This document discusses various topics related to digital disruptions and transformations including artificial intelligence, machine learning, deep learning, robotic process automation, big data, and cloud infrastructure. It provides definitions and examples of these concepts. For artificial intelligence, it discusses types like machine learning, deep learning, natural language processing, and vision. It also compares AI, machine learning and deep learning. For machine learning, it discusses popular platforms and programming languages. For robotic process automation, it discusses the development process and differences from AI. It also lists popular programming languages for RPA. For big data, it discusses solutions to problems and provides examples. It shows a simple big data flow. Finally, it defines cloud infrastructure and compares on-premise, I

1. DIGITAL DISRUPTIONS AND
TRANSFORMATION
1
Mohammad Faiz PMP, CSM, ITIL
AXA Life Japan

2. 2

3. 3

4. Digital Transformation Journey
4

5. Design
Thinking
Organizational
Culture
Leverage
Technology
Manage Data Reap Benefits
1. Enhanced
customer
engagement.
2. Improved
customer
satisfaction.
3. Increased lead
generation/sa
les
4. Informed
decision-
making and
retrospective.
5. Team
transformatio
n & bottom
up
innovation.
Release
Imagine
DefinePrototype
Test
Agile
Organization
Involve core
business
Communicate via
influencers
Leverage digital
ambassadors/Cha
mpions
Identify digital
drivers
Mobile Apps
Our
topics
for today
5

6. ARTIFICIAL INTELLIGENCE
• Artificial intelligence (AI) is an area of computer science that
emphasizes the creation of intelligent machines that work and
react like humans and make automated decisions.
• The word AI was first coined by John McCarthy in 1956 when he
held first academic conference on this subject.
Artificial Intelligence Artificial Intelligence
6

7. Virtual assistants like Siri, Google Now, and Cortana.
Self-driving cars need to have sensors to understand the world around them and a brain that
collects, processes and chooses specific actions based on information gathered.
Amazon’s anticipatory shipping project hopes to send you items before you need them, completely
obviating the need for a last-minute trip to the online store.
Artificial Intelligence (AI) techniques are proposed to overcome the increasing
challenges of online fraud.
Device like Amazon’s Echo or Google’s Home smart
Automated gate allocation on plane landings
Automated claims, Underwriting and policy recommendations based on customer
profiling.
Introduction of smart cars for normal transport
Purchase predictions and ability to decide for buyer
Doctors are just starting to consider machine learning to make better diagnoses,
for example to spot cancer and eye disease.
Wordsmith, an automated Insights’ software platform, is used for writing car
descriptions and Fantasy Football recaps.
7

8. 8
Types of AI
Machine
learning
Deep
learning
Supervised
Un
Supervised
Natural
language
processing
Content
extraction
Classificati
on
Machine
translation
Question
answering
Text
generation
Expert
systems
Vision
Image
recognitio
n
Machine
vision
Speech
Speech to
text
Text to
speech
Robotics

9. 9
Artificial
Engineering
Machine Learning
Deep Learning
Artificial intelligence is a broader concept than
machine learning, which addresses the use of
computers to mimic the cognitive functions of
humans. When machines carry out tasks based
on algorithms in an “intelligent” manner, that is
AI.
Machine learning is a subset of AI and focuses
on the ability of machines to receive a set of
data and learn for themselves, changing
algorithms as they learn more about the
information they are processing.
Deep learning is a subset of machine learning.
Deep artificial neural networks are a set of
algorithms reaching new levels of accuracy for
many important problems, such as image
recognition, sound recognition, recommender
systems, etc.
AI, Machine Learning & Deep Learning 1/2
Engineering of making intelligent machines &
programs
Ability to learn without being
explicitly programmed
Learning based on deep
neural networks

10. 10
It is a cat or a dog?
Program to
figure out
which one is cat
and which one
is dog
I need lots of
images of cats
and dogs to
learn – I need
experience
I need to create some high
level specs on my own that
help me differentiate. Lots of
abstract features that only I
understand.
Problem solving/Visual recognition
Reasoning
Generalization
AI, Machine Learning & Deep Learning 2/2

11. 11
Problem
at hand
Code the
rules
Review
errors
Evalu
ate
Productio
n
Problem
at hand
Train ML
algorithm
Review
errors
Evalu
ate
Productio
n
Traditional
method
Machine Learning
method
Computer
Data
Program
Output
Computer
Data
Output
Program
Machine learning is a field of computer science that gives
computer systems the ability to "learn" with data, without being
explicitly programmed.

12. POPULAR PLATFORMS & PROGRAMMING
LANGUAGES FOR AI
12
Amazon machine learning
Apache Singa
Azure machine learning

13. 13
AMAZON MACHINE LEARNING PLATFORM –
CASE STUDY
Data
Sources
Predictors Enablers Results
Application logs
Point of sale data
Supply chain data
Social media
Amazon
machine learning
Scalability
Ease of
user
Agility
Cost
effectiven
ess
Optimize
a wide
range of
operation
al and
consume
r centric
processe
s

14. ROBOTIC PROCESS AUTOMATION
• RPA is an application of technology aimed at automating business
processes. Using RPA tools, a company can configure software, or a
“robot,” to capture and interpret applications for processing a
transaction, manipulating data, triggering responses and
communicating with other digital systems.
• Designed to perform on a vast range of repetitive tasks, software
robots interpret, trigger responses and communicate with other
systems just like humans do however a robot never sleeps, makes
zero mistakes and costs a lot less than an employee.
14

15. BUSINESS PROCESSES IN WHICH RPA CAN
BE USED
Take over
repetitive tasks
that employees
perform several
times a day.
Periodic
reporting, data
entry and data
analysis.
Mass email
generation.
Conversion of
data formats and
graphics.
ERP transaction. Process lists and
file storage.
15

16. RPA Development ProcessInputs from
stakeholders
1. Identify
requirements
for automation
orchestration
and
management
platform.
2. Identify key
layers/compon
ents of
automation
platform.
3. Prioritize
required
components.
4. Evaluate third
party
management
1. Deploy & Use.
2. Identify
process
changes.
3. Retrain robot
on changes.
1. Use
predictive
and
adaptive
analysis.
Continuous feedback and
improvements
16

17. Difference between RPA & AI
System based on
rules
Bots automate easy
tasks and make broad
data sources
accessible to AI
System that learns
AI learns to mimic and
improve processes
based on data handed
over from RPA.
Accessing legacy system
data.
Filling in web forms.
Copying data from one
Learning from human
decisions.
Making fast judgements.
Interacting with humans.
17

18. POPULAR PROGRAMMING LANGUAGES
FOR RPA
18

19. BIGDATA
• Big data is a term that describes the large volume of data – both
structured and unstructured – that inundates a business on a day-to-
day basis. But it’s not the amount of data that’s important. It’s what
organizations do with the data that matters. Big data can be analyzed
for insights that lead to better decisions and strategic business
moves.
• The amount of data that’s being created and stored on a global level
is almost inconceivable, and it just keeps growing. That means
there’s even more potential to glean key insights from business
information – yet only a small percentage of data is actually
analyzed.
19

20. The Problem Big Data Solutions
A Sports TV wants to estimate how much it should
charge for advertisements during a specific match.
Based on large historic data of viewership, playing
teams, location, match timing, players, time of year
etc. – Big data provides analytical data and range of
pricing for advertisements.
A agriculture produce company wants to make
forecasts and investment plans for future agricultural
investments.
Based on large historic data of crop volume,
purchasing price, selling price, weather patterns,
storage – Big data provides information on which
crops will provide maximum RoI to the company.
Wendy`s fast-food take away section has long ques
and unhappy customers
if the line is really backed up, the features will change
to reflect items that can be quickly prepared and
served so as to move through the queue faster. If the
line is relatively short, then the features will display
higher margin menu items that take a bit more time to
prepare.
CERN, the nuclear physics lab with its Large Hadron
Collider, the world's largest and most powerful particle
accelerator was generating terabytes of research
data.
The CERN data center has 65,000 processors to
analyze its 30 petabytes of data. However, it uses the
computing powers of thousands of computers
distributed across 150 data centers worldwide to
analyze the data.
20

21. Email
Database
Social Media
Unstructured
Logs
RDBMS
Big Data
Data Lake
Reports/
Analytics
Decision
making
Business
OperationMobile
External
Repositories
Simple Big Data Flow
21

22. CLOUD INFRASTRUCTURE
• Cloud infrastructure refers to a virtual infrastructure that is
delivered or accessed via a network or the internet. This usually
refers to the on-demand services or products being delivered
through the model known as infrastructure as a service (IaaS),
advanced versions of cloud include Platform as a service (PaaS),
Software as a service (SaaS).
22

23. Cloud Services
Applications
Data
Runtime
Middleware
Operating
System
Virtualization
Server
Storage
Networking
Applications
Data
Runtime
Middleware
Operating
System
Virtualization
Server
Storage
Networking
Applications
Data
Runtime
Middleware
Operating
System
Virtualization
Server
Storage
Networking
Applications
Data
Runtime
Middleware
Operating
System
Virtualization
Server
Storage
Networking
Manage on your own Managed by provider
On
Premise
IaaS PaaS SaaS
23

24. THANK YOU
24