Introduction To Artificial Intelligence | What Is AI?| Artificial Intelligence Tutorial |Simplilearn

Summary

This 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

• 📈 **Data Economy Growth**: The rapid increase in data volume has led to the emergence of artificial intelligence (AI).
• 🤖 **Defining AI**: AI refers to the intelligence displayed by machines that simulate human and animal intelligence.
• 🚗 **AI in Practice**: Self-driving cars are a notable example of AI in action, requiring no human intervention to operate.
• 🔍 **AI Applications**: AI is redefining industries by personalizing user experiences and automating processes.
• 🗣️ **Siri and AI**: Apple's Siri is an AI application that simplifies iPhone navigation through voice commands.
• 🏆 **AlphaGo**: Google's AlphaGo is an AI program that made history by defeating a world champion at the game of Go.
• 🏠 **Amazon Echo**: Amazon Echo is an AI-driven home control device that responds to voice commands.
• 🎶 **IBM Watson**: IBM Watson is an AI known for composing music, playing chess, and even cooking food.
• 🛒 **Recommendation Systems**: E-commerce companies use AI to analyze user data and recommend products based on past behavior.
• 🔄 **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

00:00

🌟 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.

05:01

🔗 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.

10:02

🤖 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.

15:04

🚀 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.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is discussed as a technique that enables computers to mimic human intelligence using logic, involving intelligence agents that perceive their environment and take actions to maximize their chances of success. An example given is self-driving cars, which are a practical application of AI where the vehicle operates autonomously without the need for human control.

💡Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from structured and unstructured data. The video describes data science as helping analyze the vast amounts of data produced in the data economy, and it is integral to the new paradigm where machines are taught to learn from data, which is a fundamental aspect of AI.

💡Machine Learning

Machine Learning is a subset of AI that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The video explains that machine learning allows machines to gain intelligence, which in turn enables AI. It is depicted as a process where algorithms are applied to data to output a learning model that helps the machine learn from the data.

💡Data Economy

The Data Economy refers to the significant growth in data volume over the years and its impact on the economy. The video mentions that the explosion of data has given rise to a new economy where there is a constant battle for data ownership between companies, aiming to derive benefits from it.

💡Big Data

Big Data refers to the massive volume of structured and unstructured data that is too large and complex to be processed by traditional data management tools. In the context of the video, the increase in data volume has given rise to big data, which helps manage huge amounts of data, setting the stage for AI and machine learning to analyze and derive insights from this data.

💡Deep Learning

Deep Learning is a subfield of machine learning that uses artificial neural networks inspired by the structure and function of the brain. The video explains that deep learning is most effective when there isn't a clear structure to the data, and it is used to find patterns and relationships within complex data sets.

💡Siri

Siri, as mentioned in the video, is a voice-activated virtual assistant that uses AI to perform tasks or answer questions as it listens to voice commands. It exemplifies how AI can provide greater personalization to users, making technology more interactive and user-friendly.

💡AlphaGo

AlphaGo, highlighted in the video, is a computer program developed by Google DeepMind that plays the board game Go. It is significant because it was the first program to defeat a world champion at Go, showcasing the advanced capabilities of AI in mastering complex tasks.

💡Amazon Echo

Amazon Echo, as described in the video, is a home control chatbot device that responds to voice commands, playing music, movies, and controlling smart home devices. It illustrates the application of AI in creating interactive devices that can understand and respond to human speech.

💡IBM Watson

IBM Watson, mentioned in the video, is an AI system known for its ability to compose music, play chess, and even cook food. It represents the versatility of AI in various fields, from creative tasks to strategic games and complex decision-making.

💡Recommendation Systems

Recommendation Systems use AI and machine learning algorithms to suggest products or services to users based on their past behavior. The video gives an example of Amazon using data from users to recommend products, enhancing the shopping experience by suggesting items that align with the user's interests.

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