AI INTRODUCTION.pptx,INFORMATION TECHNOLOGY

3- DAYS CAPACITY
BUILDING PROGRAMME
ON ICT (MIDDLE)
27th,28th & 29th Oct 2022
WELCOME
TO
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SANTOSH VERMA
DAV ISPAT SR SEC PUBLIC SCHOOL
RAJHARA MINES (CG) ZONE-1

The ability to do some task is called Intelligence.
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Limitations of Human:
Human are having certain limitations-
• We can’t fly but machines can like aircraft.
• We can’t go in high temperature, we can’t go near the sun.
• We can’t go in dangerous gas in mines.
• We can’t dive deep in the ocean.
• We can’t memorize all the things of our past.
Limitations of Machines:
• They are not smarter as human are.
• They have no decision making power.
• They can’t analyze and predict the things.
• They can’t learn from their mistakes.
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So what we are doing with AI?
Now to overcome these limitations of machines, with the help
of technology we make machines as intelligent as we are.
In this world of robotics and technology we are trying to make our
machines smarter as humans are and sometimes more smarter and
more Robo standard humans.
In AI we are trying to make machines closer to humans and try
to put all the qualities of human in to the machines and beyond that.
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John MaCarthy first coined the term Artificial Intelligence in year 1956.
He defines AI as “The Science and Engineering of making Intelligent
machines.”
Artificial Intelligence is a wide ranging branch of computer science
concerned with building smart machines and with AI it is possible to learn
from the experiences.
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Language
HV
Learning
Prediction
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Human Intelligence vs Artificial Intelligence
Language
HV
Learning
Prediction
NLP
CV
ML
Prediction
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Natural Language Processing
(NLP)
If a machine is able to understand human languages that what we
called as Natural Language Processing.
Understanding language is a human intelligence and if we give this
intelligence to machine then it is Artificial intelligence and the quality that
machine is having is known as Natural Language Processing.
Some device or machines that can understand human language:-
Google Home, Alexa(Amazon), Siri(I-phone), Cortana(Microsoft),
Google Assistant , Google Translate etc.
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Chatbots and Virtual Assistant:-
Designed to reply only certain set of questions.
(Ex: news chatbot, banking, business companies, tourist guide chatbot etc.)
VA is not pre programmed, it perform activities based on user inputs.
Amazon Alexa: The Amazon echo is a device that uses speech recognition to perform
an ever growing range of tasks on command.
Command-1
Alexa book a
car.
There is a Uber 3 min
away
Command-2
Alexa order a
cheese pizza
You want medium or
large pizza
Command-3
Alexa play
my music.
Playing your favourite.
Command-4
Alexa turn
on the lights.
Turning on the lights.

Language
HV
Learning
Prediction
NLP
CV
ML
Prediction
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Computer Vision(CV)
Machines do not have eyes so basically it uses a camera for this purpose,
using camera they detect what is in front of it. In computer vision there must be
camera, it can be normal camera or day vision or night vision camera.
Camera captures images that is in its frame and AI device try to identify them.
Some example of Computer vision type of devices:-
• Google Lens
• Traffic Camera
• 360° Rotating Cameras
• ATM Camera
• Entry Verification Camera
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Language
HV
Learning
Prediction
NLP
CV
ML
Prediction
20

Machine Learning(ML)
Human are capable of learning new things through which we enhance our
capabilities. Now this capability of learning new things, If it is given to a machine or a
device, then it is become AI and this AI more specifically called as Machine Learning.
Example of Machine Learning:-
Some Example of Machine learning:
• Mobile face unlock
• Finger pattern unlock
• Google Ads
• Facebook tagging
• Search engine
• Play Store
• Netflix
• You Tube Recommendation 21

Rock, Paper, Scissors Game
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https://rockpaperscissors-ai.vercel.app

Ex-1
Ex-2
Step-1 Feed lot many values in Celsius (-40,-10, 0 , 8 15, 22, 38)
Step-2 Feed corresponding Fahrenheit values (-40, -14,32,46,59,72, 100)
Step-3 Pass these two set of values to ML Algorithm
Step-4 Now ask the ML program to predict any other celsius value to Fahrenheit and
program will tell you the answer. (F=C * 1.8 +32)
PEN

Conventional Programming Approach
Input + Output
Program =
Input + Program
Output =
Machine Learning Approach

Language
HV
Learning
Prediction
NLP
CV
ML
Prediction
25

Prediction
As we can predict the things, machines have algorithms and programs that
can also predict the situations like rain, storm, prediction of COVID cases etc. on basis
of its historical data.
Example:
• Tesla (Self driving cars) can recognize the obstacles on road, traffic signals,
breaker, any vehicle around it etc.
• Banking and Finance: Predictive analysis is used to detect and reduce fraud,
measure market risk and to identify new business opportunities.
• Retail: Predictive analysis and machine learning allows retailers to understand
consumer behavior such as who will buy what and at which store.
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Stage-1 : Problem Scoping [4 W’s Who,What,Where,Why]
Stage-2 : Data Acquisition [Data collection]
Stage-3 : Data Exploration [Analysing Data]
Stage-4 : Modelling [Code the AI model]
Stage-5 : Evaluation and Development [Checking the accuracy of model]
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AI model is program or
algorithm.supervised/uns
upervised,reinforcement
learning model
Visualize data via
table,graph,charts
Testing the
reliability of model

Supervised learning :-
Supervised learning is a category in which we feed labelled data as input to the
machine learning model.

Unsupervised learning:
It is a category of machine learning in which we only have the input data to feed to
the model but no corresponding output data.

Reinforcement learning:
Reinforcement Learning is a feedback-based Machine learning
technique in which an agent learns to behave in an environment by
performing the actions and seeing the results of actions. For each good
action, the agent gets positive feedback, and for each bad action, the
agent gets negative feedback or penalty.

Relationship among AI Components
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AI
NLP
ML
Deep
Learning
Neural
Network

Deep Learning
 Huge amount of data
 Complex problems
 Advance Feature Extraction
 Handle large amount
of structured and
unstructured data
 Medical Fields
 Robotics
 Self driving cars
 Translation
Volume of
Data
Performance
DL
ML
It is a type of machine learning based on artificial neural
networks in which multiple layers of processing are used to
extract higher level of features from data.

Neural Netwotk
It is a computational model that works in a similar way to the neurons in the
human brain. Each neuron takes an input, performs some operations then
passes the output to the next neuron.
It also known as artificial neural networks and are the heart of deep
learning algorithms.

Applications of Artificial Intelligence
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1. Muse Net:- A deep neural networks that can generate 4 minutes musical
compositions with 10 different instruments.
2. AI in Social Media:- Facebook uses Machine learning and deep learning to
detect facial features and tag your friends.
3. Google Assistant:- Set alarm to 5AM.
Open Calculator.
Play the songs of Jagjit Singh.
Call to Kamlendra sir.
Take my selfie.

Applications of Artificial Intelligence
4. AI in Space Exploration:- AEGIS is on mars, NASA’s current rovers.
It is developed to handle autonomous targeting of cameras and choose what
to investigate.
5. AI in Gaming field:-On the past few years AI has become an integral part of gaming
industry.
6. AI in Banking and Finance:- AI is very much suitable for this task as it learn from
pattern of past data and predicts the trading market in future.
7. AI in Health Care:-
Many medical centre are relying on AI (IBM Watson health care centre)
> Retinal Scans > Complex surgery
> Body investigation > Acupuncture and Physiotherapy
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8. AI in Marketing:- Amazon/ Flip Cart recommendation of products.
• Recently viewed item and similar products
• Customer can also buy this.
• All of these are carried out by AI and machine learning algorithms.
9. Smart Homes:
Smart homes are also run by Artificial Intelligence.
 Home appliances are voice controlled.
 Sensor adjust coolers and light according to the climate.
 Security system warn the resident after detecting movement outside
home.
10. AI in autonomous Vehicles: Companies like WAYMO and TESLA has launched
AI base driverless cars . These vehicles collects data from vehicle's GPS, Radar,
Camera’s and cloud services to produce control signals that operates the vehicles.
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AI Ethics
New technologies have unintended negative side effects and it can be ban or
boon. The ethics is a study of values, generating moral values for good or bad, right or
wrong. Ethics plays a major role to find ways on how to handle future projects.
 AI technology is costly.
 It can lead to increase in unemployment.
 AI system result in a loss of accountability.
 AI machines lacks in creativity.
 Less human interaction and emotions.
 Privacy is another issue with AI machines.
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AI INTRODUCTION.pptx,INFORMATION TECHNOLOGY

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