All About Introduction to Artificial IntelligenceAI.pptx

MICROSOFT ACCENTURE
ALL ABOUT AI
AIF101

05
04
01
TOPICS TO BE COVERED
Origins of AI: From
Turing to Neural
Networks
AI Safety: Addressing
Fear and Uncertainty
Evolution of AI: Expert
Systems to Deep
Learning
02
03
Major Milestones in AI:
From Logic Theorist to
AlphaGo
Significance of AI in
Modern Technology and
Society
06
Separating Fact from
Fiction: Understanding
AI's True Potential

09
TOPICS TO BE COVERED
Reskilling and Upskilling:
Adapting to AI in the
Workplace
07
08
AI and Employment Trends:
Automation, Augmentation,
and New Opportunities
Understanding the
Distinctions Between
Generative AI and
Traditional AI

Origins of AI:
From Turing to
Neural
Networks
01

Activity: AI or Not AI?
Text
Virtual
Assistants
Search Engine
Automated Spam
Filters
Autonomous Cars

Activity: AI or Not AI?
GPS Systems Face Recognition
Technology
YouTube
Recommendations
Text-to-video
Generation Models

Origins of Artificial Intelligence
Origins of AI are beyond 20th-century laboratories
Rooted in human imagination and ingenuity
Ancient Myths and Automata (Pre-20th Century)
• Fascination with creating artificial beings
• Greek Mythology: Pygmalion and Galatea
• Medeia and Talus
Illustration of bringing inanimate objects to
life
• Ancient Chinese and Egyptian tales
• Mechanical figures performing tasks.
Medeia and Talus by
illustrator Sybil
Tawse

A Humanoid Sketch by
Leonardo Da Vinci
Renaissance and the Age of
Enlightenment (16th-18th
Century)
• Inventors like Leonardo da Vinci
made sketches of humanoid robots
and mechanical knights
• Early attempts to mimic human
movements and behaviors

Automata by Jazari, Pierre Jaquet-Droz, and
Wolfgang von Kempelen,

Video: We’re already using AI more
than we realize
https://www.youtube.com/watch?v=YsZ-lx_3eoM

Major Milestones
in AI: From
Logic Theorist
to AlphaGo
02

History of Artificial
Intelligence
Ada Lovelace and the
Analytical Engine at the
Science Museum
Ada Lovelace and the Analytical Engine
(19th Century)
• In the 19th century, Ada Lovelace, an English
mathematician and writer, made a groundbreaking
contribution.
• She wrote the first algorithm intended for
implementation on a machine.
• Lovelace's work laid the foundation for computer
programming and is considered the birth of computer
science.

History of Artificial
Intelligence
1936: Alan Turing & The Turing
Machine
Alan Turing and the Turing Test (20th
Century)
• Alan Turing: British mathematician and computer
scientist
• Introduced Turing Test in 1950 paper "Computing
Machinery and Intelligence"
• Turing Test: Questioned whether machines could exhibit
human-like intelligence
• Sparked debates and experiments
Formal birth of AI as a field of study
emerged in mid-20th century

Evolution of AI
The 1950s
were a
time of
post-
World War
II
scientifi
c
optimism.
Mathemati
cians,
engineers
, and
visionari
es began
to
explore
the
concept
of
creating
machines
that
could
Formal
Birth
of AI
(1950s
):
The 1950s were a time of post-World War II
scientific optimism.
Mathematicians, engineers, and visionaries began to
explore the concept of creating machines that could
simulate human intelligence.
This marked the formal beginning of AI as a distinct
field of study.
Formal Birth of AI (1950s):

Early AI Milestones: Pioneering Achievements
In 1956,
Allen
Newell
and
Herbert
A.
Simon,
along
with
their
colleagu
es,
created
the
Logic
Theorist
.
It was
one of
The
Logic
Theori
st
(1956)
:
Logic Theorist , the first
running artificial
intelligence program

Logic Theorist , the first
running artificial
intelligence program
-
Develop
ed by
Allen
Newell
and
Herbert
A.
Simon,
the
General
Problem
Solver
(GPS)
was a
groundb
reaking
program
designe
d to
solve a
The
Gener
al
Probl
em
Solve
r
(GPS)
(1957
):

LISP Programming Language
Early
AI
Progr
ammin
g
Langu
ages:
LISP
(1958
):

Algorithm for Checkers game
Machine
learnin
g, an
AI
subset,
emerged
by
develop
ing
algorit
hms and
statist
ical
models
to
improve
from
data.
In
1959,
The
Birth
of
Machine
Learnin
g
(1950s-
1960s)

ELIZA Chatbot from 1966
In
1966,
Joseph
Weizenb
aum
created
the
world’s
first
chatbot
named
ELIZA.
ELIZA
was a
groundb
reaking
program
Year
1966:
ELIZA
— The
First
Chatbo
t

WABOT-1 — The First
Humanoid Robot
Japan
made a
signifi
cant
leap in
AI
develop
ment in
1972
when it
built
WABOT-
1, the
first
humanoi
d
robot.
Year
1972:
WABOT-
1 —
The
First
Humano
id
Robot

Video: The History of Artificial
Intelligence

Evolution of
Artificial
Intelligence
03

AI Winters: Periods of
Hibernation in AI Research
The years from 1974 to 1980 and from 1987 to 1993 witnessed AI winter, marked by
reduced funding and interest due to the high cost and limited efficiency of
existing AI technologies.

A Boom of AI (1980–1987)
After
the AI
winter,
AI made
a
comeback
with the
developm
ent of
expert
systems.
These
programs
were
designed
to mimic
the
decision
-making
abilitie
Year
1980:
The
Rise of
Expert
Systems

A Boom of AI (1980–1987)
Year
1986:
Rise of
Backpro
pagatio
n
Algorit
hm
The backpropagation
algorithm

The Emergence of Intelligent Agents
(1993–2011)
Garry Kasparov vs IBM Deep
Blue
In 1997,
IBM’s Deep
Blue made
headlines by
defeating
the world
chess
champion,
Gary
Kasparov.
This victory
showcased
AI’s ability
to excel in
complex
strategic
games and
opened new
possibilitie
s for AI
applications
.
Year
1997:
IBM
Deep
Blue’
s
Trium
ph

(1993–2011)
Google Search Engine
Introducing AI
In the year 2000,
Google started
incorporating AI-
powered search
algorithms into
its search
engine.
This
implementation
significantly
improved the
accuracy and
relevance of
search results.
This made Google
one of the
leading search
platforms
globally and
setting the stage
for AI’s
continued
integration into
various aspects
of technology and
everyday life.
Year
2000:
Googl
e
Searc
h
uses
AI

(1993–2011)
Roomba introduced in 2002
Year
2002:
AI in
Homes
—
Roomb
a

Deep Learning, Big Data, and
Artificial General Intelligence
(2011-Present)
Neural Network Used in AI
Year
2006:
Neural
Networ
ks
into
Deep
Learni
ng

(2011-Present)
Visual Recognition tasks
using ImageNet
Year
2010:
Visual
Recognit
ion
tasks
using
ImageNet

(2011-Present)
Watson and the Jeopardy!
Challenge
Year
2011:
IBM’s
Watso
n
Wins
Jeopa
rdy
https://
youtu.be/P18EdAKuC1U

(2011-Present)
Eugene Goostman fools the
Turing Test
Year
2014:
Eugene
Goostm
an and
the
Turing
Test

(2011-Present)
DeepMind’s AlphaGo defeating
the world champion Go
player, Lee Sedol.
Year
2016:
DeepMi
nd’s
AlphaG
O
defeat
ed
Champi
on

(2011-Present)
Waymo starts commercial
ride-share service
In 2018,
Waymo, a
subsidia
ry of
Alphabet
Inc.
(Google’
s parent
company)
, made
signific
ant
progress
in self-
driving
car
Year
2018:
Waymo(
Self
Drivin
g Car)

(2011-Present)
Open AI Chat GPT 3
Year
2022:
Open AI
Chat GPT

Significance of
AI in Modern
Technology and
Society
04

Video: Helping Solve Humanity's
Greatest Challenges with AI Tools

Activity: Identify AI
Around You.
Text

Applications of AI in Today’s Word
Advancements in Machine Learning
Increased Use of AI in Healthcare:
Facilitating High-Quality 3D Visualisation
Expansion of AI in Education
Implementation in Natural Language Processing
Increased Automation in Manufacturing
Processes
Improved Autonomous Vehicles
Advancing Event Management
Improves Efficiency and Productivity
Personalized Recommendations
Predictive Analytics
Enhanced Safety and Security

Advancements in Machine Learning
• Machine learning is the core of AI
technology, and we can expect
significant advancements in this field
in the future.
• This includes improvements in deep
learning algorithms.
• This can enable machines to learn more
complex tasks and understand the world
better.
• As machines become more capable of
learning from data and recognizing
patterns, they will be able to make
more accurate predictions and
facilitate smart decision-making.

Increased Use of AI in Healthcare
• AI is already making significant
strides in the healthcare industry.
• Going forward, it can help diagnose
diseases, develop personalized
treatment plans and improve patient
outcomes.
• Moreover, in the future, AI is expected
to become more integrated into
healthcare systems,
• Enables more efficient and accurate
diagnoses and treatments.

Increased Use of AI in Healthcare

Facilitating High-Quality 3D
Visualisation
• AI technology is increasingly being
used in 3D design applications to
enhance and streamline the design
process.
• AI algorithms can use generative design
to explore a wide range of design
possibilities and come up with
optimized solutions.
• They can analyze 3D scans to
automatically detect and correct
errors, such as missing or extra
geometry.
• As a result, they are expected to
assist designers to produce more
accurate and high-quality 3D models.

Video: A.I. Experiments:
Visualizing High-Dimensional Space

Increased Automation in
Manufacturing Processes
• Automation is critical to workplace
safety and promoting compliance with
industry regulations.
• It enhances operational efficiency and
safety by delegating hazardous tasks to
automated systems.
• You can create a safer environment for
your employees, avoiding workplace
accidents and injuries.
• Automation's data-driven approach
empowers manufacturers to make informed
decisions, optimise processes, and
minimise production lead times.

Advancing Event Management
• AI revolutionizing event management
industry: Automates and streamlines
tasks.
• Analyzes venue layouts and attendee
behavior.
• Optimizes placement of booths, signage,
etc.
• Improves attendee flow and engagement.
• Provides real-time data insights.
• Helps event organizers gauge attendee
engagement and satisfaction.

AI in Logistics and Supply Chain Industry
• AI brings robust optimization
capabilities
• Functions include demand forecasting,
predictive maintenance, and intelligent
decision-making
• Enhances productivity and operational
efficiencies
• Lowers supply chain costs
• Enables automation of manual and
repetitive tasks
• Automation technologies include RPA,
digital workers, warehouse robots, and
autonomous vehicles
• Supports quality checks automation and
back-office automation

AI Revolutionizes Logistics and
Supply Chain Industry

AI Automation in Logistics and Supply
Chain Industry

AI in Entertainment
Content Personalization
• AI used for content personalization on
streaming platforms like Spotify, Netflix,
and Amazon Prime Video
• Algorithms analyze user data for tailored
recommendations
• Improves user experience and engagement
Subtitle Generation
• AI enables quick and accurate subtitle
generation
• Makes content accessible to diverse
audiences
• YouTube example: automatic caption
generation for wider audience reach

AI in Entertainment
AI-Generated Music and Art
AI algorithms can generate musical
compositions and visual artwork that are
often indistinguishable from those created
by humans.
Amper Music, for example, is an AI-powered
music composition tool that allows users
to produce professional-quality tracks
without any musical expertise.
Similarly, Aiva Technologies offers AI-
driven music composition software that has
already been used in various films,
television shows, and commercials.
https://www.youtube.com/watch?v
=EyeW_axUEQU&pp=ygUQYWkgbXVzaWM
gZXhhbXBsZQ%3D%3D

AI in Entertainment
Film Production
AI was put to work in movie production
once in 2016 when IBM Watson used AI
technology to create the world’s first-
ever movie trailer for Fox’s Morgan.
This was the time that Fox wanted to wow
the audiences and keep them on the edge of
their seats with a frightening and
suspenseful trailer made by the power of
artificial intelligence.
Typically, the development of CGI, which
is extensively used in the film industry,
has been greatly influenced by artificial
intelligence.
https://www.youtube.com/watch?v=0bVBlWtjs0w&
pp=ygUNYWkgZmlsbSBzY2VuZQ%3D%3D

AI Platforms for Video-
making
Sora AI
•https://
openai.com/
index/sora
DeepArt
•https://
www.deeparteffects.
com/
Dall-E
•https://openai.com/
index/dall-e-3
Runway ML
•https://runwayml.com/

AI in Game Development
Speaking of AI in gaming, we are mentioning the
responsive and adaptive video game experiences.
AI-powered interactive experiences are usually
employed in games via NPCs (non-player
characters).
With this technology, NPCs can think and act
toward players’ behaviors.
Even better, they can predict what gamers are
going to do next by analyzing their previous
actions.
In brief, AI comes into video games with the
role of making the unrealistic gaming
environment vivid.
Together with virtual reality (VR) and augmented
reality (AR), AI can broaden the world of gaming
with more creative and immersive experiences.

AI in Education
AI-assisted learning methodologies are
transforming the education industry at
scale.
Using Artificial Intelligence, educational
institutions can personalize the learning
experience for students based on the data
collected from their test results,
exercise completion time, interaction with
educational materials, and overall
performance.
Besides personalized learning, AI can be
used to unburden teachers by automating
administrative tasks that are manual,
tedious, repetitive, and time-consuming in
nature, such as checking assignments, test
assessment, grading, and more.

AI in Real Estate
Artificial intelligence is transforming the
real estate industry by unlocking a sea of
opportunities for brokers, agents, and
clients.
Using Artificial Intelligence, real estate
professionals can analyze and predict
property valuations, rental yield, market
conditions, and other critical aspects
influencing the real estate market.
AI technology can be leveraged to provide a
3D view of the property to clients without
letting them step foot on the actual site.

AI in Manufacturing
AI has emerged as a game-changer in the
manufacturing industry by revolutionizing
operations across product assembly, inventory
management, quality assurance, equipment
predictive maintenance, and defect Inspection.
The impact of Artificial Intelligence in the
manufacturing industry is further fueled by the
Industry 4.0 revolution focussed on automation,
digital transformation, real-time data, and
interconnectivity.

Video: Artificial intelligence
comes to farming in India
https://
www.youtube.com/watch?v=JeU
_EYFH1Jk&pp=ygULYWkgaW4gaW5
kaWE%3D

Video: Microsoft AI technology
helps farmers feed the future

Video: Mapping the future of
our forests with Microsoft AI

AI Safety:
Addressing Fear
and Uncertainty
05

Short Term Concerns About AI
Societal Impact:
•AI technologies pose risks to personal privacy through enhanced
surveillance and data collection.
Loss of Privacy:
•AI systems can perpetuate and even exacerbate societal biases,
leading to unfair hiring and law enforcement treatment.
Bias and Discrimination:
•AI's capability to influence public opinion and consumer behaviour
raises concerns about misinformation and ethical implications.
Manipulation of Information
and Behaviour:
•AI’s role in critical decisions, such as healthcare and criminal justice,
may conflict with human ethical standards.
Ethical Concerns in
Decision-Making:
•The opaque nature of AI decision-making processes can hinder
accountability and trust in AI systems.
Lack of Transparency and
Accountability:

Short Term Concerns About AI
Economic/Technological Impact:
• AI-driven automation threatens traditional employment and
disrupts established job markets.
Job Displacement and Workforce
Disruption:
• AI could widen the income gap and contribute to social
inequality.
Economic Inequality and
Exacerbation of Poverty:
• AI enhances the potential for spreading misinformation and
conducting cyber-attacks, posing significant security risks.
Misinformation and Cyber Warfare:
• Integrating AI in critical systems introduces the risk of errors or
malicious exploitation.
Safety Risks Due to AI Errors or
Misuse:
• AI-driven innovations may render existing business models
obsolete, impacting small enterprises and traditional industries.
Market Disruption and
Obsolescence of Traditional
Businesses:

Long Term Concerns About AI
Societal Impact:
• Speculations about AI evolving into sentient beings raise
existential concerns about human safety and control.
Development of Sentient AI
and Potential Existential
Threat:
• AI-driven interactions might diminish meaningful human
connections, affecting mental health and community cohesion.
Erosion of Human
Relationships and Social
Connection:
• Increased reliance on AI could undermine the importance of
human creativity and skills.
Diminished Value of Human
Skills and Creativity:
• AI-generated content challenges traditional notions of intellectual
property and ownership.
Concerns About Intellectual
Property and Ownership of AI-
generated content:

Long Term Concerns About AI
Economic/Technological Impact:
• Excessive reliance on AI may erode essential human
cognitive abilities.
Overdependence on AI
Leads to Loss of Critical
Thinking and Problem-
Solving Skills.
• Advanced AI could operate autonomously, potentially
losing human oversight and unintended
consequences.
Loss of Human Control
Over Advanced AI Systems:
• Autonomous AI systems might make decisions that
result in unpredictable and possibly harmful outcomes.
Potential for Unforeseen
Consequences Due to
Autonomous AI Decision-
Making:

Need for Ethical Considerations in AI
Biased Facial Recognition:
•Commercial facial recognition systems exhibit higher error
rates for darker-skinned individuals and women.
•This bias can lead to wrongful arrests or discrimination
against marginalized communities.
Algorithmic Hiring Bias:
•AI algorithms in hiring processes may perpetuate biases
present in historical hiring data.
•Biased hiring practices can result from algorithms trained on
discriminatory data, leading to unequal opportunities.

Need for Ethical Considerations in AI
Autonomous Vehicles and Moral Dilemmas:
•Ethical questions arise regarding how AI should make decisions in
life-threatening scenarios.
•Resolving moral dilemmas, such as prioritizing human safety in
autonomous vehicle accidents, is crucial for public trust.
Deepfakes and Misinformation:
•AI-generated deepfake videos pose challenges for media
authenticity and truth.
•Deepfake technology can spread misinformation, erode trust, and
undermine the credibility of public figures and institutions.

Case Study: Project Maven
Pentagon initiative integrating AI into
military drones for surveillance.
Google's participation raised ethical
concerns internally and externally.
• Ethical Concerns Raised:
• Potential development of autonomous
weapons.
• Moral questions about AI's role in lethal
decision-making.
• Risk of contributing to global
instability.
• Internal Debate and Protests:
• Google employees protested, urging
withdrawal from the project.
• Over 3,000 employees signed a petition
against military involvement.

Case Study: Project Maven
• Google's AI Principles:
• Aim to be socially beneficial.
• Avoid creating or reinforcing unfair
biases.
• Be accountable to people.
• Uphold privacy and security standards.
• Impact and Lessons Learned:
• Highlighted the importance of ethical
considerations in AI development.
• Demonstrated the power of internal
activism in tech companies.
• Influenced broader industry discussions
on responsible AI use.

Video: Ethics of AI: Challenges
and Governance| UNESCO

Understanding AI's
True Potential
06

Industrial Revolution Phases Over
the Centuries

Global Impact of AI Revolution
According to a PwC analysis, AI is
expected to contribute $15.7 trillion to
the global economy by 2030.
McKinsey Global Institute estimates
suggest that AI could add 1.2% to annual
global GDP growth through 2030.
AI is transforming industries across the
board, including manufacturing,
healthcare, finance, retail, and
transportation, by streamlining processes,
improving efficiency, and enabling
innovative products and services.

Global Impact of AI Revolution
Global
Impact of
AI
Economic
Transformation
Healthcare
Education
Environmental
Sustainability
Transportation
Finance
Communication
and Language
Ethical and
Societal
Implication

AI and Economic Development
AI has the potential to significantly impact economic development by
driving productivity gains, fostering innovation, and creating new
opportunities for growth across various sectors.

Video: AI and Economic Development

AI Impact: Increased Productivity
AI technologies can automate routine
tasks, optimize processes, and
augment human capabilities, leading
to increased productivity in the
workplace.
By streaming operations and reducing
labour-intensive activities, AI can
free up resources, time and talent
for higher-value tasks, thereby
boosting economic efficiency and
competitiveness.
https://www.youtube.com/watch?v=HNl
8ELNrCuk&pp=ygUUYWkgYW5kIHBwcm9kdWN
0aXZpdHk%3D

AI Impact: Innovation and
Entrepreneurship
AI fuels innovation by enabling new
applications, business models and
market opportunities.
AI-driven technologies such as
machine learning, natural language
processing, and computer vision
empower entrepreneurs and startups
to develop novel products, services
and solution that address unmet
needs and create value in the
marketplace.

Innovation and Entrepreneurship

Upcoming Trends and Developments
in AI Finance

Video: Microsoft COPILOT - Your
New AI Best Friend

Understanding the
Distinctions Between
Generative AI like
ChatGPT and Traditional
AI Systems
07

Video: Traditional AI vs
Generative AI | What's the
difference?

What is Traditional AI?
•Definition: AI designed for
specific tasks
•Examples: Rule-based
systems, Decision Trees,
Expert Systems
•Capabilities:
•Perform specific tasks
efficiently
•Predict outcomes based on
input data

What is Traditional AI?
The main characteristics of Traditional AI include:
Programmed intelligence – Traditional AI works based on preprogrammed algorithms and
rules. The system provides solutions and performs tasks within the limitations of its algorithm
developed by programmers.
Restricted applications – These AI models are designed with a specific set of tasks in mind,
limiting their scope of potential applications.
Data analysis – Traditional AI focuses on analyzing sets of data and making predictions
based on this analysis. It can be successfully used for creating forecasts and other data
analysis.
Limited learning capabilities – The learning capabilities of Narrow AI are limited and
dependent on data sets inputted by the human creator.

What is Generative AI?
•Definition: AI that can create new content
•Examples: ChatGPT, DALL-E, GPT-4
•Capabilities:
•Generate text, images, music, etc.
•Mimic human-like responses

Comparing Features of Traditional and
Generative AI
Generative
AI
Flexibility and
creativity
Contextual
understanding
Continuous learning
from large datasets
Traditional
AI
Task-specific
Predefined rules and
logic
Limited learning scope

Key Differences
Generative AI Traditional AI
Image and video generation Medical diagnosis
Medical diagnosis Fraud detection
Music generation Product recommendation systems
Code generation Self-driving cars
Drug discovery Voice assistants
Material design Machine translation
Creative writing Game playing
Art and design Financial trading
Here are a few examples from the wide range of Generative AI and
Traditional AI applications.

Video: Generative AI, Large
Language Models and ChatGPT

AI and Employment
Trends: Automation,
Augmentation, and
New Opportunities
08

Job Creation and Workforce
Development
While AI automation may disrupt certain
jobs and industries, it also creates new
job opportunities and demands for skilled
workers in AI-related fields such as data
science, machine learning, engineering and
AI ethics.
Investing in workforce development
programs, reskilling initiatives, and
lifelong learning opportunities can help
prepare individuals for the jobs of the
future and ensure that they can benefit
from AI-driven economic growth.

Video: What Will AI Do To Workforce?
AI’s Impact On Jobs | Microsoft's Satya
Nadella

Upcoming Trends and Developments
in AI
AI is on the rise. While there are legitimate concerns about the rapidly
advancing technology, there are also numerous artificial intelligence examples
that prove it’s shaping the future for the better.
Manufacturing
robots
Self-driving
cars
Smart
assistants
Healthcare
management
Automated
financial
investing
Virtual
travel
booking agent
Social media
monitoring
Marketing
chatbots

AI Opportunities and Challenges
Ahead
Opportunities Challenges
Automating tedious tasks to free up human
potential
Job displacement and unemployment
Advancements in healthcare, such as
personalized medicine and early disease
detection
Ethical concerns surrounding data privacy
and security
Improving efficiency in various industries,
leading to cost reduction and increased
productivity
Bias in AI algorithms leading to unfair
outcomes
Enhancing education through personalized
learning experiences
Potential misuse of AI for malicious
purposes, such as cyber attacks
Accelerating scientific research and
discovery through data analysis and pattern
recognition
Lack of regulation and accountability in AI
development and deployment
Facilitating environmental sustainability
efforts through optimization and resource
management
Socioeconomic disparities exacerbated by
unequal access to AI technologies

Video: 20 Emerging Technologies
That Will Change The World

Quiz: Opportunities and
challenges of AI in the future
What is one opportunity of AI in the future that could lead to
cost reduction and increased productivity?
• a) Advancements in healthcare
• b) Automating tedious tasks
• c) Enhancing education
• d) Facilitating environmental sustainability efforts

What is one challenge associated with AI in the future that
relates to potential job displacement?
• a) Ethical concerns surrounding data privacy
• b) Bias in AI algorithms
• c) Lack of regulation
• d) Unemployment

Which aspect of AI in the future poses ethical concerns regarding
unfair outcomes?
• a) Bias in AI algorithms
• b) Job displacement
• c) Personalized medicine
• d) Environmental sustainability efforts

Answers
• 1) c) Automating tedious tasks
• 2) d) Unemployment
• 3) a) Bias in AI algorithms
challenges of AI in the
future

Reskilling and
Upskilling: Adapting to
AI in the Workplace
09

Executive and Employee Perspectives
Generative AI's Disruption
• 60% of executives predict AI will transform customer and employee experiences.
• AI upskilling is crucial to adapt to these changes.
Employee Concerns
• 2024 Gallup poll: 25% of workers fear job obsolescence due to AI, up from 15% in
2021.
• Over 70% of CHROs predict AI will replace jobs within 3 years.

Upskilling vs Reskilling
Upskilling
• Enhancing existing skills
through training and
development.
• Example: CSRs learning to use
generative AI and chatbots.
Reskilling
• Learning new skills for entirely
different jobs.
• Example: Data processors
learning web development.
• Why They Are Important:
• Keeping up with technological advancements
• Staying competitive in the job market

- IBM's AI Skills Academy
- Objective: Equip employees with AI
and data science skills
- Programs offered: Online courses,
workshops, certifications
- Outcome: Improved employee retention
and innovation
Case Study 1 - IBM

- Amazon's Upskilling 2025
Initiative
- Objective: Train 100,000
employees for in-demand jobs
- Programs: AWS training, Machine
Learning University, Amazon
Technical Academy
- Impact: Increased internal
mobility and job satisfaction
Case Study 2 - Amazon

Tools and Technologies for Learning
E-learning Platforms
• Coursera, Udemy, LinkedIn Learning
AI-Based Training Tools
• Personalized learning paths
• Virtual and augmented reality training
Data Analytics
• Identifying skill gaps
• Measuring training effectiveness

Video: Do You REALLY Need A Course
In AI? | Upskilling In The Age Of
Artificial Intelligence

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