Kaimar karu - The Real Promise of AI Beyond The Hype

Beyond the Hype:
the Real Promise of AI
KAIMAR KARU
MindBridge

@kaimarkaru© Mindbridge 2019
Presenter: Kaimar Karu»
Teaching and Training
Project Management
IT Service Management
Appreciating good beer
Software Development
IT Operations and Support
itSMF
@kaimarkaru
Agile & DevOps

Agenda
1. Our Robot Overlords
2. Types and Sub-Types of AI, Machine Learning and Deep Learning
3. AI as an Existential Threat
4. AI, Philosophy, and Ethics
5. The AI race
6. The promise of AI in IT Management
»

Our Robot OverlordsAI
https://youtu.be/LikxFZZO2sk

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

„More human than
human“ is our motto.
Blade Runner (1982)
AI

I'll be back!
The Terminator (1984)
AI

The mind is a terrible
thing to waste - don't
make me waste yours.
Class of 1999 (1990)
AI

Never send a human to
do a machine’s job.
The Matrix (1999)
AI

I'm becoming much
more than they
programmed.
I'm excited!
Her (2013)
AI

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
AI

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.
AI

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

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

Cucumber sorterAI
https://youtu.be/4HCE1P-m1l8

Fear, Uncertainty, and Doubt (FUD)
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)
AI

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

Human Nervous System
https://twitter.com/fredwumd/status/1007737117131399168
Spinal cord»
Enteric nervous system»
Nerves»
Brain»
AI

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

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

Neural Networks in Deep Learning
„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
DL

Applying DL: Natural Language Processing
Speech recognition»
Machine translation»
Sentiment analysis»
Entity extraction»
Information extraction»
Natural language generation»
Summarization»
Question answering»
DL

Challenges and risks with Deep Learning
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
DL

Immediate Artificial Intelligence related risks
Fake news (both speed and spread)»
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»
AI

Fake videosAI
https://youtu.be/emeIRYffz6c

Existential ThreatAI

Predictions for AGI
Median optimistic year 10% likelihood 2022
Median realistic year 50% likelihood 2040
Median pessimistic year 90% likelihood 2075
42% of respondents 2030
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
AI

Philosophical aspects
CONSCIOUSNESS FREE WILL INTENTIONALITY BELIEF
AI

Ethical challenges: bias correction
BIASED DATA?»
BIASED ALGORITHM?»
BIASED ESTIMATOR?»
AI

Ethical challenges: rules-based decision-making
TRANSPARENCY»
EXPLAINABILITY»
INTENTIONALITY»
AI

Ethical challenges: empathy
POLICE SOLDIERJUDGE SUPPORTTHERAPIST
AI

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

Challenges: ‘AI race’ and national policiesAI

Challenges and opportunities: normative rulesAI

Benefits of AI (or, well, ML) for IT Management
INCREASED
PRODUCTIVITY
IMPROVED
DECISION-MAKING
IMPROVED
COMMUNICATION
IMPROVED RISK
MANAGEMENT
INCREASED
SPEED
REDUCED
COSTS
IMPROVED
ANALYTICS
IMPROVED
KNOWLEDGE MGMT
AI

Which then gives hope for
ENTERPRISE
SERVICE MANAGEMENT
IMPROVED
CUSTOMER EXPERIENCE
IMPROVED
EMPLOYEE SATISFACTION
AI

Tooling
Ask your tool vendor
about their ML roadmap!
AI

The biggest challenge when leveraging AI
Ask the right questions.
AI

Get in touch
@kaimarkaru
linkedin.com/in/kaimar
medium.com/@kaimarkaru

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