Introduction to Artificial Intelligence. Not complex and should be relative easy to follow. Be aware that due to its high levelness (and no voice over) some care should be taken by the simplified examples used.

1. Artificial
Intelligence
Dr. Kim Kyllesbech Larsen,
Group Technology, Deutsche Telekom.
Network Innovation Forum
Bonn, Germany.
June 24th, 2016

2. 2
Any sufficiently
advanced technology
is indistinguishable
from Magic.
“Clarke’s 3rd Law”

3. 3

4. 4
A.I.‘s today
are more comparable with
idiot savants
than a human inteligence.

5. 5
Reference: S.M. Potter (2007), “What can AI get from Neuroscience”.
Natural Artificial Intelligence.& Artificial

6. 6
Natural Artificial Intelligence.
~ 10+ Peta computations per sec*.
< 100 Watt (over 1,200 cm3)
~ 100 Billion neurons.
~ 100 Trillion connections.
~ 200 Hz neuron speed.
Memory fades.
& Sleep is required.
~ 90+ Peta computations per sec**.
~ 15+ Mega Watt (huge data center)
~ 10 Million Cores.
~ Trillions of transistors.
~ 2.0 Giga Hz CPU speed.
Memory on RAM … long-term precise.
& Never gets tried (24x7).
& Artificial
Reference: (*) Interesting discussion with refs https://www.quora.com/Roughly-what-processing-power-does-the-human-
brain-equate-to , (**) http://www.top500.org/news/china-tops-supercomputer-rankings-with-new-93-petaflop-machine/

7. 7
6 Bn neurons
(Chimpanzee Brain)
(cerebral cortex)
20 Bn neurons
(Human Brain)
(cerebral cortex)
~ 2025 - 2030
(Artificial “Brain”)

8. 8
1 0
Reference:David Silver etal.,“Mastering thegameofGo with deepneural networksand treesearch”, Nature (2016).
Machine Man

9. NARROW AI
Today ~100% of use cases
Ex Machina (2015)
GENERAL AI
(WIP:-)

10. 10
45 percent of
American jobs are at high risk of
being taken over by
A.I. & Robotics
within the Next Decade or two.
Carl BenediktFrey& MichaelA. Osborne,“TheFutureof Employment”, Oxford Martin Programme on
TechnologyandEmployment(2013).

11. 11
?

12. 12
Machine Learning
The A.I. Seed
How to give birth to an A.I.
Supervisor
or Not 𝑦 𝑚𝑜𝑑𝑒𝑙 𝑥; 𝜃 = 𝜃01 +
𝑖=1
𝑛
𝜃𝑖 𝑓𝑖(𝑥𝑖)
𝑦 𝑚𝑜𝑑𝑒𝑙 𝑥; 𝜃 = g(𝜃01 + 𝑖=1
𝑛
𝜃𝑖 𝑓𝑖(𝑥𝑖))
Learning Rules

13. 13
Machine Learning
The A.I. Seed
How to give birth to an A.I.
Supervisor
or Not 𝑦 𝑚𝑜𝑑𝑒𝑙 𝑥; 𝜃 = 𝜃01 +
𝑖=1
𝑛
𝜃𝑖 𝑓𝑖(𝑥𝑖)
𝑦 𝑚𝑜𝑑𝑒𝑙 𝑥; 𝜃 = g(𝜃01 + 𝑖=1
𝑛
𝜃𝑖 𝑓𝑖(𝑥𝑖))
Learning Rules

14. 14
Supervised Learning.
A.I.
Learning
Principles.
"10,000-Hour Rule“
The key to achieving world class
expertise in any skill, is, to a large
extent, a matter of practicing the
correct way, for a total of around
10,000 hours.
Malcolm Gladwell, “Outliers” (2008).
Unsupervised Learning.
The A.I. “Holy Grail”
The Default Approach

15. 15
The A.I. Brain
Machine Learning.
Training
Data
Feature
Engineering
Machine
Learning
Model
Quality
Metric
Algorithm
PRODUCT
MODEL
New Data
Creation Process.

16. 16
MachineLearning Apps Big Data Process
A.I. in the Telco Network.

17. 17

18. Anomaly Detection.
Events & Incidents.
Self-restoration.
Service Quality.
Security.
18
Network
Management
Capacity Planning.
Self-optimized
Network.
Congestion
management.
Network
Optimization
Classical CEM.
Zero-touch CEM.
Customer care.
Reporting – KPIs, …
User
Experience
Illustration of some A.I.- ML use case.
Non-exhaustive.
milliseconds - minutes milliseconds - month milliseconds - month
 zero-human-touch  zero-human-touch  zero-human-touch

19. 19
Cell
Cell
Cell
Illustration
 n = 20,000 Radio Cells.
 Input: 16 cell-level parameters.
 Output: 5 load-functions.
 100,000+ regression models.
 Planning validity ~ 4+ month
• 16 Measures of Traffic.
• 5 Load Measures;
on a cell by cell level
hour by hour!
(supervised learning models)
A comprehensive, reliable
and accurate forecast
model of future traffic.
Model Input (per hour).
Model Output
𝑪𝒊=𝟏..𝟓
𝑳𝒐𝒂𝒅
=
𝒋=𝟏
𝟏𝟔
𝒂𝒊𝒋 𝑿𝒋 ∀𝒏
Cell
Cell
Smarter & more efficient planning.
1 Paper on “Mass Scale Modeling for Prediction and Simulation of the Air-Interface Load in 3G Radio Access Networks”, by D. Radosavljevik,
v.d. Putten & K. Kyllesbech Larsen submitted to The 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining’12.
Design Machine Intelligence
using Assisted Learning
The Data.

20. 20
(Real) Perceived Customer Experience.
*Participants in the survey are informed and agreed (i.e., opt in policy applied) that their data will be used for
research. No DPI applied.
Dissatisfied Customer Characteristics
 Less than 90% on 3G when using data.
 Successful PDP context creations < 80%.
 3G Voice Call Setup Duration > 3 seconds.
 2G Voice Call Setup Duration > 5 seconds.
 Postal code areas (i.e., coverage/capacity)
 Handset type
 Data usage > 300MB per month.
 Number of sites visited > 60.
 Voice call duration per month >450 minutes.
 A perception of paying too much.
(i.e., higher bill, higher expectations)
Expressed degree of
unhappiness (call survey).
Actual Network Experience
up to survey call.
(supervised learning models)
Degree of customer
unhappiness
(e.g., TRI*M “prediction”)
Model Input.
Model Output.
The Data.

21. 21
Data Center Centre Power Usage Efficiency.
DC performance boost & energy reduction
Note: PUE is Total Facility Energy / IT Equipment Energy. Source: https://googleblog.blogspot.de/2014/05/better-
data-centers-through-machine.html
19 operational DC parameters
(e.g., Server Load, Total Network Room
Load, Total Number of Water Pumps
Running, Weather conditions, etc..).
Measured Power Usage Efficiency
(supervised learning models)
99.6% prediction accuracy
(based on new data)
of the PUE
Model Input.
Model Output
The mechanical plant at Google DC facility in The Dalles, Oregon.
3LNN (Neural Network)
The Data (Google Example).

22. 22
Recommender systems (Netflix example).
Netflix’s 1 Million Dollar Competition.
* The winning algorithm was never implemented due to scalability (designed to 100M ratings, Netflix has more than 5 Billion) and change of
business focus from DVDs to streaming. However several elements of the algorithm was carried over to the digitized recommender system. See
also: http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html (part 1) & http://techblog.netflix.com/2012/06/netflix-
recommendations-beyond-5-stars.html (Part 2).
- 60 Million subscribers.
- 10+ million ratings per day.
- 10 Billion hours / month (5.5+ hour/day/sub).
- 50+ GB consumer per subscriber per month.
- Delivery content across many devices.
Improve the Netflix
recommender accuracy
with 10% (was 76%).
Leap Customer Satisfaction.
(unsupervised combined with
supervised learning models)
75+% of content
people watch is through
the recommendations.
Model Aim.
Model Output.
Develop Highly Accurate Recommender

23. 23
Predictive “caching” in the mobile domain.
- 60+% of mobile data traffic from videos.
- YouTube takes another 60% of video traffic.
- Minimum bandwidth for video increasing!
- Increased storage space on device.
Streaming Profile (dynamic).
Device Capabilities.
Social Network.
Gender – Age – Background.
(unsupervised combined with
supervised learning models)
Reduce substantially video
network load during busy traffic
hours shifting traffic “off-peak”.
Model Input*.
Model Output.
How to move mobile video off-peak?
Mobile
Traffic Profile
voice
Primary indoor usage → off-load possibilities
Off-busy-hour In-fill delivery
data
* Given the information required it might be much more efficiently done by video content provider (e.g., YouTube,
NetFlix, ..) than operators. Incentives should be provided to stimulate more efficient predictive content delivery.

24. 24
Anomaly Detection – Earliest Detection.
Identifying the black sheep … ASAP.
Fraud Detection (most common appl.)
DDOS Attacks.
Imminent Hardware Failure.
Virus Propagation.
(RT) System Anomaly Detection.
Examples.
× “Easy“
thousands of
normal events
1 critical anomaly
Pi
Pj
×Nasty
Dimensionality Challenge
… parameters monitored
are not representing issue.
Un-supervised learning models are
(usually) used for anomaly detection.
Normal accuracy measures
are usually not appropriate.
Appropriate Learning Models.
The Data.

25. 25
Challenges ... the next steps.
ML in the Real Time Domain … from seconds to milliseconds.
Data Sources
(Data Generation Entity)
Data
Stream
{ X(t) }
Process
(e.g., filter, route,
enrich, compute)
Transport
Decision Point
(e.g., ML model)
Data
Stream
{ X(t), F(X(t)) }
Transport Store
(e.g., HDFS)
Store or
in-memory
Change
Order
Input Output
t0 t1
Roundtrip
time
Scale
~ms
t2
Batch
Process
Typical timescales from  ms and up
Insights
Typical timescales
Minutes  Daily  Monthly
+ Ad-hoc
Streaming or micro-batch processing
MachineLearning Apps

26. Danger of over-engineering ML.
Very efficient solution!
Good Bike
Very expensive & complex solution!
Bad “Bike”
vs
A B
Best
Solution?
Desired outcomeNeed or Desire
e.g., GLM or parsing e.g., DCNN, RNN, …

27. Some hidden Debt in Machine Learning.
Economics of ML system integration is largely unknown.
Machine Learning Model
Entanglement
Machine Learning Systems
mix signals together,
entangling them &
makes isolation of
improvements
largely impossible.
Correction Cascades Undeclared Consumers
  
m1 m’1 m’’1 m’’’’’’1
Once in place, a correction
cascade can create an
improvement deadlock, as
improving the accuracy of
any individual component
actually leads to system-
level detriments.
correction cascade
Reference: D, Sculley et al (2015), “Hidden Technical Debts in Machine Learning”.
Official System
with Model M1 &
Output OM1
OM1
Undeclared
system w. M1
dependency
Undeclared consumers are
expensive at best and
dangerous at worst, because
they create a hidden tight
coupling of a given model to
other parts of the stack/system.

28. 28
D.H. Wolpert etal. (1997)“NoFreeLunch TheoremsforOptimization”.
For any ML solution,
any increase in performance
over one class of problems
is offset by lower performance
over another class.
No free lunch theorem ...

29. Next developing steps.
29
Developing a Big Data Architecture in the Tactile Domain
Study Real Time (e.g., ms – sec domain) requirements.
Study System Engineering requirements for Tactile Applications.
Several proof of concepts.
Developing RT Applied Machine Learning expertise
Feasibility study of Deep Learning Algorithms applied to RT.
Applied Machine Learning in Tactile Domain, e.g., dynamic algorithms.
Alternatives: Genetic algorithms, scale-free networks.
Developing re-enforcement learning applications.
Spectrum auctions, network management, customer experience, self-
optimized network applications, etc..

30. 30
Finally.

31. Thank
you!