My keynote from this weeks (June 12, 2018) 5G World conference in London. I focus here on AI for Telcos and in particular what can be done in the network and IT parts of the network. Making the point that there remains a lot of low hanging fruits which are simpler and easier to solve than present day image and NLP challenges. It is amazing how much you can do with fairly simple ML architectures with out going to state-of-art DL frameworks.

1. AI FOR
TELCO’S
5GWorld –London,June12,2018
Dr.Kim KyllesbechLarsen,CTIO MagyarTelekom.

2. Who we are serving
EXPERIENCE MATTER!

3. We are
increasingly
dependent
on digital
technologies.
“Today,
everything & more
that makes us us
are replicated in
the digital world.

4. More than 70% of consumers do not trust
companies have their best interest in mind!
SurveyMonkey “Millennials – Digital Challenges & Human Answers Survey “ (March 2018).

5. DO NOT APPLY
MAGIC
Leprechauns are
unlikely to exist!
The Leprechaun

6. AUTOMATION & AUTONOMY

7. WHAT AI?
DT’s
Vanda
NARROW AI
~100% of use cases

8. HOW AI? NEED TO
“CLEAN” DATA!
QUALITY
GOALS!
(to train the model, normally not as heavy to run it) (need to beat “Flipping a Coin” or Majority “Vote”)
IT STARTS
HERE!
Train Test
COMPUTING
POWER
LOTS
OF DATA!
(the more data, the higher quality should result & the
less complexity required … in general)
MODEL /
ARCHITECTURE

9. Why AI?

10. NG-
Telco
Industry 4.0
Manufacturing
Energy
Cities Surveillance
Healthcare
Transportation
Retail
Automotive
Smart Home
Banking
$$
Critical
Infrastructure
Agriculture
Engineering
DLT
4&5G
FTTx
Telekom InfrastructureMedia
A.I.
IOT

11. OPERATIONS
CENTER
Stores
PLANNING OFFICE WORK
CALL CENTER
MASSIVELY
MANUAL
STORES

12. 3Z TELCOS

13. Access
Data Center
Experience n = n + 1
Telco AIs
Learning Agents
EnvironmentObservations:
Data Center, Access
Device, Services
Customers, Etc..
Actions
e.g., driving SON
Reward
e.g., to achieve
desired outcomes
Millions of Experience
iterations per hour.
3Z TELCO
Services
Possible ingredients:
 RNN with Reinforcement learning.
 LSTM, anomaly detection, ML + FFT, …
 POFCNN (plain-old fully-connected nn)

14. TELCO AI
Machine Intelligent
Prevent Tool Basic Research
NG-SON RNN w. re-
enforcement learning
Anodot
There are a lot of
low hanging ML fruits
in Telco
Network & IT.
Illustrations (not exhaustive)
Anomaly Detection
RFMLS & Spectrum
Collaboration Challenge

15. NETWORK OPERATIONS
(sub)milliseconds to minutes
NETWORK OPTIMIZATION
(sub)seconds to minutes
PROACTIVE USER EXPERIENCE
(sub)milliseconds to (sub)seconds
COMPLAINT MANAGEMENT
minutes to hours
NETWORK MAINTENANCE
hours to months

16. THANK YOU!
Acknowledgement
Many thanks many colleagues who have
contributed with valuable insights, discussions
& comments throughout this work.
Also I would like to thank my wife Eva Varadi
for her patience during this work.
Contact:
Email: kim.larsen@telekom.hu
Linkedin: www.linkedin.com/in/kimklarsen
Blogs: www.aistrategyblog.com & www.techneconomyblog.com
Twitter: @KimKLarsen