This pragmatic session from ITSM18 focused on the ""so what"" questions related to Artificial Intelligence (AI). It covered the real-world technical capabilities based on current practices and the latest AI research, as well as the philosophical aspects and ethical implications of designing, developing, and operating human-impacting systems that leverage machine learning and advanced AI capabilities. Kaimar specifically looked at the impact of AI on agile software development and technical IT operations, plus he discussed how to tune in and cut through the (marketing) noise of "AI-as-a-buzzword". Take a look at his presentation to find out the direction that AI is heading, key practical challenges, and the differences between AI, machine learning, and deep learning among other things. Discover what aspects of your role is or could be impacted by AI, and what it means in practical terms beyond the "your job will disappear" scare. Plus, learn practical considerations for leveraging AI in a responsible and ethical manner.

1. Beyond the Hype:
the Real Promise of AI
Kaimar Karu, MindBridge
@kaimarkaru

2. Agenda
1. Our Robot Overlords
2. Types and Sub-Types of AI
3. Machine Learning and Deep Learning
4. AI as an Existential Threat
5. AI, Philosophy, and Ethics
6. The AI race
7. The promise of AI in ITSM
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3. Presenter: Kaimar Karu
Teaching and Professional Training
Project Management
IT Service Management
Appreciating good beer
Software Development
IT Operations and Support
»
itSMF

4. Our Robot OverlordsAI
@kaimarkaru

5. Everything is going according to plan #1AI
@kaimarkaru
I'm sorry, Dave. I'm
afraid I can't do that.
2001: A Space Odyssey (1968)

6. Everything is going according to plan #2AI
@kaimarkaru
„More human than
human“ is our motto.
Blade Runner (1982)

7. Everything is going according to plan #3AI
@kaimarkaru
I'll be back!
The Terminator (1984)

8. Everything is going according to plan #4AI
@kaimarkaru
The mind is a terrible
thing to waste - don't
make me waste yours.
Class of 1999 (1990)

9. Everything is going according to plan #5AI
@kaimarkaru
Never send a human to
do a machine’s job.
The Matrix (1999)

10. Everything is going according to plan #6AI
@kaimarkaru
I'm becoming much
more than they
programmed.
I'm excited!
Her (2013)

11. Artificial Intelligence, Machine & Deep Learning
AI ML DL
The capability of a machine to imitate intelligent human behavior.
The capability to learn (without explicit programming) by
analyzing data using statistical methods and techniques.
Unsupervised learning capabilities
inspired by information processing
in biological nervous systems.
@kaimarkaru
AI

12. The (ancient) history of Artificial Intelligence
The Turing Test Machine or human?1950
Dartmouth Workshop „Artificial Intelligence“1956
1st AI Winter Machine Translation1960s
2nd AI Winter Limited domain language1970s
3rd AI Winter Non-extending expert systems1980s
The Chinese Room „AI is contradiction in terms“1984
@kaimarkaru
AI

13. Three types of Artificial Intelligence
NARROW AI (ANI)
GENERAL AI (AGI)
SUPER AI (ASI)
Multi-domain application
Single-domain application
Smarter than humans
AI
»
»
»
@kaimarkaru

14. What does it mean to think? To be intelligent?AI
@kaimarkaru
Is the brain like a computer?
Should the computer operate like a brain?

15. Human Nervous SystemAI
@kaimarkaruhttps://twitter.com/fredwumd/status/1007737117131399168
Spinal cord»
Enteric nervous system»
Nerves»
Brain»

16. Fear, Uncertainty, and Doubt (FUD)AI
@kaimarkaru
Terrifying promises,
mundane reality.
„Every time we figure out a piece of it, it stops being magical;
we say, ‘Oh, that's just a computation.’“ (Rodney Brooks)

17. Artificial Intelligence (-ish) in our daily lives
@kaimarkaru
AI
WEB
SEARCH
MAPS AND
NAVIGATION
TICKET
PRICING
SONG
RECOGNITION
CHATBOTS AND
AUTO-RESPONSES
SPORTS EVENTS
COVERAGE
FOOTBALL
GAME ANALYSIS
CUCUMBER
SORTER

18. Cucumber sorterAI
@kaimarkaru

19. Machine Learning styles
SUPERVISED
LEARNING
UNSUPERVISED
LEARNING
REINFORCEMENT
LEARNING
Finding patterns in data.
Do you have labelled data?
Achieving rewards.
ML
»
»
»
@kaimarkaru

20. Deep Learning models
MULTILAYER
NEURAL NETWORKS
CONVOLUTIONAL
NEURAL NETWORKS
RECURRENT
NEURAL NETWORKS
Work well with data that has a spatial
relationship (e.g. image recognition).
Work well with prediction problems for
labelled or classified inputs
(e.g. structured datasets).
Work well with sequence prediction
problems (e.g. natural language
processing).
DL
»
»
»
@kaimarkaru

21. Neural Networks in Deep LearningDL
@kaimarkaru
„I believe Deep Learning is our best shot at progress towards real AI.“
(Andrew Ng)
„Deep Learning“, Adam Gibson, Josh Patterson, O'Reilly Media 2017

22. Applying DL: Natural Language Processing
Speech recognition»
@kaimarkaru
DL
Machine translation»
Sentiment analysis»
Entity extraction»
Information extraction»
Natural language generation»
Summarisation»
Question answering»

23. Challenges and risks with Deep LearningDL
@kaimarkaru
CONTEXT
UNAWARENESS
LACK OF
DATA
LACK OF
LABELLED DATA
HIGH RELIANCE ON
TRAINING DATA
PROCESSING POWER
REQUIREMENTS
BLACK BOX
ALGORITHMS
LACK OF
FLEXIBILITY
BUTTERFLY
EFFECT

24. Immediate Artificial Intelligence related risks
Fake news (both speed and spread)»
@kaimarkaru
AI
Impact on financial institutions (e.g. 2010 Flash Crash)»
Cyber attacks (on nations)»
Social media related anxiety (curated feeds)»
Surveillance (privacy)»
Spear phishing»
‘AI race’»
Fake audio and fake video»

25. Fake videosAI
@kaimarkaru

26. Predictions for AGIAI
@kaimarkaru
Median optimistic year 10% likelihood 2022
Median realistic year 50% likelihood 2040
Median pessimistic year 90% likelihood 2075
42% of respondents 2030
25% of respondents 2050
20% of respondents 2100
10% of respondents After 2100
2% of respondents Never
AI experts’ opinion
Vincent C. Müller and
Nick Bostrom, 2013
AGI conference participants
James Barrat, 2013

27. Existential ThreatAI
@kaimarkaru

28. Philosophical aspectsAI
@kaimarkaru
CONSCIOUSNESS FREE WILL INTENTIONALITY BELIEF

29. Ethical challenges: bias correctionAI
@kaimarkaru
BIASED DATA?»
BIASED ALGORITHM?»
BIASED ESTIMATOR?»

30. Ethical challenges: rules-based decision-makingAI
@kaimarkaru
TRANSPARENCY»
EXPLAINABILITY»
INTENTIONALITY»

31. Ethical challenges: empathyAI
@kaimarkaru
POLICE SOLDIERJUDGE SUPPORTTHERAPIST

32. Challenges: legislation
GDPR Recital 71:
In order to ensure fair and transparent processing in respect of the data subject, taking into account the specific
circumstances and context in which the personal data are processed, the controller should use appropriate
mathematical or statistical procedures for the profiling, implement technical and organisational measures
appropriate to ensure, in particular, that factors which result in inaccuracies in personal data are corrected and the
risk of errors is minimised, secure personal data in a manner that takes account of the potential risks involved for
the interests and rights of the data subject, and prevent, inter alia, discriminatory effects on natural persons on the
basis of racial or ethnic origin, political opinion, religion or beliefs, trade union membership, genetic or health
status or sexual orientation, or processing that results in measures having such an effect.
GDPR Article 22:
The data subject shall have the right not to be subject to a decision based solely on automated processing,
including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.
AI
@kaimarkaru

33. Challenges: ‘AI race’ and national policiesAI
@kaimarkaru

34. Challenges and opportunities: normative rulesAI
@kaimarkaru

35. Benefits of AI (or, well, ML) for ITSMAI
@kaimarkaru
INCREASED
PRODUCTIVITY
IMPROVED
DECISION-MAKING
IMPROVED
COMMUNICATION
IMPROVED RISK
MANAGEMENT
INCREASED
SPEED
REDUCED
COSTS
IMPROVED
ANALYTICS
IMPROVED
KNOWLEDGE MGMT

36. Which then gives hope forAI
@kaimarkaru
ENTERPRISE
SERVICE MANAGEMENT
IMPROVED
CUSTOMER EXPERIENCE
IMPROVED
EMPLOYEE SATISFACTION

37. ITSM toolingAI
@kaimarkaru
Ask your ITSM tool vendor
about their ML roadmap!

38. The biggest challenge when leveraging AIAI
@kaimarkaru
Ask the right questions.