Machines are now bettering people at a range of specific intuition tasks. How are they doing this and what might be next? What does this mean for the development and governance of intelligent products and services that we access as consumers and citizens, and what are the implications for the organisations that provide them? David explores the impact across the product lifecycle, from customer perception, to job design, technology development, knowledge management, risk and ethics.

1. Reasoning About
MACHINE INTUITION
David Colls

2. GENERAL NARROW
MACHINE
LEARNING
DEEP
LEARNING*
REASONING INTUITING
ARTIFICIAL INTELLIGENCE
*AKA ARTIFICIAL NEURAL NETWORKS

3. MACHINE
INTUITION,
FTW

4. MACHINES BESTING HUMANS
4
1997 20172007
Chess* Face recognition
Jeopardy!
Go
Poker
Conversational speech recognition,
Lipreading, Guessing locations
Atari games, Image classification,
Deceptive pain expressions, Skin cancer diagnosis
https://finnaarupnielsen.wordpress.com/2015/03/15/status-on-human-vs-machines/
Dota-2

5. PERFORMANCE LEAPS
5

6. PERFORMANCE LEAPS
6

7. PERFORMANCE LEAPS
7

8. BESTING HUMANS
8

9. BESTING HUMANS
9

10. BESTING HUMANS
10
Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm

11. WHAT JUST
HAPPENED?

12. WHAT IS AN ARTIFICIAL NEURAL NETWORK?
12
BEACH
SEA
SKY
…

13. WHAT IS AN ARTIFICIAL NEURAL NETWORK?
12
© Fjodor van Veen 2016
BEACH
SEA
SKY
…

14. WHAT IS AN ARTIFICIAL NEURAL NETWORK?
12
Weights of
connections
between nodes
repeatedly
adjusted as
network “learns”
training set of
labelled images
1.
TRAINING
Network can
classify novel
images with
“learned” labels
2.
USAGE

15. EXERCISE
13
1
2
3
4
5
A
BEACH
B
C

16. NEURAL NETWORKS HAVE BEEN AROUND FOR A WHILE…
14
Dave’s 1994 university internship

17. …BUT THEN THIS HAPPENED
15
WEB-SCALE
DATA
1
Data volumes double
every year

18. Massive adoption of GPU
(and TPU, HPU, FPGA)
2
DEDICATED
PARALLEL
HARDWARE
…BUT THEN THIS HAPPENED
16

19. 3
ADVANCED
NETWORK
DESIGNS
…BUT THEN THIS HAPPENED
17
© Fjodor van Veen 2016

20. NETWORK PARAMETERS - STATE OF THE ART
18
2013 201720152014 20162012
160 B
11.2 B
1.7 B
1 Billion
10 Billion
100 Billion
1,000 Billion

21. SO, WHAT’S
NEXT?

22. EXPLOSION OF MICRO-INTUITORS
20
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
topbots.com
133 Enterprise AI Startups

23. HUMANS AND MACHINES COMPLEMENTING EACH OTHER
21
MACHINES
Wider learners
Scalable thinkers
HUMANS
Faster learners
Flexible thinkers

24. DISTRIBUTED MACHINE INTUITION
22
Apple iOS Core ML
Google Federated Learning
CLOUD TRAINING DEVICE INTUITION
AGGREGATED DATA PRIVATE DATA
FEDERATING LEARNING

25. PRO-ACTIVE DISCUSSION AND MANAGEMENT OF RISKS
23

26. 24
com·put·er
/kəmˈpyo͞ odər/
noun
(17th century)
“one who computes”

27. de·ci·sion mak·er
/dəˈsiZHən/ ˈ/mākər/
noun
(20th century)
“one who decides”
25
Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm

28. DESIGNING
PRODUCTS
WITH INTUITION

29. UNDERSTAND CUSTOMER PERCEPTION
27
Just 7 percent of respondents would
trust a robot with their savings,
versus the 14 percent willing to submit
to a machine for heart surgery.
Andy Maguire
COO HSBC Europe

30. BUILD CUSTOMER TRUST
28
https://hbr.org/2017/04/to-get-consumers-to-trust-ai-show-them-its-benefits
OPERATIONAL SAFETY & DATA SECURITYHygiene
1
COGNITIVE
COMPATIBILITY
2
TRIALABILITY
3
USABILITY
Key
Attributes
4
AUTONOMY &
CONTROL
5
GRADUAL
INTRODUCTION
6
PRO-ACTIVE
COMMUNICATIONS
Key
Strategies

31. TAKE A HUMAN-CENTRED APPROACH
29
GOOGLE BRAIN
People + AI Research
institute
Prototype with Real People Instead of an Algorithm
Machine Learning Doesn’t Solve Everything
Design with the System’s Failure in Mind
Get Feedback, Forever

32. CHOOSE THE RIGHT PROBLEMS
30https://medium.com/intuitionmachine

33. BUSINESS VS GAMEPLAY
31
BUSINESS GAMEPLAY
Goal
Environment
Limited Resources
Moves
Taking Turns
Scoring
Results
More
freedom.
Act ethically!
Less
freedom.
Follow the
rules!
Necessarily
complex
Simplification
reveals
insight

34. DEVELOPING
TECHNOLOGY
WITH INTUITION

35. THE PROBLEM
33
XKCD (CC BY-NC 2.5)

36. THE NEW NEW PRODUCT DEVELOPMENT GAME
34
OBJECTIVES
PRODUCT
Validate
RULES +
SOFTWARE
ARCHITECTURE
Research &
Analyse
Develop
(PEOPLE)
CODE
Verify
Deploy

37. THE NEW NEW NEW PRODUCT DEVELOPMENT GAME
35
OBJECTIVES
PRODUCT
Validate
RULES +
SOFTWARE
ARCHITECTURE
Research &
Analyse
DATA SET +
NETWORK
ARCHITECTURE
Curate
Data
Deploy
Develop
(PEOPLE)
CODE
Verify
Deploy
Train
(MACHINES)
MODEL
Verify

38. DATA CURATION
36
DATA SET +
NETWORK
ARCHITECTURE
Curate
Data
OBJECTIVESYour existing data
• Needs 10,000-100,000 samples
• Normalisation
• Bias treatment
Generating new data
• Secondary app
• Simulation
• Generative networks

39. MACHINES TRAINING
37
DATA SET +
NETWORK
ARCHITECTURE
Train
(MACHINES)
MODEL
Verify
Concerns
• Number crunching
• Fine-tuning & debugging
• Evolving network architecture
Approaches
• Dedicated hardware
• Pre-trained models
• Adversarial networks
• High-level frameworks & automation

40. SKILLS SETS
38
Diverse teams
• More robust approaches
• Less risk of inadvertent bias
Data scientists
• High-level direction - hybrid approaches
• Deep technical expertise
• Risk and quality assurance
Architecture & Development
• AI API
• Integrate data pipelines & UI
• Develop automation tools
• Run network iterations and training cycles
TOOLS & FRAMEWORKS (EG)
Platforms
• TensorFlow - general purpose and
cross-architecture
• Caffe - specifically vision - includes
Model Zoo with pre-trained models
High-level frameworks
• KERAS - python based flexible
network definition
• DeepLearning.scala - differentiable
functional programming
• PyTorch - evolving and flexible

41. A GOOD PROBLEM
TO HAVE?
39
XKCD (CC BY-NC 2.5)

42. BUILDING
ORGANISATIONS
WITH INTUITION

43. KNOW WHAT
41
Explicit, rule-based

44. KNOW HOW
42
Tacit, feel-based
* Video of a FULLY SICK skid

45. KNOW WHY
43
Explicit, cause-based

46. THE
NONAKA
CYCLE
44
HUMAN
KNOW HOW
KNOW WHY
KNOW WHAT
Organisational
learning

47. THE
NONAKA
CYCLE
44
HUMAN
KNOW HOW
KNOW WHY
KNOW WHATMACHINE
KNOW HOW
Organisational
learning

48. THE
NONAKA
CYCLE
44
KNOW WHY
KNOW WHATMACHINE
KNOW HOW
Organisational
learning

49. THE
NONAKA
CYCLE
44
KNOW WHATMACHINE
KNOW HOW
Organisational
learning

50. THE
NONAKA
CYCLE
44
MACHINE
KNOW HOW

51. MACHINES BETTERING HUMANS, NOT JUST BESTING HUMANS
45
As Fan’s losses piled up
against AlphaGo, [he] came
to see Go in an entirely new
way. Against other humans,
he started winning more.
Cade Metz
Wired
Just as machines made human
muscles a thousand times
stronger, machines will make
the human brain a thousand
times more powerful.
Sebastian Thrun
Google X

52. BUILDING
THE NEW
NONAKA
CYCLE
46
HUMAN
KNOW HOW
KNOW WHY
KNOW WHATMACHINE
KNOW HOW
Enrich

53. Enhanced
Learning
BUILDING
THE NEW
NONAKA
CYCLE
46
HUMAN
KNOW HOW
KNOW WHY
KNOW WHATMACHINE
KNOW HOW
Enrich

54. CHANGING JOB DESIGN
47
Alexandra Heath
Head of Economic Analysis Department, RBA
Carlos Perez
Intuition Machine, University of Massachusetts Lowell
Jobs that use automation as a tool
Jobs that use humans as a safety valve against
automation failure
Jobs that interpret the decisions of machines
Jobs that design human-machine interfaces
Jobs that design automation to manipulate
human behaviour

55. MANAGING
RISK & ETHICS
WITH INTUITION

56. MACHINE FAILURE MODES
49
http://www.evolvingai.org/fooling
Training Set Bias Fooled by Hidden Heuristics

57. HUMAN FAILURE MODES
50
Training Set Bias
Google image search: “faces in things”@thisismoonlight
Fooled by Hidden Heuristics

58. HUMAN FAILURE MODES
50
Training Set Bias
Google image search: “faces in things”@thisismoonlight
Execution Variability
You are anywhere between two and six times as likely to
be released if you're one of the first three prisoners
considered versus the last three prisoners considered.
https://www.theguardian.com/law/2011/apr/11/judges-lenient-break
Fooled by Hidden Heuristics

59. NO EXPLANATION - THE “SEMANTIC GAP”
51
BLACK BOX
INTUITION
KNOW WHY
KNOW HOWSCENARIO

60. SOCIETAL IMPLICATIONS
52
According to recent reports, every
Chinese citizen will receive a so-
called ”Citizen Score” [based in
part on deep learning against
Baidu history], which will
determine under what conditions
they may get loans, jobs, or travel
visa to other countries.
https://www.scientificamerican.com/article/will-democracy-survive-big-data-and-artificial-intelligence/

61. SOME RESPONSES TO RISK & ETHICAL CHALLENGES
53
WEAPONS OF MATH
DESTRUCTION
1. Are your objectives
aligned with your
customer’s?
2. Is your model’s operation
opaque?
3. Is it “scaled” from a
similar application, or
likely to “scale” in turn to
related applications?
4. Does your model create
its own reality with
feedback loops?
EU RIGHT TO
EXPLANATION
General Data Protection
Regulation to take effect
2018. The law will effectively
create a “right to
explanation” for users
about whom algorithmic
decisions were made.
In its current form, the GDPR’s
requirements could require a
complete overhaul of
standard and widely used
algorithmic techniques.
SELF-DRIVING CARS
Mercedes will save
occupants as a priority -
they have taken a pre-
meditated position.
Volvo will accept liability for
any incident involving one
of their vehicles in
autonomous mode.

62. CONCLUSION

63. OUTPUT LAYER
55
Machines are outperforming humans in narrow intuition tasks.
This is due to a confluence of recent developments,
and innovation continues at incredible pace.
This delivers enormous improvement potential, and has significant implications
for how we design and develop products, and how we build and manage organisations.
There are potentially huge benefits for society,
but ethics and risk to be managed.
Understanding machine intuition better will help us better manage these developments,
and ultimately help us understand humans better.

64. END