The document discusses machine intuition and its development. It notes that machines are outperforming humans at narrow intuitive tasks due to recent advances in areas like data volumes, hardware, and network designs. This allows incredible innovation and improvement, with significant implications for product design, organizational structure, and more. However, it also notes ethics and risks must be managed to realize potential benefits, such as a lack of explainability, bias, and other failure modes. Understanding machine intuition and the responsible development of technologies can help address challenges while maximizing positive outcomes.

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/

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
© Fjodor van Veen 2016
BEACH
SEA
SKY
…
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

13. NEURAL NETWORKS HAVE BEEN AROUND FOR A WHILE…
13
Dave’s 1994 university internship

14. …BUT THEN THIS HAPPENED
14
WEB-SCALE
DATA
1
Data volumes double
every year

15. Massive adoption of GPU
(and TPU, HPU, FPGA)
2
DEDICATED
PARALLEL
HARDWARE
…BUT THEN THIS HAPPENED
15

16. 3
ADVANCED
NETWORK
DESIGNS
…BUT THEN THIS HAPPENED
16
© Fjodor van Veen 2016

17. NETWORK PARAMETERS - STATE OF THE ART
17
2013 201720152014 20162012
160 B
11.2 B
1.7 B
1 Billion
10 Billion
100 Billion
1,000 Billion

18. SO, WHAT’S
NEXT?

19. EXPLOSION OF MICRO-INTUITORS
19
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

20. HUMANS AND MACHINES COMPLEMENTING EACH OTHER
20
MACHINES
Wider learners
Scalable thinkers
HUMANS
Faster learners
Flexible thinkers

21. DISTRIBUTED MACHINE INTUITION
21
Apple iOS Core ML
Google Federated Learning
CLOUD TRAINING DEVICE INTUITION
AGGREGATED DATA PRIVATE DATA
FEDERATING LEARNING

22. PRO-ACTIVE DISCUSSION AND MANAGEMENT OF RISKS
22

23. 23
com·put·er
/kəmˈpyo͞ odər/
noun
(17th century)
“one who computes”

24. de·ci·sion mak·er
/dəˈsiZHən/ ˈ/mākər/
noun
(20th century)
“one who decides”
24
Source: https://www.pokernews.com/strategy/how-to-bet-in-poker-tournaments-22371.htm

25. DESIGNING
PRODUCTS
WITH INTUITION

26. UNDERSTAND CUSTOMER PERCEPTION
26
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

27. BUILD CUSTOMER TRUST
27
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
Attribute
s
4
AUTONOMY &
CONTROL
5
GRADUAL
INTRODUCTION
6
PRO-ACTIVE
COMMUNICATIONS
Key
Strategie
s

28. TAKE A HUMAN-CENTRED APPROACH
28
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

29. CHOOSE THE RIGHT PROBLEMS
29https://medium.com/intuitionmachine

30. BUSINESS VS GAMEPLAY
30
BUSINESS GAMEPLAY
Goal
Environment
Limited Resources
Moves
Taking Turns
Scoring
More
freedom.
Act ethically!
Less
freedom.
Follow the
rules!
Necessarily
complex
Simplification
reveals
insight

31. DEVELOPING
TECHNOLOGY
WITH INTUITION

32. THE PROBLEM
32
XKCD (CC BY-NC 2.5)

33. THE NEW NEW PRODUCT DEVELOPMENT GAME
33
OBJECTIVES
PRODUCT
Validate
RULES + SOFTWARE
ARCHITECTURE
Research &
Analyse
Develop
(PEOPLE)
CODE
Verify
Deploy

34. THE NEW NEW NEW PRODUCT DEVELOPMENT GAME
34
OBJECTIVES
PRODUCT
Validate
RULES + SOFTWARE
ARCHITECTURE
Research &
Analyse
DATA SET +
NETWORK
ARCHITECTURE
Curate
Data
Deploy
Develop
(PEOPLE)
CODE
Verify
Deploy
Train
(MACHINES)
MODEL
Verify

35. DATA CURATION
35
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

36. MACHINES TRAINING
36
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

37. SKILLS SETS
37
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
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
Edge deployment
• iOS Core ML

38. BUILDING
ORGANISATIONS
WITH INTUITION

39. KNOW WHAT
39
Explicit, rule-based

40. KNOW HOW
40
Tacit, feel-based

41. KNOW WHY
41
Explicit, cause-based

42. THE
NONAKA
CYCLE
42
HUMAN
KNOW HOW
KNOW WHY
KNOW WHATMACHINE
KNOW HOW
Organisational
learning

43. MACHINES BETTERING HUMANS, NOT JUST BESTING HUMANS
43
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

44. Enhanced
Learning
BUILDING
THE NEW
NONAKA
CYCLE
44
HUMAN
KNOW HOW
KNOW WHY
KNOW WHATMACHINE
KNOW HOW
Enrich

45. CHANGING JOB DESIGN
45
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

46. MANAGING
RISK & ETHICS
WITH INTUITION

47. MACHINE FAILURE MODES
47
http://www.evolvingai.org/fooling
Recognising the Wrong ThingsTraining Set Bias

48. HUMAN FAILURE MODES
48
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
Recognising the Wrong Things

49. NO EXPLANATION - THE “SEMANTIC GAP”
49
BLACK BOX
INTUITION
KNOW WHY
KNOW HOWSCENARIO

50. SOCIETAL IMPLICATIONS
50
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/

51. SOME RESPONSES TO RISK & ETHICAL CHALLENGES
51
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
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.

52. CONCLUSION

53. OUTPUT LAYER
53
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.

54. END