SlideShare a Scribd company logo
Chapter Three
Introduction to
Artificial Intelligence (AI)
What is Artificial Intelligence (AI)
2
•AI is composed of two words Artificial and
Intelligence.
•Artificial defines "man-made," and
• Intelligence defines "thinking power", or
“the ability to learn and solve problems”
• hence Artificial Intelligence means "a
man-made thinking power."
What is Artificial Intelligence (AI)
3
Cont
.
4
•So, we can define AI as the branch of CS by
which we can create intelligent machines which
can:
•behave like a human,
•think like humans, and
•able to make decisions.
Cont
.
5
• Intelligence, as we know, is the ability to acquire and
apply knowledge.
• Knowledge is the information acquired
through
experience.
• Experience is the knowledge gained through
exposure
(training).
• artificial intelligence is the “copy of something
natural (i.e., human beings) ‘WHO’ is capable of
acquiring and applying the information it has
gained through exposure.”
cont
.
6
• AI exists when a machine can have human-based skills
such as learning, reasoning, and solving problems.
• In AI you do not need to preprogram a machine to do some
work, despite that you can create a machine with
programmed algorithms which can work with own
intelligence.
• Intelligence is composed of:
•Reasoning
•Learning
•Problem Solving
•Perception
•Linguistic Intelligence
AI System
• An AI system is composed of an agent and its environment.
o An agent (e.g., human or robot) is anything that can perceive its
environment through sensors and acts upon that environment
through effectors.
• Intelligent systems must be able to set goals and achieve them.
• In classical planning problems,
o the agent can assume that it is the only system acting in the
world, allowing the agent to be certain of the consequences of
• However,
o if the agent is not the only actor, then it requires that the agent
can reason under uncertainty.
• This calls for an agent that cannot only assess its environment
and make predictions but also evaluate its predictions and
adapt based on its assessment.
• Machine perception is the ability to use input from sensors
(such as cameras, microphones, sensors, etc.) to deduce
aspects of the world. e.g., Computer Vision.
cont
.
High-profile examples of AI
include
• Autonomous vehicles (such as drones and self-
driving
cars),
• Medical diagnosis,
targeting
• Creating art (such as poetry),
• Proving mathematical theorems,
• Playing games (such as chess or go),
• Search engines (such as google search),
• Online assistants (such as siri),
• Image recognition in photographs,
• Spam filtering,
• Prediction of judicial decisions
and advertisements
online
69
AI, Big Data and GPU
10
•AI is the development of machines able to
perform tasks normally requiring human
intelligence such as: visual perception, speech
recognition, decision-making and translation
between languages.
•The advent of big data combined with cheaper
and more powerful hardware such as
Graphical Processing Units (GPUs) has
enabled AI to evolve into more complex
architectures.
•GPU: able to process tasks in parallel fashion
Machine learning
11
•Machine learning was introduced by Arthur
Samuel in 1959.
•Machine Learning is an advanced form of
AI where the machine can learn as it goes
rather than having every action
programmed by humans.
•ML, fundamental concept of AI research since
the field’s inception, is the study of computer
algorithms that improve automatically through
experience.
Machine learning
12
Machine learning
13
Machine learning Terms
14
Machine learning Terms
15
Machine learning Terms
16
Deep learning
Neural networks are biologically inspired networks
that extract features from the data in a hierarchical
fashion. The field of neural networks with several
hidden layers is called deep learning.
17
Figure 3.1 Artificial Intelligence (AI), Machine Learning (ML)
and Deep Learning (DL)
Deep learning
18
Why we Need AI at this time?
19
1.To create expert systems that
show
intelligent behavior with the capability to
learn, demonstrate, explain and advice its
users.
2.Helping machines find solutions to
complex problems like humans do and
applying them as algorithms in a
computer-friendly manner.
Goals of Artificial
Intelligence
1.Replicate human intelligence
2. Solve Knowledge-intensive tasks
3.An intelligent connection of perception and action
4.Building a machine which can perform tasks
that requires human intelligence such as:
•Proving a theorem
• Playing chess
•Plan some surgical operation
•Driving a car in traffic
5.Creating some system which can show intelligent
behavior, learn new things by itself,
demonstrate,
explain, and can advise to its user.
73
Disciplines which AI
requires
21
To make a machine learn and make a decision
like humans do, AI requires the knowledge of
some disciplines. Write down some disciplines
which AI requires?
Disciplines which AI
requires
22
• Intelligence is an intangible part of our brain which is a
combination of Reasoning, learning, problem-solving,
perception, language understanding.
• To achieve those factors for a machine or software,
Artificial Intelligence requires the following disciplines
Figure 3.2 Artificial Intelligence is multidisciplinary
Advantages of Artificial Intelligence
23
•High Accuracy with fewer errors:
•High-Speed:
•High reliability
•Useful for risky areas: e.g. bomb area
•Digital Assistant: e.g. E-commerce website
•Useful as a public utility e.g. self driving car
Disadvantages of Artificial Intelligence
24
•High Cost: The HW and SW requirement of
AI is very costly
•Can't think out of the box: as the robot
will only do that work for which they are
trained, or programmed.
•No feelings and emotions:.
•Increase dependence on machines:
•No Original Creativity:
History of
AI
25
26
Types of AI
• There are mainly two types of the main
categorization which are based on capabilities and
based on functionally of AI.
7
9
capabilities
functionally
A. Based on Capabilities
28
Weak AI or Narrow AI:
• Narrow AI is a type of AI which is able to perform a
dedicated task with intelligence.
• Narrow AI cannot perform beyond its field or limitations,
as it is only trained for one specific task.
• Hence it is also called as weak AI.
• Narrow AI can fail in unpredictable ways if it goes
beyond its limits.
• e.g. Apple Siri, Google translate, playing
chess,
purchasing suggestions on e-commerce sites, self-driving
cars, speech recognition, and image recognition.
Cont
.
29
General AI:
• General AI is a type of intelligence that could perform
any intellectual task with efficiency like a human.
o also known as strong AI
• The idea behind the general AI to make such a system that
could be smarter and think like a human on its own.
• Currently there is no such system existing which could
come under general AI and can perform any task as perfect
as human.
• The worldwide researchers are now focused on developing
machines with General AI.
Super
AI
30
• Super AI is a level of Intelligence of Systems at which
machines could surpass human intelligence,
• and can perform any task better than a human with
cognitive properties.
• This refers to aspects like general wisdom, problem
solving and creativity. It is an outcome of general AI.
• key characteristics of strong AI include capability
include the ability to think, to reason solve the puzzle,
make judgments, plan, learn, and communicate on its
own.
• Super AI is still a hypothetical concept of AI.
Currently existing level of Intelligence
31
• Many currently existing systems that claim to use AI
are actually operating as weak AI, focus on narrowly
defined specific problems.
• We are now at the stage of ANI, we haven’t actually
reach AGI & ASI.
B. Based on the functionality
32
1. Reactive Machines
• Purely reactive machines are the most basic
types of Artificial Intelligence.
• Such AI systems do not store memories or
past experiences for future actions.
• These machines only focus on current
scenarios and react on it as per possible best
action.
• Example: IBM's Deep Blue system, Google's
AlphaGo
Cont
.
33
2. Limited Memory
• Limited memory machines can store past
experiences or some data for a short period of
time.
• These machines can use stored data for a limited
time period only.
E.g. Self-driving cars
• These cars can store the recent speed of nearby
cars, the distance of other cars, speed limits, and
other information to navigate the road.
.
34
3. Theory of Mind
• Theory of Mind AI should understand human emotions,
people, beliefs, and be able to interact socially like
humans.
• This type of AI machine is still not developed, but
researchers are making lots of efforts and improvements
for developing such AI machines.
4. Self-Awareness
• Self-awareness AI is the future of Artificial Intelligence.
• These machines will be super intelligent and will have
their own consciousness, sentiments, and self-awareness.
• These machines will be smarter than the human mind.
How humans think
35
•Observe and input the information or data
in the brain.
• Interpret and evaluate the input that is
received from the surrounding environment.
•Make decisions as a reaction towards what
you received as input and interpreted
and evaluated.
Activity
36
• Is it possible to map the way of human thinking
to artificial intelligence components? If your
answer is yes, how?
How human think Mapping human thinking to AI
components or layers
Observe and input the
information or data in the
brain based on human
organs such as eyes, ears,
sensing layer, which perceives
information from the surrounding
environment.
such as voice recognition for sensing
voice and visual imaging recognition
for sensing images.
Interpret and evaluate the
input that is received from
the surrounding
environment.
interpretation layer, that is,
reasoning and thinking about the
gathered input that is acquired by the
sensing layer.
Make decisions or taking
action as a reaction towards
what you received as input
and interpreted and
evaluated.
interacting layer taking action
or making decisions.
E.g. Robotic movement control
and 88
speech generation.
Influencers of artificial intelligence
•List down some influential factors that
accelerate the rise of AI?
•Big data: Structured data versus
unstructured data
•Advancements in computer processing
speed and new chip architectures
•Cloud computing and APIs
•The emergence of data science
89
Big
Data
• Big data refers to huge amounts of data.
• Big data requires innovative forms of
information processing to draw insights,
automate processes, and help decision making.
• Big data can be structured data that corresponds to
a formal pattern, such as traditional data sets and
databases.
• Also, big data includes semi-structured and
unstructured formats, such as word-processing
documents, videos, images, audio, presentations,
social media interactions, streams, web pages, and
many other kinds of content.
90
Structured data versus unstructured data
40
• structured data: information with an organized structure,
such as a relational database that is searchable by simple
and straightforward search engine algorithms or SQL
statements.
• But, real-world data such as the type that humans deal with
constantly does not have a high degree of organization.
• For example, text that is written or spoken in natural
language (the language that humans speak) does
not constitute structured data.
• Unstructured data is not contained in a regular database
and is growing exponentially, making up most of the
data in the world.
The emergence of data science
41
• The goal of data science is to extract knowledge
or insights from data in various forms, either
structured or unstructured, which is like data
mining.
• Data science has emerged in the last few years as a
new profession that combines several disciplines,
such as statistics, data analysis, machine learning,
and others.
Advancements in computer processing
speed, new chip architectures, and big
data file systems.
42
• Significant advancements in computer processing
and memory speeds enable us to make sense of the
information that is generated by big data more
quickly.
• In the past, statisticians and early data scientists
were limited to working with sample data sets.
• In recent years, big data and the ability to process a
large amount of data at high speeds have enabled
researchers and developers to access and work with
massive sets of data
43
• Processing speeds and new computer chip
architectures contribute to the rapid evolution of AI
applications.
• The meaning of big data expanded beyond the
volume of data after the release of a paper by
Google on MapReduce and the Google File System
(GFS), which evolved into the Apache Hadoop
open-source project.
• The Hadoop file system is a distributed file system
that may run on a cluster of commodity machines,
where the storage of data is distributed among the
cluster and the processing is distributed too.
Cont
.
44
• This approach determines the speed with which data
is processed.
• This approach includes an element of complexity
with the introduction of new, structured,
unstructured, and multi-structured data types.
• Large manufacturers of computer chips such as IBM
and Intel are prototyping “brain-like” chips whose
architecture is configured to mimic the biological
brain’s network of neurons and the connections
between them called synapses.
Cont
.
45
• Cloud computing is the on-demand availability of computer
system resources, especially data storage (cloud storage) and
computing power, without direct active management by the
user.
• Cloud computing is a general term that describes the delivery
of on-demand services, usually through the internet, on a
pay-per-use basis.
• Companies worldwide offer their services to customers over
cloud platforms.
• These services might be data analysis, social media, video
storage, e-commerce, and AI capabilities that are available
through the internet and supported by cloud computing.
Cloud Computing
46
• An application programming interface (API), is a computing
interface that defines interactions between multiple software
intermediaries. It defines the kinds of calls or requests that
can be made, how to make them, the data formats that should
be used, the conventions to follow, etc.
• In general, application programming interfaces (APIs)
expose capabilities and services. APIs enable software
components to communicate with each other easily.
• APIs abstract the underlying workings of a service,
application, or tool, and expose only what a developer needs,
so programming becomes easier and faster.
• AI APIs are usually delivered on an open cloud-based
platform on which developers can infuse AI capabilities into
digital applications, products, and operations by using one or
more of the available APIs.
Cloud Computing
47
• All the significant companies in the AI services market
deliver their services and tools on the internet through APIs
over cloud platforms, for example:
o IBM delivers Watson AI services over IBM Cloud.
(https://www.ibm.com/watson/about)
o Amazon AI services are delivered over Amazon Web
Services (AWS). (https://aws.amazon.com/)
o The leading cloud provider in the marketplace is
Amazon Web Services. It provides over 170 AWS
services to the developers so they can access them
from anywhere at the time of need.
o Microsoft AI tools are available over the MS Azure
cloud.
o Google AI services are available in the Google Cloud
Platform.
Cloud Computing
Application of
AI
48
• Artificial Intelligence has various applications
in today's society.
• It is becoming essential for today's time because it
can solve complex problems
• in an efficient way in multiple industries, such as
Healthcare, entertainment, finance, education, etc.
• AI is making our daily life more comfortable and
faster.
Applications of AI
cont.
49
AI in agriculture
• Agriculture is an area that requires various
resources, labor, money, and time for the best
result.
• Now a day's agriculture is becoming digital,
• Agriculture is applying AI as agriculture
robotics,
solid and crop monitoring, predictive
analysis.
• AI in agriculture can be very helpful for
AI in Healthcare
50
•Healthcare Industries are applying AI
to make a better and faster diagnosis
than humans.
•AI can help doctors with diagnoses
and can inform when patients are
worsening so that medical help can
reach the patient before hospitalization.
AI in education
51
•AI can automate grading so that the tutor
can have more time to teach.
•AI chatbot can communicate with
students as a teaching assistant.
•AI in the future can be work as a personal
virtual tutor for students, which will be
accessible easily at any time and any
place
• AI in Gaming
• AI in Finance and E-commerce
• AI in Data Security
• AI in Social Media
• AI in Travel &Transport
• AI in Robotics
• AI in Entertainment
• AI in the Automotive Industry
52
Cont
.
AI tools and Platforms
53
•Reading assignment
.
54
End of chapter
three ?

More Related Content

Similar to a set of technologies that enable computers to perform a variety of advanced functions (20)

PPTX
Chapter Three:Artifitial Intelligence (AI)
mootii4
 
PPTX
Chapter 3 - EMTE.pptx
Eyersu Selemon
 
PPTX
Introduction to AI.pptx
TIROEDITS1
 
PPTX
Chapter 3 - Artificial Intelligence.pptx
surafel123emiru
 
PPTX
Emerging chapter 3.pptx
GemechuAyana4
 
PPTX
Unit1_AI&ML (2).pptx
sahilshah890338
 
PPTX
Intro to ET [Chapter 03 chgjkljg uyt ftyf f yyf 7].pptx
TemesgenAsmamaw4
 
PPTX
Artificial Intelligence PPT of Gauransh Mullick 5A,R.N-15.pptx
GauranshMullick
 
PPTX
Artificial Intelligence - Intro History Adv Disad and Types.pptx
DarpanKamboj
 
PPTX
ARTIFICIAL INTELLIGENCE Competition AI PPT.pptx
prawinsk2005
 
PPTX
Addis abeb university ..Artificial intelligence .pptx
ethiouniverse
 
PPTX
emerging thchnology cha 3 about artificial intellegence(1).pptx
karaosmanm142
 
PPTX
Different Branches of AI best prestation for
AssadLeo1
 
PPTX
module 1 & 2_AI_introduction to AI,Problem solving techniques
poojapp6
 
PPTX
Artificial Intelligence
Prakhyath Rai
 
PDF
Technologies Demystified: Artificial Intelligence
Pioneers.io
 
PPTX
Introduction to Artificial Intelligence Concept
ArvindMeniya1
 
PPTX
Emerging Technology Chapter 3 Artificial Intelligence
SolomonEndalu
 
PPTX
ARTIFICIAL INTELLIGENCE and how it imactas
AkshatSingh451724
 
PPTX
Presentation-AI.pptx
SumaiyaRaiyan
 
Chapter Three:Artifitial Intelligence (AI)
mootii4
 
Chapter 3 - EMTE.pptx
Eyersu Selemon
 
Introduction to AI.pptx
TIROEDITS1
 
Chapter 3 - Artificial Intelligence.pptx
surafel123emiru
 
Emerging chapter 3.pptx
GemechuAyana4
 
Unit1_AI&ML (2).pptx
sahilshah890338
 
Intro to ET [Chapter 03 chgjkljg uyt ftyf f yyf 7].pptx
TemesgenAsmamaw4
 
Artificial Intelligence PPT of Gauransh Mullick 5A,R.N-15.pptx
GauranshMullick
 
Artificial Intelligence - Intro History Adv Disad and Types.pptx
DarpanKamboj
 
ARTIFICIAL INTELLIGENCE Competition AI PPT.pptx
prawinsk2005
 
Addis abeb university ..Artificial intelligence .pptx
ethiouniverse
 
emerging thchnology cha 3 about artificial intellegence(1).pptx
karaosmanm142
 
Different Branches of AI best prestation for
AssadLeo1
 
module 1 & 2_AI_introduction to AI,Problem solving techniques
poojapp6
 
Artificial Intelligence
Prakhyath Rai
 
Technologies Demystified: Artificial Intelligence
Pioneers.io
 
Introduction to Artificial Intelligence Concept
ArvindMeniya1
 
Emerging Technology Chapter 3 Artificial Intelligence
SolomonEndalu
 
ARTIFICIAL INTELLIGENCE and how it imactas
AkshatSingh451724
 
Presentation-AI.pptx
SumaiyaRaiyan
 

Recently uploaded (20)

PPTX
Pharmaceuticals and fine chemicals.pptxx
jaypa242004
 
PDF
Introduction to Productivity and Quality
মোঃ ফুরকান উদ্দিন জুয়েল
 
PPTX
原版一样(Acadia毕业证书)加拿大阿卡迪亚大学毕业证办理方法
Taqyea
 
PPTX
Innowell Capability B0425 - Commercial Buildings.pptx
regobertroza
 
PPTX
265587293-NFPA 101 Life safety code-PPT-1.pptx
chandermwason
 
PPTX
MPMC_Module-2 xxxxxxxxxxxxxxxxxxxxx.pptx
ShivanshVaidya5
 
PDF
Set Relation Function Practice session 24.05.2025.pdf
DrStephenStrange4
 
PDF
Unified_Cloud_Comm_Presentation anil singh ppt
anilsingh298751
 
PPT
inherently safer design for engineering.ppt
DhavalShah616893
 
PPTX
NEUROMOROPHIC nu iajwojeieheueueueu.pptx
knkoodalingam39
 
PPT
Oxygen Co2 Transport in the Lungs(Exchange og gases)
SUNDERLINSHIBUD
 
PPTX
drones for disaster prevention response.pptx
NawrasShatnawi1
 
PDF
monopile foundation seminar topic for civil engineering students
Ahina5
 
PPTX
Types of Bearing_Specifications_PPT.pptx
PranjulAgrahariAkash
 
PPTX
EC3551-Transmission lines Demo class .pptx
Mahalakshmiprasannag
 
PPTX
Presentation on Foundation Design for Civil Engineers.pptx
KamalKhan563106
 
PPTX
Heart Bleed Bug - A case study (Course: Cryptography and Network Security)
Adri Jovin
 
PDF
MOBILE AND WEB BASED REMOTE BUSINESS MONITORING SYSTEM
ijait
 
PPTX
Introduction to Neural Networks and Perceptron Learning Algorithm.pptx
Kayalvizhi A
 
PDF
PRIZ Academy - Change Flow Thinking Master Change with Confidence.pdf
PRIZ Guru
 
Pharmaceuticals and fine chemicals.pptxx
jaypa242004
 
Introduction to Productivity and Quality
মোঃ ফুরকান উদ্দিন জুয়েল
 
原版一样(Acadia毕业证书)加拿大阿卡迪亚大学毕业证办理方法
Taqyea
 
Innowell Capability B0425 - Commercial Buildings.pptx
regobertroza
 
265587293-NFPA 101 Life safety code-PPT-1.pptx
chandermwason
 
MPMC_Module-2 xxxxxxxxxxxxxxxxxxxxx.pptx
ShivanshVaidya5
 
Set Relation Function Practice session 24.05.2025.pdf
DrStephenStrange4
 
Unified_Cloud_Comm_Presentation anil singh ppt
anilsingh298751
 
inherently safer design for engineering.ppt
DhavalShah616893
 
NEUROMOROPHIC nu iajwojeieheueueueu.pptx
knkoodalingam39
 
Oxygen Co2 Transport in the Lungs(Exchange og gases)
SUNDERLINSHIBUD
 
drones for disaster prevention response.pptx
NawrasShatnawi1
 
monopile foundation seminar topic for civil engineering students
Ahina5
 
Types of Bearing_Specifications_PPT.pptx
PranjulAgrahariAkash
 
EC3551-Transmission lines Demo class .pptx
Mahalakshmiprasannag
 
Presentation on Foundation Design for Civil Engineers.pptx
KamalKhan563106
 
Heart Bleed Bug - A case study (Course: Cryptography and Network Security)
Adri Jovin
 
MOBILE AND WEB BASED REMOTE BUSINESS MONITORING SYSTEM
ijait
 
Introduction to Neural Networks and Perceptron Learning Algorithm.pptx
Kayalvizhi A
 
PRIZ Academy - Change Flow Thinking Master Change with Confidence.pdf
PRIZ Guru
 
Ad

a set of technologies that enable computers to perform a variety of advanced functions

  • 2. What is Artificial Intelligence (AI) 2 •AI is composed of two words Artificial and Intelligence. •Artificial defines "man-made," and • Intelligence defines "thinking power", or “the ability to learn and solve problems” • hence Artificial Intelligence means "a man-made thinking power."
  • 3. What is Artificial Intelligence (AI) 3
  • 4. Cont . 4 •So, we can define AI as the branch of CS by which we can create intelligent machines which can: •behave like a human, •think like humans, and •able to make decisions.
  • 5. Cont . 5 • Intelligence, as we know, is the ability to acquire and apply knowledge. • Knowledge is the information acquired through experience. • Experience is the knowledge gained through exposure (training). • artificial intelligence is the “copy of something natural (i.e., human beings) ‘WHO’ is capable of acquiring and applying the information it has gained through exposure.”
  • 6. cont . 6 • AI exists when a machine can have human-based skills such as learning, reasoning, and solving problems. • In AI you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence. • Intelligence is composed of: •Reasoning •Learning •Problem Solving •Perception •Linguistic Intelligence
  • 7. AI System • An AI system is composed of an agent and its environment. o An agent (e.g., human or robot) is anything that can perceive its environment through sensors and acts upon that environment through effectors. • Intelligent systems must be able to set goals and achieve them. • In classical planning problems, o the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of
  • 8. • However, o if the agent is not the only actor, then it requires that the agent can reason under uncertainty. • This calls for an agent that cannot only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment. • Machine perception is the ability to use input from sensors (such as cameras, microphones, sensors, etc.) to deduce aspects of the world. e.g., Computer Vision. cont .
  • 9. High-profile examples of AI include • Autonomous vehicles (such as drones and self- driving cars), • Medical diagnosis, targeting • Creating art (such as poetry), • Proving mathematical theorems, • Playing games (such as chess or go), • Search engines (such as google search), • Online assistants (such as siri), • Image recognition in photographs, • Spam filtering, • Prediction of judicial decisions and advertisements online 69
  • 10. AI, Big Data and GPU 10 •AI is the development of machines able to perform tasks normally requiring human intelligence such as: visual perception, speech recognition, decision-making and translation between languages. •The advent of big data combined with cheaper and more powerful hardware such as Graphical Processing Units (GPUs) has enabled AI to evolve into more complex architectures. •GPU: able to process tasks in parallel fashion
  • 11. Machine learning 11 •Machine learning was introduced by Arthur Samuel in 1959. •Machine Learning is an advanced form of AI where the machine can learn as it goes rather than having every action programmed by humans. •ML, fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience.
  • 17. Deep learning Neural networks are biologically inspired networks that extract features from the data in a hierarchical fashion. The field of neural networks with several hidden layers is called deep learning. 17 Figure 3.1 Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
  • 19. Why we Need AI at this time? 19 1.To create expert systems that show intelligent behavior with the capability to learn, demonstrate, explain and advice its users. 2.Helping machines find solutions to complex problems like humans do and applying them as algorithms in a computer-friendly manner.
  • 20. Goals of Artificial Intelligence 1.Replicate human intelligence 2. Solve Knowledge-intensive tasks 3.An intelligent connection of perception and action 4.Building a machine which can perform tasks that requires human intelligence such as: •Proving a theorem • Playing chess •Plan some surgical operation •Driving a car in traffic 5.Creating some system which can show intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user. 73
  • 21. Disciplines which AI requires 21 To make a machine learn and make a decision like humans do, AI requires the knowledge of some disciplines. Write down some disciplines which AI requires?
  • 22. Disciplines which AI requires 22 • Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving, perception, language understanding. • To achieve those factors for a machine or software, Artificial Intelligence requires the following disciplines Figure 3.2 Artificial Intelligence is multidisciplinary
  • 23. Advantages of Artificial Intelligence 23 •High Accuracy with fewer errors: •High-Speed: •High reliability •Useful for risky areas: e.g. bomb area •Digital Assistant: e.g. E-commerce website •Useful as a public utility e.g. self driving car
  • 24. Disadvantages of Artificial Intelligence 24 •High Cost: The HW and SW requirement of AI is very costly •Can't think out of the box: as the robot will only do that work for which they are trained, or programmed. •No feelings and emotions:. •Increase dependence on machines: •No Original Creativity:
  • 26. 26
  • 27. Types of AI • There are mainly two types of the main categorization which are based on capabilities and based on functionally of AI. 7 9 capabilities functionally
  • 28. A. Based on Capabilities 28 Weak AI or Narrow AI: • Narrow AI is a type of AI which is able to perform a dedicated task with intelligence. • Narrow AI cannot perform beyond its field or limitations, as it is only trained for one specific task. • Hence it is also called as weak AI. • Narrow AI can fail in unpredictable ways if it goes beyond its limits. • e.g. Apple Siri, Google translate, playing chess, purchasing suggestions on e-commerce sites, self-driving cars, speech recognition, and image recognition.
  • 29. Cont . 29 General AI: • General AI is a type of intelligence that could perform any intellectual task with efficiency like a human. o also known as strong AI • The idea behind the general AI to make such a system that could be smarter and think like a human on its own. • Currently there is no such system existing which could come under general AI and can perform any task as perfect as human. • The worldwide researchers are now focused on developing machines with General AI.
  • 30. Super AI 30 • Super AI is a level of Intelligence of Systems at which machines could surpass human intelligence, • and can perform any task better than a human with cognitive properties. • This refers to aspects like general wisdom, problem solving and creativity. It is an outcome of general AI. • key characteristics of strong AI include capability include the ability to think, to reason solve the puzzle, make judgments, plan, learn, and communicate on its own. • Super AI is still a hypothetical concept of AI.
  • 31. Currently existing level of Intelligence 31 • Many currently existing systems that claim to use AI are actually operating as weak AI, focus on narrowly defined specific problems. • We are now at the stage of ANI, we haven’t actually reach AGI & ASI.
  • 32. B. Based on the functionality 32 1. Reactive Machines • Purely reactive machines are the most basic types of Artificial Intelligence. • Such AI systems do not store memories or past experiences for future actions. • These machines only focus on current scenarios and react on it as per possible best action. • Example: IBM's Deep Blue system, Google's AlphaGo
  • 33. Cont . 33 2. Limited Memory • Limited memory machines can store past experiences or some data for a short period of time. • These machines can use stored data for a limited time period only. E.g. Self-driving cars • These cars can store the recent speed of nearby cars, the distance of other cars, speed limits, and other information to navigate the road.
  • 34. . 34 3. Theory of Mind • Theory of Mind AI should understand human emotions, people, beliefs, and be able to interact socially like humans. • This type of AI machine is still not developed, but researchers are making lots of efforts and improvements for developing such AI machines. 4. Self-Awareness • Self-awareness AI is the future of Artificial Intelligence. • These machines will be super intelligent and will have their own consciousness, sentiments, and self-awareness. • These machines will be smarter than the human mind.
  • 35. How humans think 35 •Observe and input the information or data in the brain. • Interpret and evaluate the input that is received from the surrounding environment. •Make decisions as a reaction towards what you received as input and interpreted and evaluated.
  • 36. Activity 36 • Is it possible to map the way of human thinking to artificial intelligence components? If your answer is yes, how?
  • 37. How human think Mapping human thinking to AI components or layers Observe and input the information or data in the brain based on human organs such as eyes, ears, sensing layer, which perceives information from the surrounding environment. such as voice recognition for sensing voice and visual imaging recognition for sensing images. Interpret and evaluate the input that is received from the surrounding environment. interpretation layer, that is, reasoning and thinking about the gathered input that is acquired by the sensing layer. Make decisions or taking action as a reaction towards what you received as input and interpreted and evaluated. interacting layer taking action or making decisions. E.g. Robotic movement control and 88 speech generation.
  • 38. Influencers of artificial intelligence •List down some influential factors that accelerate the rise of AI? •Big data: Structured data versus unstructured data •Advancements in computer processing speed and new chip architectures •Cloud computing and APIs •The emergence of data science 89
  • 39. Big Data • Big data refers to huge amounts of data. • Big data requires innovative forms of information processing to draw insights, automate processes, and help decision making. • Big data can be structured data that corresponds to a formal pattern, such as traditional data sets and databases. • Also, big data includes semi-structured and unstructured formats, such as word-processing documents, videos, images, audio, presentations, social media interactions, streams, web pages, and many other kinds of content. 90
  • 40. Structured data versus unstructured data 40 • structured data: information with an organized structure, such as a relational database that is searchable by simple and straightforward search engine algorithms or SQL statements. • But, real-world data such as the type that humans deal with constantly does not have a high degree of organization. • For example, text that is written or spoken in natural language (the language that humans speak) does not constitute structured data. • Unstructured data is not contained in a regular database and is growing exponentially, making up most of the data in the world.
  • 41. The emergence of data science 41 • The goal of data science is to extract knowledge or insights from data in various forms, either structured or unstructured, which is like data mining. • Data science has emerged in the last few years as a new profession that combines several disciplines, such as statistics, data analysis, machine learning, and others.
  • 42. Advancements in computer processing speed, new chip architectures, and big data file systems. 42 • Significant advancements in computer processing and memory speeds enable us to make sense of the information that is generated by big data more quickly. • In the past, statisticians and early data scientists were limited to working with sample data sets. • In recent years, big data and the ability to process a large amount of data at high speeds have enabled researchers and developers to access and work with massive sets of data
  • 43. 43 • Processing speeds and new computer chip architectures contribute to the rapid evolution of AI applications. • The meaning of big data expanded beyond the volume of data after the release of a paper by Google on MapReduce and the Google File System (GFS), which evolved into the Apache Hadoop open-source project. • The Hadoop file system is a distributed file system that may run on a cluster of commodity machines, where the storage of data is distributed among the cluster and the processing is distributed too. Cont .
  • 44. 44 • This approach determines the speed with which data is processed. • This approach includes an element of complexity with the introduction of new, structured, unstructured, and multi-structured data types. • Large manufacturers of computer chips such as IBM and Intel are prototyping “brain-like” chips whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses. Cont .
  • 45. 45 • Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. • Cloud computing is a general term that describes the delivery of on-demand services, usually through the internet, on a pay-per-use basis. • Companies worldwide offer their services to customers over cloud platforms. • These services might be data analysis, social media, video storage, e-commerce, and AI capabilities that are available through the internet and supported by cloud computing. Cloud Computing
  • 46. 46 • An application programming interface (API), is a computing interface that defines interactions between multiple software intermediaries. It defines the kinds of calls or requests that can be made, how to make them, the data formats that should be used, the conventions to follow, etc. • In general, application programming interfaces (APIs) expose capabilities and services. APIs enable software components to communicate with each other easily. • APIs abstract the underlying workings of a service, application, or tool, and expose only what a developer needs, so programming becomes easier and faster. • AI APIs are usually delivered on an open cloud-based platform on which developers can infuse AI capabilities into digital applications, products, and operations by using one or more of the available APIs. Cloud Computing
  • 47. 47 • All the significant companies in the AI services market deliver their services and tools on the internet through APIs over cloud platforms, for example: o IBM delivers Watson AI services over IBM Cloud. (https://www.ibm.com/watson/about) o Amazon AI services are delivered over Amazon Web Services (AWS). (https://aws.amazon.com/) o The leading cloud provider in the marketplace is Amazon Web Services. It provides over 170 AWS services to the developers so they can access them from anywhere at the time of need. o Microsoft AI tools are available over the MS Azure cloud. o Google AI services are available in the Google Cloud Platform. Cloud Computing
  • 48. Application of AI 48 • Artificial Intelligence has various applications in today's society. • It is becoming essential for today's time because it can solve complex problems • in an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. • AI is making our daily life more comfortable and faster.
  • 49. Applications of AI cont. 49 AI in agriculture • Agriculture is an area that requires various resources, labor, money, and time for the best result. • Now a day's agriculture is becoming digital, • Agriculture is applying AI as agriculture robotics, solid and crop monitoring, predictive analysis. • AI in agriculture can be very helpful for
  • 50. AI in Healthcare 50 •Healthcare Industries are applying AI to make a better and faster diagnosis than humans. •AI can help doctors with diagnoses and can inform when patients are worsening so that medical help can reach the patient before hospitalization.
  • 51. AI in education 51 •AI can automate grading so that the tutor can have more time to teach. •AI chatbot can communicate with students as a teaching assistant. •AI in the future can be work as a personal virtual tutor for students, which will be accessible easily at any time and any place
  • 52. • AI in Gaming • AI in Finance and E-commerce • AI in Data Security • AI in Social Media • AI in Travel &Transport • AI in Robotics • AI in Entertainment • AI in the Automotive Industry 52 Cont .
  • 53. AI tools and Platforms 53 •Reading assignment