats Health Fidelity and
ginger dot IO
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what's interesting to me is we will use
AI as we have been to specifically
Target tasks that we need or want done
in place of ourselves
that's how AI will ultimately unfold it
is a Renaissance it is a golden age we
are now solving problems with machine
learning and artificial intelligence
that were you know kind of in the realm
of Science Fiction for the last several
decades
AI is probably the most important thing
Humanity has ever worked on and I think
of it as something more profound than
electricity or fire and anytime you work
with technology you need to learn to
harness the benefits and while
minimizing the downside
foreign
autonomous systems and clearly one
purpose of autonomous systems is
self-driving cars there are others
uh and we sort of see it as the mother
of all AI projects there's a class of
things like game plane or speech
recognition or image recognition that
the performance levels are phenomenal
you know if you compare human speech
recognition computer speech recognition
the computer is slightly better and
that's you know mind-blowing
certainly we use AI to do drug discovery
on these biological systems are very
complicated and so the fact that we have
vaccines for TB and HIV coming that's
partly enabled by this Rich data advance
in biology and machine learning
when I look at seeing AI which is an
application we just launched what
anybody with visual impairment they can
go download this app that uses the
latest Cutting Edge computer vision
technology in our Cloud to give anyone
the ability to see and in fact Angela
Mills was a colleague of mine was just
telling me about how it's changed her
life inside of Microsoft she can go into
our cafeteria or order food using this
app with confidence walk into conference
rooms with confidence because she knows
she's walking into the right conference
room or what we've done with our OneNote
and word learning tools anyone who has
dyslexia can now use AI to be able to
read better the latest release of
Windows 10 has this capability called
eye gaze which is something that we
learned working with Steve Gleason and
the ALS patients saying if you all you
have is the eye muscle and the Gaze can
we help you type
yeah you have to be careful uh when
there's advances in a sense we're all
better off if the machines can make all
the food and the clothes and none of us
have to work uh and you think okay now
we have all the freedom if we want to
stand behind the the counter and you
know make sandwiches okay you can if you
want but there's this other way to make
those goods and services but it will be
very disruptive uh because you know say
your mid-career in manufacturing or
driving then it's a disruption now we've
had that in a slow way for hundreds of
years you know we used to all be Farmers
now very few of us are farmers I said
it's right to be concerned absolutely
you have to worry about it otherwise
you're not going to solve it right and
it's important to understand tomorrow
whether Google is there or not you know
artificial intelligence is going to
progress uh you know technology has this
nature it's a it's going to evolve I
think pull link back history shows
pulling back countries which pull back
don't do well with the change we know
that 20 30 years ago you educated
yourself and that carried you through
for the rest of your life that is not
going to be true for the generation
which is being born now
they have to learn continuously over
their lives we know that so we have to
transform How We Do education
look I think I mean you nailed it
anything that's repetitive and done you
know on the back of you know technology
or you know is going to be fundamentally
vulnerable yeah so I think uh technology
and in particular AI can in fact bring
more empowerment more inclusiveness and
at the same time we should be clear-eyed
about displacement clear-eyed about
unintended consequences like any other
technology and work both Skilling so
that you know people can find the jobs
of the future create new jobs and lastly
I think have a set of policy decisions
that really help people uh as they go
through this change
the risks are important and I think the
way we solve it is we think ahead we
worry about it we do things like from
from be upfront uh you know have ethical
Charters think about AI safety from day
one be very transparent and open in how
we perceive progress there and figure
out Global Frameworks by which we can
engage just like Paris agreement and
climate change you know using forums
like this I think we bring people
together to engage on the hard questions
and I think answers will emerge
um you know I have exposure to the very
most Cutting Edge AI
and I think people should be really
concerned about it
um I keep a soundingly long Bell but you
know until
people see like robots going down the
street killing people like they don't
know how to react you know because it
seems so ethereal
um
and um I think we should be really
concerned about Ai and I think we should
yeah this is AI is a rare case where I
think we need to be proactive in
regulation instead of reactive
because I think by the time we are
reactive in AI regulation it's too late
right now we have
machine learning algorithms that can
solve an incredibly complex problem
Beyond any human intelligence but
they're essentially complete idiots and
like two-year-olds and anything that's
not that problem they're dumb like that
you can give them this enormous data set
and they come up with brilliant
correlations and insights but they're
not going to plug into Skynet and you
know like like like threaten us anytime
soon
so I'm quite optimistic
and I don't think
artificial intelligence is a threat I
don't think
intelligence is something terrible
but human being are smart enough to
learn that
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