Daniel Hulme is the director of an applied artificial intelligence program and founder and CEO of Satalia. He has a PhD in artificial intelligence and is a senior lecturer in AI and innovation. He is also an advisor to several organizations on AI strategy. The document discusses artificial intelligence, data-driven decision making, and innovation. It presents models of data, information, knowledge and wisdom and describes different types of artificial intelligence and challenges related to talent strategy.

1. Get noticed | Get connected | Get ahead

2. Get noticed | Get connected | Get ahead
Daniel Hulme - Satalia
• Director of Applied Artificial Intelligence MSc
• PhD in Artificial Intelligence
• Senior Lecturer in AI and Innovation
• Postdoc in Innovation and Technology Transfer
• MBA Electives London Business School
• MSci in Artificial Intelligence
• Founder & CEO of Satalia
• Advisor to UAE AI Strategy
• Co-founder of the ASI Data Science
• Advisor to NOIA Network and others
• Kauffman Global Scholar
• Startup mentor and speaker
2

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Artificial Intelligence

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WISDOM
UNDERSTANDING
KNOWLEDGE
INFORMATION
DATA
Data Driven Decision Making

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JOHN READ THE LETTER TO MARY

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Data Driven Decision Making
WISDOM
UNDERSTANDING
KNOWLEDGE
INFORMATION
DATA = symbols
= contextualized DATA

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Data Driven Decision Making
WISDOM
UNDERSTANDING
KNOWLEDGE
INFORMATION
DATA = symbols
= contextualized DATA
= organised INFORMATION

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Data Driven Decision Making
WISDOM
UNDERSTANDING
KNOWLEDGE
INFORMATION
DATA = symbols
= contextualized DATA
= organised INFORMATION
= interpreted KNOWLEDGE

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Data Driven Decision Making
= symbols
= contextualized DATA
= organised INFORMATION
= interpreted KNOWLEDGE
= utilised UNDERSTANDING
WISDOM
UNDERSTANDING
KNOWLEDGE
INFORMATION
DATA

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Decision Making

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Resource Allocation
RULES
Flying right of walking
Swimming left of walking
Reptiles 2-away from felines
No same colours touching
Tails prefer next to tails
Males prefer near females

19. Get noticed | Get connected | Get ahead
RULES
Flying right of walking
Swimming left of walking
Reptiles 2-away from felines
No same colours touching
Tails prefer next to tails
Males prefer near females
Resource Allocation 120

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Resource Allocation
RULES
Flying right of walking
Swimming left of walking
Reptiles 2-away from felines
No same colours touching
Tails prefer next to tails
Males prefer near females

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Resource Allocation 1,307,674,368,000
RULES
Flying right of walking
Swimming left of walking
Reptiles 2-away from felines
No same colours touching
Tails prefer next to tails
Males prefer near females

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23. Get noticed | Get connected | Get ahead
500! ≈ 1e+1134

24. Get noticed | Get connected | Get ahead
500! ≈ 1e+1134
12201368259911100687012387854230469262535743428031928421924135883858453731538819976054964475022032818630136164771482035
84163378722078177200480785205159329285477907571939330603772960859086270429174547882424912726344305670173270769461062802
31045264421887878946575477714986349436778103764427403382736539747138647787849543848959553753799042324106127132698432774
57155463099772027810145610811883737095310163563244329870295638966289116589747695720879269288712817800702651745077684107
19624390394322536422605234945850129918571501248706961568141625359056693423813008856249246891564126775654481886506593847
95177536089400574523894033579847636394490531306232374906644504882466507594673586207463792518420045936969298102226397195
25971909452178233317569345815085523328207628200234026269078983424517120062077146409794561161276291459512372299133401695
52363850942885592018727433795173014586357570828355780158735432768888680120399882384702151467605445407663535984174430480
12893831389688163948746965881750450692636533817505547812864000000000000000000000000000000000000000000000000000000000000
0000000000000000000000000000000000000000000000000000000000000000

25. Get noticed | Get connected | Get ahead
500! ≈ 1e+1134
12201368259911100687012387854230469262535743428031928421924135883858453731538819976054964475022032818630136164771482035
84163378722078177200480785205159329285477907571939330603772960859086270429174547882424912726344305670173270769461062802
31045264421887878946575477714986349436778103764427403382736539747138647787849543848959553753799042324106127132698432774
57155463099772027810145610811883737095310163563244329870295638966289116589747695720879269288712817800702651745077684107
19624390394322536422605234945850129918571501248706961568141625359056693423813008856249246891564126775654481886506593847
95177536089400574523894033579847636394490531306232374906644504882466507594673586207463792518420045936969298102226397195
25971909452178233317569345815085523328207628200234026269078983424517120062077146409794561161276291459512372299133401695
52363850942885592018727433795173014586357570828355780158735432768888680120399882384702151467605445407663535984174430480
12893831389688163948746965881750450692636533817505547812864000000000000000000000000000000000000000000000000000000000000
0000000000000000000000000000000000000000000000000000000000000000
atoms ≈ 1e+80

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Transportation Problems

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Transportation Problems
# ROUTES
1,000,000
PER
SECOND
10 3,628,800 4 seconds
11 39,916,800 1 minute
13 6,227,020,800 2 hours
14 87,178,291,200 1 day
16 20,922,789,888,000 1 year
20 2,432,902,008,176,640,000 77,000 years
22
1,124,000,727,777,610,000,00
0
36 millennia
24
620,448,401,733,239,000,000,
000
20B years

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Data Driven Decision Making
WISDOM
UNDERSTANDING
KNOWLEDGE
INFORMATION
DATA
ACTION
INSIGHT
DATA
Prescriptive analytics
● Optimisation
● Decision-science
Predictive analytics
● Data-science
● Machine learning
Descriptive analytics
● Contextualisation
● Data visualisation
Data Engineering
● Infrastructure
● Data assimilation

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Artificial Intelligence
ACTION
INSIGHT
DATAADAPT
Intelligence: Goal-directed
Adaptive Behaviour
Sternberg & Salter

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Symbolic AI

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Sub-symbolic AI

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Artificial Intelligences
Artificial
Intelligence
bounds of
human ability

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Artificial
Intelligence
Artificial
General
Intelligence
bounds of
human ability
Artificial Intelligences

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Artificial
Intelligence
Artificial
General
Intelligence
Super
Intelligence
bounds of
human ability
Artificial Intelligences

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Simulation Theory

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Ethical Decision Making

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Ethical AI, Explainable Algorithms and
GDPR

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Innovation

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Hidden Dynamics in Hierarchy

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Innovation, Motivation and Purpose
“Creativity that ships” - Steve Jobs
“Motivation is the Energy for Action” -
Edward Deci
• “Autonomy, Mastery, Purpose” - Dan Pink,
Drive
“Time is the new money” - Richard Branson
The rise of the purposeful company
DATA SCIENCE
CLOUD COMPUTING
OPEN (BIG) DATA
ARTIFICIAL INTELLIGENCE
TALENT

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Talent Strategy
Industry Sexiness
ChallengingProblems

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Talent Strategy
Industry Sexiness
ChallengingProblems
No talent

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Talent Strategy
Industry Sexiness
ChallengingProblems
No talent
Sticky talent

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Talent Churn
No talent
Sticky talent
Talent Churn
Talent Strategy
Industry Sexiness
ChallengingProblems

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Talent Churn
No talent
Sticky talent
Talent Churn
Talent Strategy
Industry Sexiness
ChallengingProblems

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Fat
Thin
Lean
(Distributed)

47. Get noticed | Get connected | Get aheadStrictly Confidential
People Networks

48. Get noticed | Get connected | Get aheadStrictly Confidential
Team Formation
Team formation

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Real time, high
definition data analytics
of a self-organising
company:
Productivity
Costs
Engagement
Priorities
Sentiment

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Swarm diagrams provide
transparent data on
activity across network
tools.
Interactive network
diagrams show
relationships and the flow of
information from and to the
worker.
Swarm diagrams show the
significance of points and
the weight of opinion
compared to the
suggestions received by
others within the
organisation.

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T Algorithm
The Four by Scott Galloway - 8 success metrics:
1. Product Differentiation
2. Visionary Capital
3. Global Reach
4. Likability
5. Vertical Integration
6. Are we a Benjamin Button company?
7. Are we a talent accelerator?
8. Geography

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Social Scoring

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Capturing Attention

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The Two Singularities and 2048

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Purpose
Enable
everyone to do
what they love

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Daniel Hulme
daniel@satalia.com
www.satalia.com
+447773765097
@TheSolveEngine