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2. 2
Agenda 03
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Agenda 02
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Agenda 01
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Agenda
3. 3
Artificial Intelligence Outline
Artificial Intelligence Introduction
Artificial Intelligence Objectives
Artificial Intelligence Components
Artificial Intelligence Key Statistics
Reasons For Using Artificial Intelligence
Survey on Adoption of
Emerging Technologies
Artificial Intelligence & Investment
By Sector
Machine Learning Description
Machine Learning Process
Machine Learning Use Cases
Artificial Narrow Intelligence Vs
Artificial General Intelligence
Potential Use Cases of AI in Healthcare
Challenges in Adoption of
Artificial Intelligence
Artificial Intelligence In Various Sectors
Driving Force Behind Artificial
Intelligence Maturity
Core Areas of Artificial Intelligence
Artificial Intelligence Value
Chain Elements
Artificial Intelligence
Development Phases
Artificial Intelligence Themes
Artificial Intelligence Approaches
• Logic & Rules-based Approach
• Machine Learning (Pattern Based)
4. 4
Artificial Intelligence Introduction
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AI
Technologies
Illustrative
Solutions
Computer
Vision
Virtual
Agents
Audio
Processing
Identity
Analytics
Sense
Natural Language
Processing
Cognitive
Robotics
Knowledge
Representation
Speech
Analytics
Comprehend
Machine
Learning
Recommendation
Systems
Expert
Systems
Data
Visualization
Act
5. 5
Achieve The Objectives of Company
XX By 2020
Boost Organizational Performance
at all Levels
Use an Integrated Smart Digital System
That Can Overcome Challenges and
Provide Quick Efficient Solutions
Make Company XX The First in The Field
of AI Investments in Various Sectors
Text Here
Text Here
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Artificial Intelligence Objectives
6. 6
Strategy
• Aligning with strategic goals
• Text here
Design
• Software design is transparent & auditable
• Text here
Development
• Iterative development
• Managing data dependency
Operating Model
• Data as your key intellectual property
• Text here
Artificial Intelligence Components Template 1
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7. 7
Artificial Intelligence Components Template 2
Data
• Pay per click
• Search console data
• Customer service transcripts
• Social feedback
Technology
• AI analytical engine
• Interface for data upload
Strategy
• Select optimal AI engine
• Instruct AI engine
• Create a training strategy
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8. 8
Artificial Intelligence Key Statistics
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7 in 10 organizations that have
replaced, or plan to replace, roles
with technology will retain or
redeploy those who are displaced
69%
Two- third of senior
decision- makers agree that AI is
fundamental to the success of
their organization’s strategy
65%
By 2022, those currently or planning to use AI
technology anticipate a 39% boost to their
organization’s revenue, on average
45%
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Automated Data- Driven Reporting
Automation That Eliminates Manual
And Repetitive Tasks
Automated Communications That
Give Consumers Data They Can Use
To Effective Decisions
Monitoring And Alerts About The
Health Of The Business
Automated Communications That Give Firms Data They
Can Use To Make Effective Business Decisions
Other
All Of The Above Automated
Data-driven Reporting
44%
6%
20%
5% 7%
11%
7%
Reason For Using Artificial Intelligence
10. 10
Survey on Adoption of Emerging Technologies
65%
57%
45%
40%
38%
35%
32%
28%
23%
0%
20%
40%
60%
80%
IOT/Smart Sensors Artificial Intelligence Blockchain AR/VR Robotics/Autonomous
Robots
3D Printing Autonomous Vehicles Wearables Text Here
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11. 11
Artificial Intelligence & Investment by Sector
Travel & Tourism
Construction
Education
Automotive & Assembly
Transportation & Logistics
Financial Services
Media & Entertainment
Energy & Resources
Professional Services
Healthcare
Retail
Consumer packed Goods
High tech &
Telecommunication
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30
Leading Sectors
Falling Behind
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12. 12
Artificial Intelligence in Various Sectors
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To reduce accidents and cut
operational costs
Transport
01
Health
To minimize chronic and
dangerous diseases
02
Water
To conduct analysis and studies to
provide water sources
03
Technology
To increase productivity and help
with general spending
04
Environment
To increase
forestation rate
05
Traffic
To reduce accidents & traffic jams &
draw more effective traffic policies
06
13. 13
Driving Force Behind Artificial Intelligence Maturity
35%
30%
25%
22%
19%
16%
14%
10%
20%
30%
40%
Competitor Advantage Executive-led Decision Particular Business,
Operational or Technical
Problem
Internal Experiment Customer Demands Unexpected Solution to
Problem
Offshoot of Another
Project
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14. 14
Core Areas of Artificial Intelligence
Sensory AI Such As Internet of Things
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Physical AI Like Industrial Automation
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Cognitive AI Such as Worker Training
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General AI
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High Precision Learning From Small Data Sets
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Research on New Algorithms
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Explainable AI
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15. 15
Artificial Intelligence Value Chain Elements
Data
Capture
Cleansing of
Raw Data
Curation, Labelling
& Standardization
Annotation of Raw
Data For ML Models
Creation of ML
Model For Use Case
Training of ML Model With
Annotated Data With
Computational Resources
Testing of Model
on New Data
Deployment of Solution
on Computational
Infrastructure
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16. 16
Artificial Intelligence Development Phases
By Q1
FY19
Phase 1
• Utilization & application of data-driven artificial
intelligence developed in various domains
• Utilization of AI will increase together with new
possibilities of growth in related service industry
By Q3
FY20
Phase 2
• Public use of Artificial Intelligence
across various domains
• Your text here
By Q1
FY21
Phase 3
• Ecosystem is built by connecting
various domains
• Your text here
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17. 17
Artificial Intelligence Themes
Formation of The Artificial
Intelligence Department
Workshops, Programmes,
Initiatives and Field Visits
Develop Capabilities and
Skills of all Staff Operating in
The Field of Technology and
Organize Training
Provide All Services Via
Artificial Intelligence
Integration of AI Into
Different Departments
Text Here
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18. 18
Artificial Intelligence Approaches
Logic & Rules-Based Approach
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Machine Learning
(Pattern Based Approach)
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Artificial Intelligence
19. 19
Logic & Rules- Based Approach
Representing
Process or System
Using Logical Rules
Top-down Rules are
Created For Computer
Computers Reason
About These Rules
Can Be Used To
Automate Process
E.G. Personal Income Tax Laws
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20. 20
Machine Learning (Pattern Based)
Learn From Data &
Improve Overtime
Text Here These Patterns Can Be Used
For Automation or Prediction
Machine learning is a dominant form of AI today
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21. 21
Machine Learning Description
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Algorithms + Techniques
Relationships Patterns
Dependencies Hidden Structures
Output
Optimum Model
Input Data
Information (+ Answers)
Machine
Learning
22. 22
Machine Learning Process
Step 1
Gathering Data From
Various Sources
Step 2
Cleaning Data To
Have Homogeneity
Step 3
Model Building- Selecting
The Right ML Algorithm
Step 4
Gaining Insights From
The Model’s Results
Step 5
Data Visualization- Transforming
Results Into Visuals Graphs
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23. 23
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Learning
Pattern Detection
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Data
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Self-Programming
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Machine Learning Main Points
24. 24
Machine Learning Use Cases
Manufacturing
• Predictive maintenance or
condition monitoring
• Demand forecasting
• Process optimization
• Telematics
Retail
• Predictive inventory planning
• Recommendation engines
• Your text here
• Customer RoI & lifetime value
Healthcare & Life Sciences
• Alerts & diagnostics from real-time patient data
• Your text here
• Proactive health management
• Healthcare provider sentiment analysis
Travel & Hospitality
• Aircraft scheduling
• Dynamic pricing
• Your text here
• Traffic patterns & congestion management
Financial Services
• Risk analytics & regulation
• Customer segmentation
• Your text here
• Credit worthiness evaluation
Energy, Feedstock & Utilities
• Power usage analytics
• Seismic data processing
• Smart grid management
• Energy demand & supply optimization
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25. 25
Artificial Narrow Intelligence Vs Artificial General Intelligence
Beat Go World
Champions
Read Facial
Expressions
Write Music
Diagnose Mental
Disorders
Comfort Earthquake
Survivors
Artificial
Narrow
Intelligence
Understand Abstract
Concepts
Explain Why
Be Creative Like
Children
Tell Right From
Wrong
Have Emotions
Artificial
General
Intelligence
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26. 26
Potential Use Cases of AI in Healthcare
AI &
ROBOTICS
Keeping Well
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Early Detection
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Diagnosis
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Decision Making
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Treatment
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End of Life Care
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Research
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Training
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27. 27
Challenges in Adoption of Artificial intelligence
Lack Of Enabling Data Ecosystem
Low Intensity of AI Research
Inadequate Availability of Expertise, Technology & Research
High Resource Cost & Low Awareness For Adopting AI in Business Processes
Unclear Privacy, Security and Ethical Regulations
Unattractive Intellectual Property Regime To Incentivize Research & Adoption of AI
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31. 31
Mission
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Vision
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Goal
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Our Mission
32. 32
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33. 33
SWOT
S O
W
T
Strength
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Threats
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Weakness
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Opportunities
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34. 34
Target Audiences
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Preferred By Many
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Value Clients
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About our Company
35. 35
Our Team
Name Here
Designation
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Name Here
Designation
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Name Here
Designation
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36. 36
Pie Chart
25% 50% 75%
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37. 37
Financial
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38. 38
Target
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Target 01
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Target 03
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Target 02