Presentations from the AI conference held by EiTESAL that showcases the challenges met in the AI market in Egypt. presented by eng. Ahmed Hassaan from Orange.

1. Artificial
Intelligence
Challenges

2. HELLO!
I am Ahmed Hassaan
A Computer Engineer, delivering Software
Solutions to Telecom industry since 2007
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- Snr Manager of Data Lake and Analytics
@ Orange Egypt
- Software Expert @ Orange France

3. Why am I Here?
● I’m not here to teach you AI.
● I’m not using models or any AI
tools anyways… expert
practitioners are there to ask
them for details.
● I may be telling you if AI is
good for you, and how to play
it safe in enterprises.
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4. Let me tell you a story
“5 years ago, there was a software developer
who was chased by a dream of Artificial
Intelligence and its capabilities”

5. ⊗ Career path
Would you consider a job as an "automation ethicist"? How about
that of an "interactive chatbot designer"?
⊗ Science to learn
Well, the science is there since 60’s, and we have studied some in
college.
⊗ Tempted by Machine Learning
I used to give computers data and application to get results, not
to give it data and results to get the application
For the dream to come true
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6. 6
Topic Popularity
A quick search on
Google Ngram for books
from 1800 to 2008 for
the word Artificial
Intelligence
[case-sensitive to force the AI
meaning as we know it today]
You can find it here: http://bit.ly/2maEnSj

7. ⊗ AI effect
Tesler's Theorem
"AI is whatever hasn't been done yet."
Means I’ll be doomed in developing models for the rest of my life
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Should I get out of my comfort zone?
it’s not that comfy anyways 😉

8. I’ve found the secret spice
$Put_AI_into_action -create_team 5 -get_machines 10
>Error: insufficient system resources
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9. What’s AI?
To appreciate artificial intelligence we have to
know what’s natural intelligence

10. As per Oxford dictionary
Intelligence:
“the ability to acquire and
apply knowledge and skills.”
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11. Impossible to simulate
⊗ Can you simulate something you can’t understand?
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Scientists are identifying how human intelligence works
“by observing how it should not work”

12. In Pop culture
Artificial Intelligence in the eyes of lay people… wow!
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13. AI is the future, but it comes at a price, that nobody will
afford to pay
⊗ Existential risk
Microsoft founder Bill Gates, and SpaceX founder Elon Musk have
expressed concerns about the possibility that AI could evolve to the point
that humans could not control it,
In the eyes of the knowledgeable
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14. “The development of full artificial intelligence could
spell the end of the human race. Once humans
develop artificial intelligence, it will take off on its
own and redesign itself at an ever-increasing rate.
Humans, who are limited by slow biological
evolution, couldn't compete and would be
superseded.”
-- Stephen Hawking
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15. ⊗Decrease in demand
for human labor
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"the worry that AI could do to
white-collar jobs what steam
power did to blue-collar ones
during the Industrial Revolution"
is "worth taking seriously"
-- "Automation and anxiety". The
Economist. 9 May 2015

16. “Amazon scraps secret AI recruiting tool that
showed bias against women”
-- Jeffrey Dastin, OCTOBER 10, 2018 Reuters
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Discrimination

17. Artificial Intelligence
is not science fiction anymore!
It’s a fact that we have to make good use of!
But Indeed
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18. The Boiling Frog Syndrome
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An Enterprise is smart enough to
adapt but slow enough to avoid the
boiling frog syndrome.
And AI is not an exception!
“Fast adaptation with
no wisdom can kill your
business”

19. Why an Enterprise
would need AI?
Things that humans can’t do at a profitable
team size and time frame.

20. ⊗ Fraud detection
⊗ Recommendation Systems
⊗ Chatbots
⊗ Autonomous network optimization
⊗ … etc.
Examples of use-cases in telecom industry
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21. ⊗ 20 years ago > Scripts, rules engines, macros
⊗ 10 years ago > Robotic process automation
(as old as screen scrapers)
⊗ 5 years ago > intelligent process automation
(unassisted RPA)
⊗ Future > artificial intelligence
Many Enterprises are stuck
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22. Challenge #1
Acquiring talents

23. Place your screenshot here
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Talents
This graph shows the
projected strategies
companies will use to
meet the shift in skills
requirements
--MARCH 29, 2019MARCH 29,
2019
https://www.getsmarter.com/blog/career-advice/career-path-artificial-
intelligence-and-robotics/

24. Hiring is not an easy task, as you have to hunt for the
qualified skills and build the ideal mix of people to get
the job done.
⊗ Most of AI applied in Telecom industry implies
machine learning [ML]
⊗ A data scientist is not a Data Engineer
⊗ You will always need a software developer to put
models into production
Are you looking for the right skills?
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25. Challenge #2
Data collection & Preparation

26. In the telecom industry, it’s very easy to get lost if you don’t know where to
get your data from. Modern Telecommunication systems use a jumble of
parameters and events to control subscriber interactions.
One would rely on charging and billing events, while the other would go
deeper with a network probe CDR.
The thing to keep in mind is: your data comes at high speed, with enormous
amounts of columns and features, and an uncontrollable amount of rows.
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27. For a successful use case, the right sources needs to be picked up. And
who knows the ins and outs of a telecom operator better than subject matter
experts [SME]
For any enterprise, an SME on board with the Machine Learning task
force is a key to lower time, and expenses.
Sometimes manual pre-filtration of significant features may be
needed.
It takes a domain expert to do the job
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28. Challenge #3
The Infrastructure

29. Despite the fact that, “Enterprises are used
to huge loads of processing and storage”
The amount and type of machine power
needed to crunch these amounts of data and
carry out Model training is another story
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http://bit.ly/2mc9gFX -- Shachi Shah, Mar 26, 2018

30. Unless your datacenter neighbour is
a nuclear reactor, you have to think
twice or pay the utility bill
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Power Consumption

31. Place your screenshot here
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Power
According to Oracle
Sun Systems
Where SWaP (space, wattage
and performance) is a metric for
data centers, incorporating
energy and space
http://bit.ly/2khCUZT, -- Marco Chiappetta, APR 9, 2014, PcWorld
Place your screenshot here

32. Challenge #4
Making it profitable

33. Minimize expenses
📖 Skills
Learning Machine
Learning.
Train your SME’s,
instead of teaching
your business rules to
new data scientists
💰 Buy Models
Some teams lack the
knowledge to develop
their own models, and
prefer to buy trained
and tested models, to
run on their data.
⛅ IaaS
Sometimes, hosting
the whole process on
cloud may make the
difference; either
public or on premises
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34. ⊗ Stick to open source tools and software stacks
⊗ Big Data is your friend
Use it to unleash the power of machines
Last but not least
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35. THANKS! Any questions?
You can get this
Presentation
from here
http://bit.ly/2mi2r5X
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And you can find me here
http://ahmedhassaan.com