Is AI Really Intelligence? - Deep Learning – Why Deep? Why Learning? - AI on the Marketplace - Do it Yourself - When are Robot Taxis Coming?

1. YOU DON’T WANNA BE THE CHEF
IN A FAST FOOD RESTAURANT
The Age of Artificial / Beneficial Intelligence

2. Make?
OFFER OR SUFFER? – THE SPECTRUM OF ATTITUDES
2

3. Use?
OFFER OR SUFFER? – THE SPECTRUM OF ATTITUDES
3

4. Offer?
OFFER OR SUFFER? – THE SPECTRUM OF ATTITUDES
4

5. Suffer?
OFFER OR SUFFER? – THE SPECTRUM OF ATTITUDES
5

6. ITINERARY
➤ Is AI Really Intelligence?
➤ Deep Learning – Why Deep?
Why Learning?
➤ AI on the Marketplace
➤ Do it Yourself
➤ When are Robot Taxis
Coming?
➤ Takeaway
6

7. IS AI REALLY
INTELLIGENCE?
7

8. HOW OUR KNOWLEDGE EVOLVES
1. Mystery = Hunches =
prelinguistic intuitions.
2. Heuristics = open-ended
prompts to think or act in a
particular way - no guarantee
for a certain result.
3. Algorithms = certified
production processes, with
guarantee for a particular result.
4. {Software}

9. M-H-A EXAMPLE #1: PERSPECTIVE
➤ 5th century BC –
Skenographia in Greek
theatres
➤ 15th century Italy –
Brunelleschi
➤ 3D modelling software

10. "We are not technically in the food business. We are in the real estate business." - former CFO
5 Chefs vs 35K+ Restaurants
M-H-A EXAMPLE #2: MCDONALD'S
10

11. THE MYSTERY - HEURISTIC - ALGORITHM PROCESS
11
Drivers Drags Pitfall
➤ Demand side: cost,
unreliability
➤ Supply side: intellectual
challenge
➤ Expensive labor
➤ (Quantity >> Quality)
>> new use cases
➤ Narrow scope >>
disruption
➤ Commoditization
➤ Conflicting interests
➤ Skill monopoly
➤ Cheap labor
➤ „Stockholm syndrome

12. THE KNOWLEDGE FUNNEL VS THE „PINK LADY”
12

13. AI = ALGORITHMIC VERSION OF COGNITIVE WORK
Physical Work Cognitive work Emotional labor
Example
Heavy lifting, manual
precision work,
repetitive work etc
Computing, pattern
recognition,
comparison, etc
„Making art,
producing generosity,
and exposing
creativity” – Seth
Godin
Algorithmic version Robots Artificial intelligence NA

14. IS AI REALLY INTELLIGENCE?
14
In some way
YES: IQ-tests
measure pattern
recognition
Seen from another angle
NOT: behind AI are also
"brains" - supported by a
university supercomputer
Libratus algorithm beats poker stars

15. IS AI REALLY INTELLIGENCE? - CRITICS SAY: NO
15
Dreyfus: "Heideggerian AI
is not Heideggerian enough"

16. IS AI ELIMINATING JOBS? - YES, LIKE ALL KINDS OF TECHNOLOGY
16
Mental calculators
are not needed any
more - still are high
prestige intellectuals
Low skill jobs are
replaced by other low
skill jobs

17. DEEP LEARNING
17

18. WHY DEEP? - THE NEURAL NETWORK ZOO
18

19. WHY LEARNING? - THE GRADIENT DESCENT ALGORITHM
19
Similar to linear regression
Challenge: find local minimum
of "cost function"
Go like a blindfolded tourist
Variants:
➤ Supervised learning
➤ Unsupervised learning
➤ Reinforcement learning

20. EVOLUTION OR REVOLUTION? - GOOGLE TRANSLATE CHANGING
20
➤ Phrase-Based Machine
Translation (PBMT) breaks
an input sentence into words
and phrases to be translated
largely independently
➤ Neural Machine Translation
(NMT) considers the entire
input sentence as a unit for
translation.

21. AI ON THE
MARKETPLACE
21

22. STREAMING VIDEO SUBTITLES – WHICH WAY WILL WIN? - HUMAN TRANSLATION
22

23. STREAMING VIDEO SUBTITLES – WHICH WAY WILL WIN? - AUTOMATIC CAPTIONING
23
Challenge: context
in the video

24. CONSUMER TRENDS BY TRENDWATCHING
24
A CONSUMER TREND IS A NEW
MANIFESTATION AMONG PEOPLE —
IN BEHAVIOR, ATTITUDE, OR
EXPECTATION — OF A
FUNDAMENTAL HUMAN NEED,
WANT, OR DESIRE.

25. THE SWEET SPOT OF CONSUMER TRENDS, EXAMPLE: INTERNET OF CARING THINGS
25
Safety
New
connected
objects

26. BENEFICIAL INTELLIGENCE – A TRENDWATCHNG TREND
26
WHY NOW?
➤ RACE OF GIANTS: Google, IBM,
Facebook, Baidu, Microsoft,
Amazon, Apple, etc
➤ HEAD IN THE CLOUDS: The IBM
Watson Developer Cloud, etc
➤ BEYOND OUR MEANS: human
minds alone will struggle to satisfy
their expectations

27. TYPICAL CO-TRENDS WITH BENEFICIAL INTELLIGENCE
27
UBITECH: The ever-greater
pervasiveness of technology
YOUNIVERSE: The desire to be seen
and served as unique
STATUS SEEKERS: The never-ending
pursuit of status
HELPFULL: The demand for convenient
and superior service
INFOLUST: The need for relevant and
actionable information
True Self: Personalization that goes
deeper
Brand Me: Consumers' personal online
profiles matter more than ever
Safety Net: Consumers are embracing
digital technologies that keep them safe

28. TYPICAL CO-TRENDS WITH BENEFICIAL INTELLIGENCE
28
Intuitive Interfaces: Consumers are
adopting technologies that let them
interact naturally with their
environment
Cash-less: technological advances
and consumers’ desire for
convenience + poor banking
infrastructure in developing countries
Off = On: Why the offline world is
adjusting to, mirroring and
incorporating the online world
Time Saviors: Consumers will
always have time for products,
services and experiences that
simplify their lives

29. DETOUR: SAME TECH, DIFFERENT NEEDS
29
Amazon Go powered by
self-driving car tech:
➤ computer vision
➤ sensor fusion
➤ deep learning

30. TYPICAL CO-TRENDS WITH BENEFICIAL INTELLIGENCE
30

31. TYPICAL CO-TRENDS WITH BENEFICIAL INTELLIGENCE
31
Mychiatry: Consumers
increasingly pursue mental
wellbeing with DIY
solutions

32. TYPICAL CO-TRENDS WITH BENEFICIAL INTELLIGENCE
32
Mapmania: Why map-
based tools will always be
popular
Crowd-Express: How
brands can harness the
wisdom of the connected
crowd

33. WHO PAINTED THIS PICTURE?
33

34. „REMBRANDT”
34
Faketastic: Many
consumers enjoy
shamelessly unnatural
goods, products and
experiences

35. „FAKETASTIC”: SIMILAR NEED, DIFFERENT TECH
35
Status Stories: Why status
lies in the story for rising
numbers of consumers
Faketastic: Many
consumers enjoy
shamelessly unnatural
goods, products and
experiences

36. DO IT YOURSELF
36

37. AMAZON WEB SERVICES
37

38. (A)LEX(A)
38

39. IBM BLUEMIX (INCL. WATSON)
39

40. WHEN ARE ROBOT
TAXIS COMING?
40

41. PACE LAYERS
41
Stuart Brand
„Old climber futurist, good climber futurist”
(See Whole Earth Catalog, NASA images of
Earth, Global Business Network, The Long
Now Foundation, How Buildings Learn, etc)

42. PACE LAYERS OF BUILDINGS
42
Site - This is the geographical setting, the urban location, and the legally defined
lot, whose boundaries and context outlast generations of ephemeral buildings. "Site
is eternal." Duffy agrees.
Structure - The foundation and load-bearing elements are perilous and
expensive to change, so people don't. These are the building. Structural life ranges
from thirty to three hundred years (but few buildings make it past sixty for other
reasons).
Skin - Exterior surfaces now change every twenty years or so, to keep up with
fashion or technology, or for wholesale repair. Recent focus on energy costs has led to
re-engineered skins that are air-tight and better-insulated.
Services - These are the working guts of a building: communications wiring,
electrical wiring, plumbing, fire sprinkler systems, HVAC (heating, ventilating, and
air conditioning), and moving parts like elevators and escalators. They wear out or
obsolesce every seven to fifteen years. Many buildings are demolished early if their
outdated systems are too deeply embedded to replace easily.
Space Plan - The Interior layout—where walls, ceilings, floors, and doors go.
Turbulent commercial space can change every three years or so; exceptionally quiet
homes might wait thirty years.

43. „CONSTRUCTIVE TURBULENCE” AT THE BOUNDARIES
43
Slow Change:
➤ More robust
➤ Few players
➤ Concession
➤ Regulation
➤ Economies of
scale
Fast Change
➤ More agile
➤ Many players
➤ Free market
➤ Economies of
learning
Military
technology
(internet)
Maps
for
autonomous
cars
AirBnB,
Uber
Responsibility for
autonomous car
accidents

44. TAKEAWAY: YOU DON’T WANNA BE THE CHEF IN A FAST FOOD RESTAURANT
44
➤ Anything that can be done by
machines, will be done by
machines
➤ Make, offer, or use AI, do not
suffer
➤ Do emotional labor instead