The document discusses the evolution and approaches of artificial intelligence (AI). It begins with a brief history of AI from its origins in 1956 to recent decades where machine learning has been applied successfully. Four main approaches to AI are described: reactive machines, limited memory, theory of mind, and self-awareness. The document provides examples of each approach. It also discusses what skills and topics engineers should understand to work in AI, such as programming languages, algorithms, and deep learning. Emerging technologies related to AI like robotics, blockchain, and cybersecurity are outlined. Finally, some key ethical concerns regarding AI like job loss, imperfect systems, and bias are listed.

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Ms SURBHI SAROHA

2.  The evolution of AI to the present.
 Various approaches to AI.
 What should all engineers know about AI?
 Other emerging technologies
 AI and ethical concerns.
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3.  In the past few years, AI evolved into a
powerful tool that enables machines to think
and act like humans.
 Moreover, it has garnered focus from tech
companies around the world and is
considered as the next significant
technological shift after the evolution in
mobile and cloud platforms.
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4.  The first patent for the invention of the
telephone happened in 1876 and AI was
introduced at a much later stage.
 In true terms, the field of AI research was
founded at a workshop held on the campus
of Dartmouth College during the summer of
1956.
 At that time, it was predicted that a
machine as intelligent as a human being
would exist in no more than a generation and
they were given millions of dollars to make
this vision come true.
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5.  Investment and interest in AI rose in the first
decades of the 21st century.
 From that time, Machine Learning was
successfully applied to many problems in
academia and industry due to the presence
of a powerful computer.
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6.  So now this concept has been around for decades
but, until 1950, people were unaware of the
term.
 John McCarthy who is known as the founder of
Artificial Intelligence introduced the term
‘Artificial Intelligence’ in the year 1955.
 McCarthy along with Alan Turing, Allen Newell,
Herbert A. Simon, and Marvin Minsky is known as
the founding fathers of AI.
 Alan suggested that if humans use available
information, as well as reason, to solve problems
to make decisions – then why can’t it be done
with the help of machines?
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7.  Gradually with time, the wave of computers
started.
 With time, they became faster, more
affordable and able to store more
information.
 The best part was that they could think
abstractly, able to self-recognize and
achieved Natural Language Processing.
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8.  In 1980, AI research fired back up with an expansion
of funds and algorithmic tools.
 With deep learning techniques, the computer learned
with the user experience.
2000’s – Landed to the Landmark
 After all the failed attempts, the technology
was successfully established but, until it was in the
2000s that the landmark goals were achieved.
 At that time, AI thrived despite a lack of government
funds and public attention.
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9.  Based on the ways the machines behave,
there are four types of Artificial Intelligence
approaches –
 Reactive Machines,
 Limited Memory,
 Theory of Mind, and
 self-awareness.
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10.  These machines are the most basic form of AI
applications.
 Examples of reactive machines are games like
Deep Blue, IBM’s chess-playing supercomputer.
 This is the same computer that beat the world’s
then Grand Master Gary Kasparov.
 The AI teams do not use any training sets to feed
the machines, nor do the latter store data for
future references.
 Based on the move made by the opponent, the
machine decides/predicts the next move.
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11.  These machines belong to the class II
category of AI applications. Self-driven cars
are the perfect example. These machines are
fed with data and are trained with other
cars’ speed and direction, lane markings,
traffic lights, curves of roads, and other
important factors, over time.
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12.  This is where we are, struggling to make this
concept work, however, we are not there yet.
 Theory of mind is the concept where the bots
will be able to understand the human emotions,
thoughts, and how they react to them.
 If the AI-powered machines are ever to mingle
with us and move around with us, understanding
human behavior is imperative.
 And then, reacting to such behaviors accordingly
is the requirement.
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13.  These machines are the extension of the Class III
type of AI.
 It is one step ahead of understanding human
emotions.
 This is the phase where the AI teams build
machines with self-awareness factor
programmed in them.
 Building self-aware machines seem far-fetched
from where we stand today.
 Here’s an instance. When someone is honking
from behind, the machines should be able to feel
the emotion.
 That’s when they understand how it feels when
they honk at someone back.
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14.  If you are researching on how to become an
AI engineer, you need to up your software
development techniques and practices, along
with your programming skills. Make sure that
you are theoretically and practically well-
informed in the following topics:
 Programming languages
 Statistical knowledge
 Applied Maths and Algorithms
 Natural Language Processing
 Deep Learning & neural networks
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15.  To become a successful AI Engineer, you have to
become proficient in a few programming
languages. You need to pick one or more
languages that will help you explore and
implement the capabilities of AI. A few of the
many languages that work well with AI are:
 Python
 Java
 C++
 Lisp
 R
 Prolog, etc.
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16.  As an AI engineer your job responsibilities
will include:
 Organizing operations between Data
Scientists and Business Analysts
 Infrastructure automation for Data Science
Team
 Developing ML models into APIs for
applications to access
 Testing and deploying models
 Automating processes with ML
 Advancing minimum viable products
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17.  AI Software Engineer is one of the most popular
job roles among the mushrooming AI vacancies.
Current AI Engineer vacancies are jutting out in:
 Amazon
 Accenture
 IBM
 NVIDIA
 Microsoft
 Intel
 Facebook
 Lenovo
 Samsung
 Adobe
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18.  Robotics is one of the most advanced and
emerging technologies in the modern technology
landscape.
 It refers to the study of robot technology and an
interdisciplinary field of science and engineering
dedicated to the design, construction, and use of
robots.
 Robotics makes use of disciplines such as
dynamic system modeling and analysis,
mathematics, biology, physics, mechanical
engineering, electrical and electronic
engineering, computer science and engineering,
and automation technology.
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19.  Comprises a network of physical devices,
automobiles, home appliances, and all those items
that are connected to the Internet.
 IoT provides a platform that creates opportunities
for people to connect smart devices such as
actuators, electronics, sensors, and others and
control them with big data technology.
 The technology consists of the extension of internet
connectivity beyond personal computers and mobile
devices.
 IoT can also reach a wide range of non-internet
enabled devices.
 Almost every industry is making use of the Internet
of Things to monitor activities and advance their
existing systems.
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20.  Draws its name from the cryptographically
encrypted chunks (called blocks) where
information is stored.
 This information is encrypted data, which is a
cryptographic hash map of the previous data,
timestamp, and new data.
 The next successive block contains information
about the last block by forming a chain, hence
the name.
 Blockchain provides an architecture that allows
us to trust on a decentralized system (Internet or
Web) rather than trusting any actor within it.
 It is a ledger that is shared between multiple
entities that everyone can inspect, but not any
single user can control it.
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21.  Cybersecurity refers to the practice of
safeguarding networks, servers, devices,
programs, and data from hackers.
 The increasing digitization across every
industry, which delivers enhanced advantages
to businesses also draws significant
cybersecurity challenges.
 These challenges may include application
security, network security, information
security, operational security, and end-user
security.
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22.  5G
 5G offers improvements over 4G, such as low latency,
intelligent power consumption and high device density. 5G
will make augmented reality, smart cities and connected
vehicles possible.
 IoT
 The Internet of Things combines information from
connected devices and allows for analytics of systems.
These platforms, devices and datasets provide additional
insights and efficiencies for the enterprise.
 Serverless Computing
 Serverless computing, or Function as a Service (FaaS),
allows companies to build applications that scale in real
time so that they can respond to demand that can change
instantly depending on orders of magnitude. FaaS offers a
consumption-based platform so that developers can
quickly and cost effectively deploy applications.
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23.  Biometrics
 Security will be improved by biometics by
allowing people and devices to authenticate
and move seamlessly through the world.
 Augmented Reality/Virtual Reality
 AR and VR transform how people engage with
machines, data and each other. The
enterprise is using mixed reality, AI and
sensor technologies to enhance execution
flexibility, operational efficiency and
individual productivity.
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24.  Blockchain
 There's an ever-increasing need to be able to secure and manage
transactions across the internet, and blockchain is the answer. Blockchain
manages data and supply chain challenges.
 Robotics
 Robotics are shifting from industrial use to service delivery and are
impacting home and businesses, both physically and virtually.
 Natural Language Processing
 NLP is a field of AI that enables computers to analyze and understand
human language. Speech-to-text converts human language into a
programming language. Text-to-speech converts a computer operation to
an audible response.
 Quantum Computing
 Our ability to process and analyze big data will be impacted by quantum
computing. It is the key to leveraging machine learning and the power of
AI.
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25.  A robot vacuum is one thing,
but ethical questions around AI in medicine,
law enforcement, military defense, data
privacy, quantum computing, and other areas
are profound and important to consider.
 One of the primary concerns people have
with AI is future loss of jobs.
 1. Job Loss and Wealth Inequality
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26.  One issue related to job loss is wealth inequality.
 Consider that most modern economic systems
require workers to produce a product or service
with their compensation based on an hourly
wage.
 The company pays wages, taxes and other
expenses, with left-over profits often being
injected back into production, training and/or
creating more business to further increase
profits.
 In this scenario, the economy continues to grow.
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27.  But what happens if we introduce AI into the
economic flow? Robots do not get paid hourly nor
do they pay taxes.
 They can contribute at a level of 100% with low
ongoing cost to keep them operable and useful.
 This opens the door for CEOs and stakeholders to
keep more company profits generated by their AI
workforce, leading to greater wealth inequality.
 Perhaps this could lead to a case of “the rich” —
those individuals and companies who have the
means to pay for AIs — getting richer.
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28.  AI is Imperfect — What if it Makes a
Mistake?
 Should AI Systems Be Allowed to Kill?
 Rogue Ais
 Singularity and Keeping Control Over Ais
 How Should We Treat AIs?
 AI Bias
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29.  THANK YOU 
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