The document discusses artificial intelligence (AI) use cases in digital telecommunications. It begins with definitions of AI, machine learning, and deep learning. It then provides examples of how AI is being applied in areas such as fraud detection, human-computer interaction, customer profiling, and speech recognition for telecom companies. The document also summarizes the current status and trends in AI technology, products, industry and discusses how AI can support telecom operators through applications like network optimization, smart home devices, and customer service. Key implementation factors of AI including data, algorithms, chips and business models are also highlighted.

1. “Artificial Intelligence Use-case in
Digital Telco”
AGUS LAKSONO
Head of Data Scientist – BIG DATA
Media and Digital Department (MDD)
Telkom Indonesia
agus.Laksono@Telkom.co.id
Indonesia AI
SUMMIT 2019
Hilton Bali, 21-22 August 2019

2. Source: nvidia
AI : Any technique which enables computers to
mimic human behavior.
Ex: Siri, Google Asistant, RPA, expert system etc
ML: Subset of AI techniques which use statistical
methods to enable machines to improve with
experiences
Ex: Churn Prediction, Customer Segmentation,
classification, supervised model,
unsupervised,Reinforcement
DL: Subset of ML which make the computation of
multi-layer neural networks feasible.
Ex: Face App, Video Analytics, Image Analytics
DEFINITION & RELATION OF:
Artificial Intelligence, Machine Learning, Deep Learning

3. 3
AI Enters a Stage of Explosive Development
2
Explosion of AI (2000-now)
2016:Google DeepMind developedAlphaGo
2012:Deep learning algorithm is widelyused
2006:Geoffrey Hinton proposed deep
learning algorithm
Development of AI (1980s-late 1990s)
1997:Deep Blue developed by IBM won thechess
champion
1986:Multilayer neural network and back
propagation arithmetic appeared
Birth of AI (1950s-1980s)
1969:IJCAI was established
1956:Dartmouth Conference
All countries in the world attach great importance to AIand
issue a series of policies to promote the development ofAI.
• Development Plan of the new generation of
Artificial Intelligence (2017.7)
• Three-year(2018-2020) Action Plan for the
development of the new generation of Artificial
Intelligence (2017.12)
• The AmericanAI Initiative （2019.2）
• SUMMARY OF THE 2018 DEPARTMENT OF
DEFENSE ARTIFICIAL INTELLIGENCE
STRA
TEGY (2019.2)
• The Age of Artificial Intelligence: Towards a
European Strategy for Human-Centric
Machines (2018.3)
• Artificial Intelligence for Europe (2018.4)
• Artificial Intelligence Technology Strategy (2017.3)

4. 4
Status and Trends of AI Technology
3
Data：Public data sets are continuouslyenriched
Current Status Trends
AI public data sets are
constantly enriched.AI public
data sets are mainly focus on
the fields:
• NLP
• Speech Recognition
• Machine Vision
• Every industry is
building its own AIdata
set
• Building AI data set
drives the industrial
development of data
service
Chips：Computing power continuouslyimproves
Current Status Trends
• CPU, GPU and FPGA are
the main chips in AI field
at present.
• ASIC chips for neural
network algorithms are
being launched
• Brain-like chips are
still at the stage of
laboratory research
and development
Algorithm：AI algorithm continuously breakthrough
Current Status Trends
• Deep learning drives the
speech recognition, machine
vision, NLP to reach the
level of practicality
• Other scenarios mainly use
classical machine learning
• Deep learning
algorithms continuously
develop
• Other AI algorithms
continue to break
through
Platform：Build AI platform and develop AI Ecology
Current Status Trends
• The open source AI
framework (TensorFlow,
Caffe...) has become the
focus of AI layout for
technology companies
• Technology
companies buildAI
platform, develop
AI capability and
build AI ecology

5. 5
Status and Trends of AI Products
4
Classification Typical Product
Intelligent Robot
Industrial
Robot
Welding Robot, Spraying Robot Processing Robot, Assembly Robot, Cleaning Robot and
Other Industrial Robots
Service Robot
Home Service Robot, Education and Entertainment Service Robot, Old-age and Disabled
Service Robot, Personal Transportation Service Robot, Security Monitoring Service Robot
Hotel Service Robot, Bank Service Robot, Venue Service Robot, Catering Service Robot
Special Robot Special Extreme Robots, Rehabilitation Assistant Robots, Agricultural Robots, Underwater
Robots, Military and Police Robots, Electric Power Robots, Petrochemical Robots, Mining
Robots, Construction Robots, Logistics Robots, Security Robots, Medical Service Robots
and Other Special Robots
Intelligent
Carrier
Self-Driving Car，Rail Transit System，Unmanned Vessel
UAV Unmanned helicopters, fixed-wing aircraft, multi-rotor aircraft, unmanned airships,
unmanned parafoilaircraft
Intelligent
Terminal
Smartphone, Vehicle-borne Intelligent Terminal，Wearable terminal
Speech Machine Translation, Machine Reading Comprehension, Question Answering System, Intelligent Search
Image and Video Image analyzer and video surveillance system
Biometric
Recognition
Fingerprint recognition system, face recognition system, iris recognition system, finger vein recognition
system, DNA/gait/palmprint/voiceprint and other biometric recognition systems
VR/AR PC VR, Integrated VR
Human-
Computer
Interaction
Voice interaction
Emotional Interaction，Somatosensory Interaction， Brain-computer Interaction
Trends
1. Intelligent products will
support more and more
scenarios
2. continuous improvement of
user experience
3. The products for vertical
industry are gradually
enriched
Current Status
1. Focus on a specific scenario to
provide services for users
2. AI products’ userexperience
in complex scenarios is not
good
3. AI products focus on picture,
character and speech fields

6. 6
Status and Trends of AI industry
5
Industry scale Industry direction Industry Talents
Current Status:
By June 2018, the total
number of AI enterprises in
the world was 4.925; In 2017,
the global AIinvestment
and financing scale reached
39.5 billion US dollars.
Current Status:
The application fields of AI
are mostly for one scenario,
such as the face recognition,
the video surveillance, the
speech recognition, and so on.
Current Status:
There are about 300,000
talents in the global AI field.
At present, the demand for AI
talents in the market is millions,
the gap of AI talents is huge,
and professional and compound
talents are more scarce.
Trends:
Statista predicts that the
global AI market will grow
at an average annual rate
of 50.7% over the next 10
years.
Trends:
In the future, AI isadaptable
to multiple and complex
scenarios with the introduction
of new AI products like that of
the smart home and the
intelligent logistics.
Trends:
The gap of AI talents willbe
further expanded. The global
competition for talents will
become increasingly fierce.

7. 7
Algorithm
B
u
s
i
n
e
s
s
Chip
D
a
t
a
Four Key Factors in Promoting AI
4 Factors are promoting AI Implementation
Chip
Deep learning has higher
demand on parallel
computing and data
exchange. IC Chip is
instrumental in providing
the appropriate processing
power
Data
The massive amount of
extracted ，or simulated, or
multi-dimensional data is
used to train AI model for
later real time inference
Business
Identifying appropriate
usage scenarios with
business value determines
whether an AI product is
successful or not
Algorithm
The breakthrough of deep
learning algorithm has led
to the prosperity of the
current generation of AI
What is the killer application?

8. 8
Examples of Better Services through AI
• Identifies telecom & network
fraud based on deep learning
• Recognizes firmware/software
vulnerability based on deep
learning
Fraud Detection and
Loophole Prevention
Human Computer
Interaction
Classifies users with real data such as terminal
type, chip type, transmission capacity, service
type, buffer length, location, movement speed,
motion trajectory, etc
Provides targeted services on demand, ensures
better user experience and improves network
utilization
Customer Personas
• Converts human’s voice command to
machine executable command
• Generates text automatically from voice
• Data mining from converted text

9. 9
Examples of Applications of AI in Telecommunication
Operation &
Maintenance
1. Network
healthiness
analysis and
early warning
2. Network alarm
root cause
analysis
3. Network fault
debug and fix
4. Network fault
prediction
Network
Optimization
1. Traffic regulation and
congestion prevention
2. Parameter
management and RF
automation
3. Network energy saving
4. Mobile network
coverage and capacity
optimization
5. Mobile network load
balancing
6. 5G massive MIMO
automatic
configuration
1. Hot spot analysis
and prediction
2. User mobility
pattern analysis
3. User network NPS
prediction
1. IP network
management
2. Intelligent
network software
deployment
3. Smart forward
pass
management and
combination
NETWORK INFRASTRUCTURE
Network Management
1. Intelligent
collection and
monitoring of
operation data
2. Environment aware
usage experience
improvement
3. Intelligent service
recommendation
4. QoS assurance of
different type of
requests
1. Optimal
deployment of
network services
2. Server
architecture and
optimal wiring
deployment
3. Intelligent
network time
slicing
management
4. Intention based
closed loop
network control
Service
Assurance
Usage
Analysis
Operational
Management
Deployment
Management
NETWORK SERVICES NETWORK MANAGEMENT

10. 10
AI in Supporting Telecom Operators
9
Future Network
1. SDN closed-loop management
2. Fault detection and self-healing
3. Network slicing and beam management
4. Cloud data center management
5. Edge services management
Current Network
Planning
•Performance Analysis
Deployment
Suggestion
Operation
Maintenance
• Root-tracing of Alarm
Construction
• Big DataAnalysis
Intelligent Design
Optimization
• RF Self-Optimization
Automatic Correction
Smart Home
Voice assistant Smart speaker Set top box
Customer Service
Call center
Service robot
Chatbot
Industrial Applications
Smart cities
Smart retail
Smart government
Smart transportation
······
AI
Platform
Networks
＋
Industries
↓
Overall management

11. 11
Typical Cases of AI in Telecom Operators

12. 12
13
Typical Cases of AI in Telecom Operators-China
China Telecom
China Mobile China Unicom
p Network architecture
“CTNet2025”
p Business-driven adaptive
network
p Intelligent data center
p Smart speaker “Xiaoyi”
p Smart customers service robot
“Xiaozhi”
p Smart customers service cloud
platform
p Smart government platform
“Ma Shang Ban”
p Smart police product “Zhi Cha”
p Medical image cloud
p AI platform “DengTa”
p Network architecture “NovoNet”
p ACOS, APOS, AIOps for
network optimization and
operation
p Intelligent QualityInspection
System
p Intelligent watch and earphone
p Voice assistant “Lingxi”
p Interactive robot “Yiwa”
p “Super-brain” plan for Xiongan
smart city
Smart vehicle terminal
“Helutong”
p AI platform “JiuTian”
p Network architecture “CUBE-
Net 2.0+ ”
p Awareness & Analysis system
p intelligent NFV operationand
maintenance platform
p Intelligent IPTV set-top box
p Smart home platform “Steward
of Wo Jia”
p Intelligent onlinecustomer
service robot “Wobao”
p "Smart Blue Sky" air pollution
prevention and controlplatform
p “AI+ Teachers'Ability
Development Joint Laboratory ”
p Cloud platform “Tian Gong”
p Intelligent networkplatform
Networks
Services
Industries p
Platform

13. 13
14
Typical Cases of AI in Telecom Operators-America
p Disaggregated Network Operating System
(dNOS)
p Threat Intellect platform fornetwork
security
p Open Networking AutomationPlatform
(ONAP)
p UAVfor cell tower inspections
p Smart home product “Digital Life”
p AI chatbot for contract center
p Smart city framework
p Smart glasses and “Hey Chloe” medical
platform for people with poorvision
p Open source AI platform“Acumos”
p Edge computing platform “Akraino Edge
Stack”
p SD-WLAN service based on AI system Mist
for network faults automatic prediction, self-
healing and illegal access prevention
p Network stability surveillance in thefiber
optic broadband service
p Smart home automation system
p End-to-end service DigitalCustomer
Experience (CX)
p Video nodes for smart city
p A portfolio of platforms “Exponent”
Networks
Services
Industries
AT&T
Platform

14. • Difficult to find data scientist from Market and University
• Need to time in data scientist training and certification
Talent of Data Scientist
• Collaborative attitude, Inclusive, Openness (transparent
process), inquisitive (curiosity)
Analytics and Data driven
Culture
• Testing mindset, fact-based, continuously improvement
by iteration
Decision Making Process
• Data quality in term of validity, accuracy, completeness
and up-to-date ness.
Data quality from front-
liner
Analytics Challenges