Introduction To Artificial Intelligence | What Is AI?| Artificial Intelligence Tutorial |Simplilearn
Summary
TLDRThis informative script delves into the realms of artificial intelligence (AI) and machine learning (ML), their relationship with data science, and their transformative impact on various industries. It outlines the emergence of AI due to the exponential growth of data, highlighting its applications in self-driving cars, virtual assistants like Siri, and Google's AlphaGo. The script also explores ML techniques such as classification and clustering, and their real-world implementations in image processing, robotics, data mining, gaming, and healthcare. It emphasizes the synergy between AI, ML, and data science, where data science lays the groundwork, ML builds predictive models, and AI executes actions based on insights.
Takeaways
- π **Data Economy Growth**: The rapid increase in data volume has led to the emergence of artificial intelligence (AI).
- π€ **Defining AI**: AI refers to the intelligence displayed by machines that simulate human and animal intelligence.
- π **AI in Practice**: Self-driving cars are a notable example of AI in action, requiring no human intervention to operate.
- π **AI Applications**: AI is redefining industries by personalizing user experiences and automating processes.
- π£οΈ **Siri and AI**: Apple's Siri is an AI application that simplifies iPhone navigation through voice commands.
- π **AlphaGo**: Google's AlphaGo is an AI program that made history by defeating a world champion at the game of Go.
- π **Amazon Echo**: Amazon Echo is an AI-driven home control device that responds to voice commands.
- πΆ **IBM Watson**: IBM Watson is an AI known for composing music, playing chess, and even cooking food.
- π **Recommendation Systems**: E-commerce companies use AI to analyze user data and recommend products based on past behavior.
- π **AI, Machine Learning, and Data Science**: AI involves mimicking human intelligence, machine learning allows systems to learn from experience, and data science encompasses various disciplines including AI and machine learning.
Q & A
What is the primary factor behind the emergence of artificial intelligence?
-The primary factor behind the emergence of artificial intelligence is the data economy, which refers to the significant growth of data over the past years and its projected growth in the future.
How has the volume of data grown since 2009?
-Since 2009, the volume of data has increased by 44 times, largely due to the explosion of data from social websites.
What is the relationship between artificial intelligence and data science?
-Artificial intelligence is a subset of data science. Data science involves analyzing data to derive insights, and artificial intelligence enables machines to learn from data, simulating human intelligence to make decisions or predictions.
Define machine learning and its relationship with artificial intelligence.
-Machine learning is a type of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It is a subset of AI that focuses on the development of computer programs that can access data and use it to learn for themselves.
What are some applications of machine learning?
-Machine learning is applied in various fields such as image processing, robotics, data mining, video games, text analysis, and healthcare. It is used for tasks like face recognition, credit card fraud detection, spam filtering, and medical diagnosis.
How does Siri on the iPhone use artificial intelligence?
-Siri uses artificial intelligence to understand and respond to voice commands, allowing users to perform tasks like making calls or playing music without manual input.
What is Google's AlphaGo, and how does it relate to AI?
-Google's AlphaGo is a computer program that plays the board game Go. It is an example of AI as it uses machine learning algorithms to learn from experience and improve its gameplay, eventually becoming the first program to defeat a world champion at Go.
How does Amazon Echo utilize AI?
-Amazon Echo is a home control chatbot device that uses AI to understand and respond to voice commands. It can play music, control smart home devices, and perform other tasks based on user interactions.
What is the role of machine learning in recommendation systems used by e-commerce companies?
-Machine learning in recommendation systems analyzes user data to predict and suggest products that align with a user's interests or past purchasing behavior, enhancing the personalized shopping experience.
How does deep learning fit into the broader field of machine learning?
-Deep learning is a subfield of machine learning that uses artificial neural networks to model complex patterns. It is effective for unstructured data and is used when there isn't a clear structure to exploit for feature building.
What are the key differences between traditional programming and machine learning?
-In traditional programming, decision rules are hardcoded, and the program's behavior is explicitly defined. In contrast, machine learning involves training models with data to learn and improve over time without explicit programming of the decision rules.
Outlines
π Introduction to Artificial Intelligence and Machine Learning
This paragraph introduces the concepts of artificial intelligence (AI) and machine learning (ML). It outlines the objectives of the lesson, which include defining AI, explaining its relationship with data science, defining ML, and describing the interplay between ML, AI, and data science. It also covers different ML approaches and their applications. The emergence of AI is attributed to the growth of the data economy, which is highlighted by the exponential increase in data volume since 2009, largely due to social media. The script explains AI as the simulation of human and animal intelligence by machines, involving autonomous entities that perceive and act in their environment. Examples of AI in practice include self-driving cars, Apple's Siri, Google's AlphaGo, Amazon Echo, and IBM Watson. The paragraph also touches on AI's role in personalization and automation, as well as its depiction in sci-fi movies and its application in recommendation systems like those used by Amazon.
π The Interconnectedness of AI, Machine Learning, and Data Science
This paragraph delves into the relationship between AI, ML, and data science. It clarifies that while these terms are related, they each have distinct applications and meanings. AI is described as systems that mimic human intelligence, ML as the ability of systems to learn and improve from experience without explicit programming, and data science as an encompassing field that includes data analytics, data mining, ML, AI, and other related disciplines. The paragraph presents a flow diagram to illustrate these relationships, starting with data gathering and transformation, which falls under data science, followed by the use of ML techniques for predictions and insights. Deep learning, a subfield of ML, is also introduced as being particularly effective with unstructured data. The paragraph concludes by discussing how AI uses predictions and insights to perform actions, either based on human decisions or automated processes.
π€ Features and Techniques of Machine Learning
This paragraph explores the features of ML, focusing on its ability to detect patterns and adjust program actions accordingly. It defines pattern detection and explains how ML uses reinforcement learning to improve system predictions over time. The paragraph also discusses how ML algorithms learn from data to produce reliable decisions and automate analytical model building. The difference between traditional programming and ML is highlighted, with traditional programming requiring hard-coded decision rules, while ML involves training a model with data to derive an algorithm. Various ML techniques are outlined, including classification, categorization, clustering, trend analysis, anomaly detection, visualization, and decision making, with brief explanations of how each is applied in practice.
π Real-World Applications of Machine Learning
The final paragraph discusses real-world applications of ML and AI across various fields. It covers image processing, robotics, data mining, video games, text analysis, and healthcare. Specific examples include Facebook's automatic face tagging, optical character recognition, Tesla's autopilot system, and the use of robots in emotion reading and manufacturing. Data mining applications include credit card fraud detection, market basket analysis, and user grouping. In video games, ML is used for predictions during battles, such as in Pokemon Go. Text analysis applications include spam filtering, sentiment analysis, and information extraction. The paragraph also mentions healthcare applications like disease identification, diagnosis, drug discovery, and medical imaging, highlighting companies like Google DeepMind Health, BioBeats Health, Fidelity, and Ginger.io that are revolutionizing healthcare with ML.
Mindmap
Keywords
π‘Artificial Intelligence (AI)
π‘Data Science
π‘Machine Learning
π‘Data Economy
π‘Big Data
π‘Deep Learning
π‘Siri
π‘AlphaGo
π‘Amazon Echo
π‘IBM Watson
π‘Recommendation Systems
Highlights
Introduction to artificial intelligence and machine learning, outlining the ability to define AI, describe its relationship with data science, and define machine learning.
The data economy as a factor behind the emergence of AI, highlighting the explosive growth of data volume since 2009.
The importance of AI in managing big data and the new paradigm of teaching machines to learn from data.
Definition of artificial intelligence as the intelligence displayed by machines that simulate human and animal intelligence.
Applications of AI in redefining industries through personalization and automation, exemplified by self-driving cars.
Examples of AI in practice, including Siri on iPhones, Google's AlphaGo, Amazon Echo, and IBM Watson.
The reflection of AI concepts in science fiction movies, indicating the fascination with AI in popular culture.
Explanation of recommendation systems used by e-commerce companies, such as Amazon's product recommendations based on user data.
Clarification of the distinct yet interconnected domains of artificial intelligence, machine learning, and data science.
Flow diagram illustrating the relationship between data gathering, machine learning techniques, and AI actions.
Deep learning as a subfield of machine learning that uses artificial neural networks to process unstructured data.
The role of data analysis in deriving insights from predictions made by machine learning algorithms.
The relationship between AI and machine learning, where machine learning enables AI through learned intelligence.
The symbiotic relationship between data science and machine learning, with data science providing the framework for machine learning algorithms.
Features of machine learning, including pattern detection, reinforcement learning, and iterative algorithms for hidden insights.
Differences between traditional programming and machine learning approaches in terms of decision rules and algorithm learning.
Machine learning techniques such as classification, categorization, clustering, trend analysis, anomaly detection, visualization, and decision making.
Real-time applications of machine learning in image processing, robotics, data mining, video games, text analysis, and healthcare.
Examples of image processing in facial recognition, character recognition, and self-driving cars' autopilot systems.
Applications of machine learning in robotics, such as emotion-reading humanoid robots and industrial robots for manufacturing.
The use of machine learning in video games for predicting outcomes based on data, like in Pokemon Go battles.
Text analysis applications, including spam filtering, sentiment analysis, and information extraction.
Machine learning's impact on the healthcare industry through disease identification, drug discovery, and medical imaging diagnosis.
Companies like Google DeepMind and Health Fidelity revolutionizing healthcare with machine learning applications.
Transcripts
[Music]
introduction of artificial intelligence
and machine learning
by the end of this lesson you will be
able to define artificial intelligence
describe the relationship between
artificial intelligence and 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 a.i
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 control 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 the 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 the 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
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 homogenous
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
as 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 health
care industry are google deep mind
health
bio beats health fidelity and ginger dot
io
[Music]
you
Browse More Related Video
Understanding Artificial Intelligence and Its Future | Neil Nie | TEDxDeerfield
ML Engineering is Not What You Think - ML jobs Explained
Artificial Intelligence Class 10 Ch 1 |AI vs Machine Learning vs Deep Learning (Differences) 2022-23
AI vs Machine Learning
Webinar: AI/ML in the Fintech Industry by PayPal Global PM, Vinod Jain
What is Artificial Intelligence for Kids | What is AI | AI for Kids | AI explained for Kids |AI Kids
5.0 / 5 (0 votes)