The document discusses the transformative potential of artificial intelligence (AI) and machine learning for businesses, highlighting a significant increase in their perceived importance among CEOs from 54% in 2016 to 81% in 2017. It emphasizes the shift towards intelligent empowerment and composable intelligence, where AI augments human capabilities rather than replacing them, and outlines various problem-solving applications of AI. The text also addresses challenges such as ethics, governance, and the iterative nature of building AI solutions.

1. 1
CONTINUOUS
INTELLIGENCE
David Colls
ThoughtWorks Live 2018

2. 2
ARTIFICIAL
INTELLIGENCE

3. AMAZING OPPORTUNITY
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
The business plans of the next 10,000 start-ups
are easy to forecast: take X and add AI.
Kevin Kelly – The Inevitable
“
“

4. AND UNPRECEDENTED RESOURCES
2
DEDICATED
PARALLEL
HARDWARE
3
ADVANCED
MODELLING
1
WEB-SCALE
DATA
DATA VOLUMES
DOUBLE EVERY YEAR
MASSIVE ADOPTION OF GPU
(AND TPU, HPU, FPGA)
LATEST RESEARCH
AND TOOLS

5. NO WONDER AI GENERATES
INTEREST FROM LEADERS
But the big change from last
year is that 81% cited
artificial intelligence and
machine learning as either
very important or extremely
important to their company’s
future, up from just 54% in
2016.
Fortune.com Fortune 500 CEO survey 2017
81% “
“

6. There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
“ “
THE HYPE PROBLEM
So how do we think about opportunities?
AI CAN DO ANYTHING
WE WANT (IT’S MAGIC)

7. There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
“
AND THE KERNEL OF THE TRUTH
“
AND ABOUT
PEOPLE
AND WE CAN
BUILD
ITERATIVELY
AI IS NARROW
(BUT COMPOSABLE)
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable
There is almost nothing we can think of that
cannot be made new, different, or more valuable
by infusing it with some extra IQ.
Kevin Kelly – The Inevitable

8. 8
INTELLIGENT
EMPOWERMENT

9. AUGMENTATION OVER AUTOMATION

10. AUGMENTATION OVER AUTOMATION
MACHINES
Wider learners
Scalable thinkers
HUMANS
Faster learners
Flexible thinkers
+

11. THE INTELLIGENT EMPOWERMENT CHALLENGE
High volume
judgement tasks are
high cost
or variable quality
High quality human
judgement is hard to
deliver at speed or
scale
Empower all your
people with the
judgement of your
best performers
Leadership
Lenses
BUSINESS
CUSTOMERS
EMPLOYEES
Outperform with insight
Experience driven by AI
Your people at their best

12. 12
COMPOSABLE
INTELLIGENCE

13. PROBLEM EXAMPLE
PREDICT A NUMBER
● How much will this house sell for?
● How many shoes will sell this week, based on recent sales?
BINARY PREDICTION
● Will this customer churn this week?
● Is this transaction fraudulent?
PREDICT A CATEGORY
● Is this customer NEW, LOYAL, FICKLE, …?
● Is the t-shirt in this image CREW, V-NECK, POLO, …?
UNDERSTANDING
NATURAL LANGUAGE
● What is the sentiment of this text?
● What action and parameters is a customer requesting?
RECOMMENDATION
● Given your purchases, what else might you buy?
● Given your contacts, who else might you know?
WHAT TYPES OF PROBLEMS?

14. 3
CONSTRUCTION:
Weatherboard
EXIF LOCATION
BEDROOMS
PRICE: $1M
COMPOSABLE SOLUTIONS
BEDROOMS:
3
PHOTO
CBD DISTANCE:
10km

15. CONSTRUCTION:
Weatherboard
EXIF LOCATION
CBD DISTANCE:
10km
EVOLVABLE COMPOSABLE SOLUTIONS
BEDROOMS:
3
PHOTO
CONDITION:
Good
PRICE: $1.1M

16. 16
CONTINUOUS
INTELLIGENCE

17. THE NEW NEW PRODUCT
DEVELOPMENT GAME
PRODUCT
RULES
+ Software architecture
CODE
Research
& Analyse
Code
Deploy
Test
Validate
CUSTOMER OBJECTIVES

18. THE NEW NEW NEW PRODUCT
DEVELOPMENT GAME
Software Engineering Machine Learning
DATA SET
+ Network architecture
MODEL
Research
& Analyse
Code
Deploy
Test Test
Validate
Curate &
Transport
Train
Deploy
RULES
+ Software architecture
CODE
PRODUCT
CUSTOMER OBJECTIVES

19. THE NEW NEW NEW PRODUCT
DEVELOPMENT GAME
Software Engineering Machine Learning
Validate
RULES
GENERATE
DATA
USAGE
GENERATES
DATA
RULES
+ Software architecture
CODE
PRODUCT
CUSTOMER OBJECTIVES
DATA SET
+ Network architecture
MODEL

20. DEPLOYMENT
REQUIRES
ENGINEERING
TRANSPORT
REQUIRES
ENGINEERING
THE NEW NEW NEW PRODUCT
DEVELOPMENT GAME
Software Engineering Machine Learning
Validate
RULES
+ Software architecture
CODE
PRODUCT
CUSTOMER OBJECTIVES
DATA SET
+ Network architecture
MODEL

21. Model
Capacity
Data
Estate
PROGRESS
SWEET ZONE
X
RAPID START BUT FRAGILE AND
LIMITED DIFFERENTIATION
(EG MLAAS)
X
SIMPLE TO DO BUT LEAVES
UNREALISED DATA VALUE
(EG SPREADSHEETS)
ITERATIVE SOLUTIONS

22. PoC IDEA
TOO MANY PoCs?
MAKE IT
SIMPLER
TEST IN
LAB?
DEPLOY TO
PROD
COLLECT,
EVALUATE
MODEL
ITERATION
DARK
LAUNCH
OR A/B
TEST IN
LAB
ADD VALUE
UNCERTAIN
HOW TO ADD
VALUE
PoC IDEA
CLEAR VALUE
ADD
REPEAT! REPEAT!

23. 23
ML PRODUCT
SQUADS

24. ML Principals
Product Squads
Scale Partners
ML PRODUCT SQUADS
Self-service data platform
Consumer
responsibilities
Producer
responsibilities
Organisational agility
and learning culture
HCD+ML Diverse Teams

25. 25
ETHICS, RISK
& GOVERNANCE

26. RISKS
Bias
Explainability
Attack Susceptibility
! !
TECHNOLOGY RESPONSES
Baseline Model
Asymmetric Costs
Wide & Deep Networks
Fallback on low Confidence
Use Canaries as Early Warning
Black/white list inputs/outputs

27. In 2018, AI will gain a moral compass
MUSTAFA SULEYMAN, DeepMind
“
“

28. CORRUPTION OF SOCIAL SPACES
DATA COLLECTION & SECURITY
SOCIAL LICENCE
ETHICS OF ALGORITHMIC DECISION MAKING
AUTONOMOUS WEAPONS

29. 29
GETTING STARTED

30. We Can Build
Iteratively
AI is Narrow
It’s About People
CONTINUOUS
INTELLIGENCE
COMPOSABLE
INTELLIGENCE
INTELLIGENT
EMPOWERMENT
THE NATURE OF SOLUTIONS

31. WHERE TO FIND OPPORTUNITY
BUSINESS
CUSTOMERS
EMPLOYEES
VOLUME QUALITY SCALE

32. VERY FIRST STEPS
! Introduce a step for Machine Learning
in your data pipeline (input == output)
! Look for a binary Yes/No suggestion or a
suggested delta to existing results
! Then iterate

33. THANKS
David Colls- dcolls@thoughtworks.com