Artificial intelligence

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Compiled By - Biniam Behailu

CHAPTER – 3
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
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 Explain what artificial intelligence (AI) is.
 Describe the eras of AI.
 Explain the types and approaches of AI.
 Describe the applications of AI in health, agriculture,
business and education
 List the factors that influenced the advancement of AI in
recent years.
 Understand the relationship between the human’s way of
thinking and AI systems
 Identify AI research focus areas.
 Identify real-world AI applications, some platforms, and
tools.

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“Artificial intelligence is now in our living rooms, cars,
and often our pockets. “

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Introduction to Emerging Technologies------------ Compiled by Biniam Behailu

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 Artificial Intelligence 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.“
 The term AI was introduced by Prof. John McCarthy at a conference at
Dartmouth College in 1956.
 McCarthy defines AI as the “science and engineering of making
intelligent machines, especially intelligent computer programs”.

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 Artificial Intelligence (AI) is the branch of computer science by which
we can create intelligent machines which can behave like a human,
think like humans, and able to make decisions.
 AI systems exhibit characteristics of human intelligence.
 Artificial intelligence (AI) is the simulation of human intelligence
processes by machines, especially computer systems.

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 AI-based machines are intended to perceive their environment and
take actions that optimize their level of success.
 The Intelligence is composed of:
 Learning
 Reasoning
 Problem Solving
 Perception
 Linguistic Intelligence

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 AI research uses techniques from many fields, such as linguistics,
economics, and psychology.
• Control systems
• Natural language processing
• Facial recognition
• Speech recognition
• Business analytics
• Pattern matching
• Data mining
• Psychology etc…

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 Machine Learning is an advanced form of
AI where the machine can learn as it goes
rather than having every action
programmed by humans.
 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.

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 To create expert systems that exhibit intelligent behavior with the
capability to learn, demonstrate, explain and advice its users.
 Helping machines find solutions to complex problems like humans do
and applying them as algorithms in a computer-friendly manner.

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 Replicate human intelligence
 Solve Knowledge-intensive tasks
 An intelligent connection of perception and action
 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
 Creating some system which can exhibit intelligent behavior, learn new
things by itself, demonstrate, explain, and can advise to its user.

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 To achieve the intelligence factors for a machine or software AI
requires the following disciplines

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 High Accuracy with fewer errors
 High speed
 High reliability
 Useful for risky areas
 Digital Assistant
 Useful as a public utility

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 What could be the disadvantage of AI?
 Computational Costs
 Unemployment
 Can’t think outside of the box
 Increase dependence on machines
 Potential for miss use
 Artificial Super Intelligence

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 During the Second World War, noted British computer scientist Alan
Turing worked to crack the ‘Enigma’ code which was used by German
forces to send messages securely.
 Alan Turing and his team created the Bombe machine that was used
to decipher Enigma’s messages.
 The Enigma and Bombe Machines laid the foundations for Machine
Learning.
 In 1956, American computer scientist John McCarthy organized the
Dartmouth Conference, at which the term ‘Artificial Intelligence’ was
first adopted.

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 In the late 1990s, American corporations once again became interested
in AI.
 The Japanese government unveiled plans to develop a fifth generation
computer to advance of machine learning.
 AI enthusiasts believed that soon computers would be able to carry on
conversations, translate languages, interpret pictures, and reason like
people.

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 In 1997, IBM’s Deep Blue defeated and became the first computer to
beat a reigning world chess champion, Garry Kasparov.

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Stage 1- Rule based Systems
 Simplest form of AI
 Business software (Robotic Process Automation) and domestic
appliances to aircraft autopilots.
Stage 2- Context Awareness and Retention
 Algorithms that develop information about the specific domain they
are being applied in.
 Well, known applications of this level are chatbots and “roboadvisors”.

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Stage 3- Domain specific Expertise
 Going beyond the capability of humans, these systems build up
expertise in a specific context taking in massive volumes of
information which they can use for decision making.
 Successful use cases have been seen in cancer diagnosis and the well
known Google Deepmind’s AlphaGo.
Stage 4- Reasoning Machines
 These algorithms have some ability to attribute mental states to
themselves and others they have a sense of beliefs, intentions,
knowledge, and how their own logic works.
 This means they could reason or negotiate with humans and other
machines.

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Stage 5- Self aware systems/ Artificial General Intelligence (AGI)
 These systems have human-like intelligence – the most commonly
portrayed AI in media – however, no such use is in evidence today.
 It is the goal of many working in AI and some believe it could be
realized already from 2024.
Stage 6- Artificial Super Intelligence (ASI)
 AI algorithms can outsmart even the most intelligent humans in every
domain.

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Stage 7- Singularity and Transcendence
 Human augmentation could connect our brains to each other and to a
future successor of the current internet, creating a “hive mind” that
shares ideas, solves problems collectively, and even gives others
access to our dreams as observers or participants.

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1. Weak AI/ Artificial Narrow Intelligence (ANI)
 Weak AI, also known as narrow AI.
 This approach is not concerned about whether the AI systems display
human-like cognitive functions; the focus is on AI systems that
perform specific tasks accurately and correctly.
 It focuses on a specific task.
 The strength of ANI is that it focuses on doing something extremely
well, sometimes exceeding a human’s capabilities.
 ANI is a good fit for automating simple and repetitive tasks.
Based on their Capabilities

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 Examples of ANI are bots and virtual assistants, such as Siri, Microsoft
Cortana, Amazon Alexa, restaurant recommendations, weather
updates, Watson DeepQA, and customer services chatbots for
answering simple and repetitive customer inquiries.

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2. Strong AI/ Artificial General Intelligence (AGI)
 AGI belongs to the strong AI.
 It refers to computer systems that exhibit capabilities of the human
brain.
 Artificial General Intelligence is the ability of an AI agent to learn,
perceive, understand, and function completely like a human being.
 AGI refers to systems or machines that can generally perform any
intellectual task that a human can do.

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 The three largest projects working on AGI are DeepMind, the Human
Brain Project (an academic project that is based in Lausanne,
Switzerland), and OpenAI.

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3. Super/ Artificial Super Intelligence (ASI)
 ASI refers to machines that surpass humans in general intelligence.
 Nick Bostrom, defines ASI as “an intellect that is much smarter than
the best human brains in practically every field, including scientific
creativity, general wisdom and social skills.”
 The unique capabilities of the human brain are the reason why
humans have a dominant position over other species.
 Super intelligent machines might surpass the human brain in general
intelligence.
 Regarding ASI, many prominent scientists and technologists have
ethical concerns about the future of humanity and intelligent life.

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Can we create Super Intelligence ?

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1. Reactive Machines
 Purely reactive machines are the most basic types of Artificial
Intelligence.
 AI systems do not store memories or past experiences for future
actions.
 Machines only focus on current scenarios and react on it as per
possible best action.
Example : IBM’s Deep Blue
Based on their Functionality

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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.
 Self-driving cars are one of the best examples of Limited Memory
systems. These cars can store the recent speed of nearby cars, the
distance of other cars, speed limits, and other information to navigate
the road.

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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.

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 Intelligence or the cognitive process is composed of three main
stages:
• 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 an input
and interpreted and evaluated.
 Simulating the same stages in building AI systems or models
represents the main three layers or components of AI systems.

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 Because AI is the science of simulating human thinking, it is possible to
map the human thinking stages to the layers or components of AI
systems.

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 Acquire information from
their surrounding
environments through
human senses.
• Hearing and
• Sight senses
1st stage
Humans AI
 Represented by the sensing
layer, which perceives
information from the
surrounding environment.
• voice recognition for sensing
voice
• visual imaging recognition for
sensing images

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 Interpreting and evaluating
the input data
 Human brain
2nd stage
Humans AI
 Represented by the
interpretation layer
 Reasoning and thinking about
the gathered input that is
acquired by the sensing layer.

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 Taking action or making
decisions
3rd stage
Humans AI
 After evaluating the input data,
the interacting layer performs
the necessary tasks.
 Robotic movement control and
speech generation are
examples of functions that are
implemented in the interacting
layer.

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 Big data
 Cloud computing and APIs
 Emergence of data science
 Advancements in computer processing speed and new chip
architectures

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 Cloud computing is a general term that describes 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 platform capabilities, such as availability, scalability,
accessibility, rapid deployment, flexible billing options, simpler
operations, and management.

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Compiled By - Biniam Behailu

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 All the significant companies in the AI services market deliver their
services and tools on the internet through APIs over cloud platforms.
 API is a software intermediary that allows two applications to talk to
each other.
• IBM delivers Watson AI services over IBM Cloud.
• Amazon AI services are delivered over Amazon Web Services
(AWS).
• Microsoft AI tools are available over the MS Azure cloud.
• Google AI services are available in the Google Cloud Platform.

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 Transportation
 Home services and robots
 Healthcare
 Education
 Public Safety and Security
 Employment and workplace
 Entertainment
 Agriculture
 Banking, Financial Services and Insurance (BFSI)
 Manufacturing
 Oil and Gas

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Transportation
 Self-driven vehicles, such as
driverless cars and unmanned
ground vehicles (UGVs).
 Vehicles that can sense their
environment and navigate
without human input

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Home services and robots
 Home services and robots have already
entered people’s homes in the form of
vacuum cleaners and personal assistants.
 Drones are already delivering packages.

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Healthcare
 In healthcare, there has been a huge forward leap in collecting useful
data from personal monitoring devices and mobile apps, electronic
health records (EHRs) in clinical settings, surgical robots that assist
with medical procedures, and service robots that support hospital
operations.

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Public Safety and Security
 Improved cameras and drones for surveillance, algorithms to detect
financial fraud, and predictive policing.

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 Microsoft AZURE Machine Learning
 Google Cloud Prediction API,
 TensorFlow,
 Infosys Nia,
 Wipro HOLMES,
 API.AI,
 Premonition…

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 Machine learning
 Natural language processing (NLP)
 NLP is the subfield of AI that applies computational techniques to
analyze and synthesize human natural language and speech.
 Computer vision (CV)
 Business analytics

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NLP is found today in the following types of applications
 Machine translation
 Search engines, such as Google and Baidu
 Spell checkers IBM Watson
 Natural language assistants, such as Siri
 Translation systems, such as Google translate
 News digest, such as Yahoo

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Sample AI Applications
 Facebook - When you upload photos to Facebook, the service
automatically highlights faces and suggests friends to tag.
 Pinterest - Pinterest uses computer vision, an application of AI where
computers are taught to “see,” in order to automatically identify
objects in images (or “pins”) and then recommend visually similar pins
 Instagram – Instagram uses machine learning to identify the contextual
meaning of emoji, which have been steadily replacing slang (for
instance, a laughing emoji could replace “lol”)
 Snapchat - Snapchat introduced facial filters, called Lenses, in 2015.
These filters track facial movements, allowing users to add animated
effects or digital masks that adjust when their faces moved.

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“If you’re not concerned about AI safety, you should be. Vastly
more risk(y) than North Korea.”
Elon Musk

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