/ My talk from the UXcamp Europe in Berlin. Please enjoy and feel free and don't hesitate to contact me if you have questions or want to talk about UX and AI What is artificial intelligence, how do we create collaboration and what’s gonna happen in the future?

1. UX FOR
ARTIFICIAL INTELLIGENCE
UXcamp Europe, Berlin, 03.June, 2017
Image: Retronaut

2. Image: Retronaut

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Image: DMITRI KESSEL/GETTY IMAGES

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Image:Wikipedia

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Image: Orion Pictures

6. Image: 20th Century Fox

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Image:AP

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Image: Photo: Ben Hider/Getty Images

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Image:AP Photo/Lee Jin-ma

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Image:Tim Kaulen/Carnegie Mellon University

11. Worries about AI are
understandable.

12. But it’s not humans
vs. the machines
VS

13. It’s about creating an
awesome together.

14. THE NEXT 45 MINUTES…
WHAT IS ARTIFICIAL INTELLIGENCE,
HOW DO WE CREATE COLLABORATION
AND WHAT’S GONNA HAPPEN IN
THE FUTURE?

15. HI! I’M JAN!
SENIOR UX DUDE
EXB RESEARCH & DEVELOPMENT
@JANKORSANKE

16. INTRODUCTION TO AI
Image: LONDON EXPRESS/GETTY IMAGES

17. ARTIFICIAL INTELLIGENCE?

18. Intelligent behaviour in an
autonomous agent —
THIS is AI.
BEEP
BEEP

19. MACHINE LEARNING
DEEP LEARNING
NEURAL NETWORKS
NATURAL LANGUAGE PROCESSING

20. Machine Learning is a part of AI.
It’s the ability for an algorithm to
learn from prior data in order to
produce a behaviour.
ML is teaching machines to make
decisions in situations they have
never seen.

21. Deep Learning is a branch of ML.
Stands for a class of optimisation
methods of artificial neural networks.
AI
Machine
Learning
Deep
Learning
The goal is, to enrich and improve the
many layers of those networks.

22. A neural network is a collection of
connected simple units called
artificial neurons.
Artificial neural networks find
patterns in raw data by combining
multiple layers of artificial neurons.

23. Natural Language Processing is the
understanding, processing and
reproduction of natural language.
NLU is a huge priority and challenge
in AI research.
Because human communication is not
straightforward, but the key to
radical progress.

24. CATEGORIES OF AI

25. Artificial Narrow Intelligence
Artificial General Intelligence
Artificial Superintelligence

26. Narrow
Intelligence
AI of today can do specific tasks:
(driving a car, translation or Netflix binge).

27. Narrow
Intelligence
General
Intelligence
When a machine can do things in a way that
is indistinguishable from human behaviour.
AI of today can do specific tasks:
(driving a car, translation or Netflix binge).

28. Narrow
Intelligence
General
Intelligence
Superartificial
Intelligence
“An intellect that is much smarter than the
best human brains in practically every field:
creativity, general wisdom and social skills.”
Nick Bostrom
When a machine can do things in a way that
is indistinguishable from human behaviour.
AI of today can do specific tasks:
(driving a car, translation or Netflix binge).

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Machine
intelligence is the
last invention
that humanity
will ever need
to make.
Nick Bostrom
“
Image:TED

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Image: Amazon Image:TED

31. WHY NOW?

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AI WINTER
Image: WALLACE G. LEVISON/THE LIFE PICTURE COLLECTION/GETTY IMAGES

33. Smarter and
better
algorithms
Much more
data
More
computing
power

34. WHAT AI CAN AND CAN’T
DO RIGHT NOW

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Image: oneyard.com/

36. Image: Science Picture Co Collection Mix: Subjects Getty Images/

37. Image: wikipedia.org

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Image:AP Photo/Lee Jin-ma

39. Image: Beltz & Gelberg

40. !AI has by now succeeded in doing essentially
everything that requires ‘thinking’, but has
failed to do most of what people and animals
do ‘without thinking.’ Donald Knuth

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Image: Giphy

42. Surprisingly, despite AI’s breadth of impact, the types of
it being deployed are still extremely limited.

43. AI work requires carefully choosing an input and a response
and providing the necessary data to help the AI understand
the input to response relationship.
Choosing those things creatively has already
revolutionized many industries. It is poised
to revolutionize many more.

44. UX FOR AI
Image: BETTMANN/CORBIS

45. It’s about creating an
awesome together.

46. WHAT DOES IT MEAN?

47. AI algorithms should make people’s jobs simpler,
easier, and more productive.
Status quo: most AI software tools are developed
by software engineers.

48. Image: http://imgur.com/ // mistaspeedy

49. The large and at the same time easy UX challenge:
Create tools, which can be populated with
knowledge without someone with a PhD in
Machine Learning having to be in the room.

50. Look back: First commercial software
Image: Rexhep-bunjaku/Wikimedia Commons

51. User Experience will probably mark out the winners from the
losers in the race to commercial success for AI software firms.
Image: Rexhep-bunjaku/Wikimedia Commons

52. DISCLAIMER:
Image: LESLIE JONES COLLECTION/BOSTON PUBLIC LIBRARY

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DISCLAIMER:
All design- and usability-paradigms are of course relevant
for AI tools too.
Image: LESLIE JONES COLLECTION/BOSTON PUBLIC LIBRARY

54. USE CASES

55. As artificial intelligence algorithms infiltrate the enterprise,
organizational learning matters as much as machine learning.
- AI -
- FRAGILE -
Image: focusmovers.com/

56. Prefer smart algorithms over well thought use cases is the
biggest mistake in many recent company AI initiatives.
Image: wikipedia.org

57. The hunt for better results is gonna shift from training the
algorithms to improve the use cases.
Image: No source

58. Aipoly Vision
Image:AipolyVision

59. Image:AipolyVision

60. ?Or your only doing it, because
everybody does it?
Please ask yourself the question,
if AI is beneficial for your case.

61. QUESTION AND ANSWER

62. Our history with computers
Our expectations of interaction with
computers has developed in the age of search.

63. Image: Google

64. Question Answer

65. Question Answer

66. tion An
We’ve learned that search is good
for some types of query but bad
for others.

67. tion An
But, what if the system asks you a
question in return?

68. Image: Giphy

69. Question Answer
In the future, questions will be solved by returning questions…
…and the path to the correct answers includes a series of
answers I have to give first.
Question
Answer
Question
Answer
Question
Answer
Question

70. There will be a shift in expectation: some computers don’t
serve ‘dumb answers’ but ‘smart questions’.
So what are the rules and etiquette of computers
asking us questions and conversing with us?

71. In particular how users form a view about
whether the time investment in this kind of
interaction is likely to yield a positive reward.
What has learned about the usage and
acceptance of Wizards or decision trees
will help here.

72. Usern den erfolgreichen und einfachen Umgang
mit Algorithmen,Trainingsmodellen, Dateneingabe,
Darstellung, etc. ermöglichen.
If customers can sense or ‘smell’ a waste of time they will bail
out.The earliest moments of interaction with AI are likely to be
critical, and they probably need deliberate design.
Image: http://www.roadpickle.com/spam-museum-of-austin-mn/ - Steve Johnson

73. COMPLEXITY

74. !The large amount of data and the complexity
in visualisation is way to big to pass it unfiltered
to the user.

75. Word2Vec
word
Quelle:
Label by:
Image:Tensorflow

76. Image:Tensorflow

77. Image:Tensorflow

78. Image: www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html

79. WORLD
KI
Image: Google

80. AI is already part of so many
applications,
we just don’t know it.
Image: Google

81. Similar to good design,
AI should be decent, but still
support the user in a best
possible way.
Image: Google

82. The large amount of data and the complexity in visualisation
is way to big to pass it unfiltered to the user.
Our job is, to enable the user to browse and
analyse large amounts of data.
Easy isn’t it?

83. STATUS, NOTIFICATIONS
AND ONBOARDING

84. When is it
done?
What is
happening?
How do I
start?

85. Image:Tensorflow

86. Image: Giphy

87. Challenge: Some calculations take forever
Image:A. R. MOORE/NATIONAL GEOGRAPHIC CREATIVE/CORBIS

88. Images: Slack;WhatsApp; Facebook

89. Image: Google

90. Image: Jeff Patton - User story mapping

91. P.S.: I miss you!
Image: Jeff Patton - User story mapping

92. Estimation of
processing time
Status indicator,
process steps
Good onboarding,
no empty state, a
simple case

93. BIAS

94. This sounds very inappropriate.
man : computer programmer woman : homemaker
Not for an AI.

95. Image: MITTech Review

96. ?
In 2013, researchers at Google set loose a
neural network on a corpus of 3 million
words taken from Google News texts.
The neural net’s goal was to look for
patterns in the way words appear next
to each other.

97. Words with similar meanings occupied similar
parts of this vector space.
They could represent these patterns using vectors in a
vector space with some 300 dimensions.
The relationships between words could be
captured by simple vector algebra:
“man : king :: woman : queen.”
“sister : woman :: brother : man,”

98. This data set is called Word2vec
and is hugely powerful.
Researchers use it to better
understand everything
from machine translation to
intelligent Web searching.
Image: http://mylearning9.com/?p=4

99. Image: http://flatironschurch.com/fi-messages/so-far-so-good-far-good-hope/

100. There is a problem with this database:
It is obviously sexist.
Image: http://flatironschurch.com/fi-messages/so-far-so-good-far-good-hope/

101. Examples:
Paris : France ::Tokyo : x x = Japan
father : doctor :: mother : x x = nurse
man : computer programmer :: woman : x x = homemaker
SAY WHAAAAAAT

102. Survey, whether these analogies
are appropriate or inappropriate.
Removed the warp with “hard
de-biasing”-process.
Searching the vector space for
word pairs that produce a similar
vector to “she: he”.
Correct the warp, but preserve
the overall structure of the space.
Image: Retronaut / Mashable

103. Computer programmer CV
That has important applications - One example: web search
Computer
programmer
#3
#1
#2
#4

104. The AI community itself has a homemade problem with this kind
of challenge, because most of them are still white, young and male.
Image: R.H. Fowler

105. TRUST

106. Image: Flickr/Pilots Of Swiss

107. Image: http://www.wikiwand.com/it/Otto_Disc

108. Image: Google

109. We generally perceive other people to be
reasonably competent drivers. Mostly.
We understand why people behave the way they
do on an intuitive level, and feel like we can
predict how they will behave.
We don’t have this empathy for current AI.

110. Siri doesn’t make life-
changing decisions for you.
It’s okay if it isn’t really
clear how it comes to its
conclusions.
Image: Jim Merithew/Cult of Mac

111. BEEPBEEP
But interacting with a system
that makes an important
decision for you requires
much more than a few
buttons and a status indicator.
Where the magic
happens
Damn complex
input
Damn complex
output

112. Trust Empathy HumanTechnology
If the purpose of smart systems is to make sophisticated subtle
decisions, it is pointless if people can’t trust them to do so.

113. Image: Google

114. Image: Google

115. Image: UBER

116. Image: UBER

117. But what if AI seriously can
make better decisions?
Street signs
X-Rays
Animals
?Cytology
Characters

118. Image:Youtube: Markus Mathias

120. Amazon Rekognition

121. REASONING

122. Image: IBM Watson

123. Image: IBM Watson

124. Image: wikipedia.org

125. Image: wikipedia.org

126. Independent which form of
reasoning you use, make
sure the user is able to
understand it.
Image: wikipedia.org

127. Level of detail for the reasoning
HighLow
Jan
(Village Idiot)

128. SUMMARY AND OUTLOOK
Image: IMAGE: STANLEY LEWIS/BIPS/GETTY IMAGES

129. Think about use cases for the AI
Questions will be the new answers
Trust in machines is hard work
Fit your reasoning on your user
Don’t pass the complexity to the user
Usable without a PhD in Machine Learning
No bias. Seriously.This sucks

130. WHERE AI CAN HARM

131. AI has the potential to reflect both the best
and the worst of humanity.
AI providing conversation
and comfort to the lonely
AI engaging in racial
discrimination.

132. The biggest harm that AI is likely to do to individuals in the
short term is job displacement, as the amount of work we can
automate with AI is vastly bigger than before.
It’s our turn to make sure we are building a
world in which every individual has an
opportunity to thrive.

133. ?
What about us?
Those that want to stay relevant in
their professions will need to focus on
skills and capabilities that artificial
intelligence has trouble replicating.
Understanding, motivating, and
interacting with human beings. Megan Beck &
Barry Libert

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Image: Guinness book of records

135. WHERE AI CAN HARM
(Part2)

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Image: wikipedia.org

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WHERE AI CAN HARM
Image: OpenAI

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WHERE AI CAN HARM
“No set of individuals has control over advanced set of AI”
“AI on its own will not develop something bad, it’s just
the people using it in a way thats bad”
Elon Musk
Image: OpenAI

139. WHEN IS IT GONNA HAPPEN

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145. When will we reach HLMI?
10 % 50 % 90 %
TOP 100 2024 2050 2070
COMBINED 2022 2040 2075
Optimistic guess
Realistic guess
Safe guess

146. 10 % 50 % 90 %
TOP 100 2024 2050 2070
COMBINED 2022 2040 2075
Optimistic guess
Realistic guess
Safe guess
Artificial Superintelligence: approx. 20-30 years later
When will we reach HLMI?

147. BETTER BE PREPARED :)

148. RETURN TO START

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Image: Giphy

150. It’s about creating an
awesome together.

151. THANKYOU!!
Image: Retronaut

152. ANY QUESTIONS?
@JANKORSANKE

153. ICONS:
Adrien Coquet
Anbileru Adaleru
Aneeque Ahmed
Arjuazka
Artem Kovyazin
Baboon designs
Creative Stall
Eucalyp
Fatahillah
Hea Poh Lin
Hopkins
Jake Dunham
Kirill Kolchenko
Knut M. Synstad
Lorie Shaull
Oksana Latysheva
Pictohaven
ProSymbols
Ralf Schmitzer
Shmidt Sergey
Souvik Maity
Vinaya Kumar P.V
Zidney