Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning. This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.

1. Machine Learning
for Designers
Memi Beltrame - @bratwurstkomet
UX Schweiz Basel Meetup
Basel, May 8. 2019

2. Machine Learning
for people with a
fuzzy idea of what it is
Memi Beltrame - @bratwurstkomet
Or rather
UX Schweiz Basel Meetup
Basel, May 8. 2019

3. Design is becoming physical,
automated and connected

4. https://pxhere.com/en/photo/1006116
An example

5. https://pxhere.com/en/photo/1006116
Her pain
is your pain

6. otoscope
This is an
It can be used to look at the
eardrum to see if the ear is inflamed.
Because the otoscope is connected
to an iPhone, an image can be taken
of the eardrum.

7. The image is sent to a service that tells me if I should go to a doctor or not.

8. AI:
Image recognition,
Data analysis
Industrial Design
InteractionDesign
Service Design

9. Machine learning is
the main driver

10. What is Machine Learning?

12. Netflix uses
machine learning
to predict the
match probability

13. machine learning:
training machines
to receive input data and
predict an output value

14. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
✔ ❌

15. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
#1 method used in machine learning

16. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
Unsupervised learning The machine is given a lot of data and it
uses algorithms to find out interesting
patterns.

17. Let's get some pizza data
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of pizzas per week
Average # of toppings
per pizza

18. Find patterns
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week
You can run this
data through an
algorithm and it
would find groups of
items that are close
together,

19. Take Action
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week

20. Take Action
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week
With these groups you now can direct address the different groups
The group on the top right probably are big households you can target
specifically
The group on the left are those that order less frequently so you could address
this and offer a super tuesday for those that don't order on that day
The last one is for the people that love boring pizza: give them what they want,
but larger!
The applications of this clustering by unsupervised learning are market
segmentation or fraud detection in banking

21. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
Unsupervised learning The machine is given a lot of data and it
uses algorithms to find out interesting
patterns.
Reinforcement learning The machine continuously learns from the
environment in an iterative fashion.
It starts dumb and gets smarter.

22. Reinforcement Learning
The machine is given a
set of rules and a goal
• Physics: Gravity etc
• Wheels turn
• Goal get farther than
previous cars
It trains itself by keeping
the features that helped
it reach the goal.
BoxCar 2D: Computation Intelligence Car Evolution (Needs Flash) http://boxcar2d.com/

23. Reinforcement Learning
After a few dozen
generations the
machine has succeded
in creating a vehicle
that looks like a car and
can reliably drive

24. #1 method: supervised learning
Bedrooms m2 Neighbourhood Floors Sale Price
4 96 Hipsterton 2 1’500’000
2 89 Snoringham 3 750’000
3 75 Hipsterton 1 1’200’000
3 79 Snoringham 2 820’000
• Give the machine
a training set with
features
• Give it the target
values
• It figures out how
important each
feature is
• The machine can
make predictions
of target values
Features Target

25. #1 method: supervised learning
Bedrooms m2 Neighbourhood Floors
4 96 Hipsterton 2
2 89 Snoringham 3
3 75 Hipsterton 1
3 79 Snoringham 2
Predictions improve with
• more features
• larger learning sample
Features

26. #1 method: supervised learning
Features Target
8

27. #1 method: supervised learning
Features Target
owl

28. #1 method: supervised learning
• Train the machine
to learn what
matches and what
does not
• Train with edge
cases
Owl or Apple?

29. machines use algorithms
to make predictions

30. how machines use algorithms
1. Take a lot of training data
2. Pass it through a generic algorithm
(some mathematical formula)
3. Let the machine figure out its own
logic based on the data.
Emails
Generic Machine
Learning Algorithm
Spam Not Spam

31. how machines use algorithms
500g white flour,
2 tsp salt
7g fast-action yeast
3 tbsp olive oil
300ml water
475g plain flour,
1 tsp salt
10g dried yeast
1 tbsp olive oil
400ml water
The algorithm finds the valid weights of the individual
features of a data-set to make the right prediction
2 cups flour,
1 cup salt
1 tsp olive oil
1 cup water
Bread Bread Salty play dough

32. generic algorithms
There are many generic
algorithms that already exist.
The same generic algorithm
can be used to solve
problems in completely
different areas.
Emails Algorithm
Spam
Not Spam
Articles Algorithm Finance
Politics
Sports

33. 2 types of algorithms
Classification algorithms
Emails Algorithm
Spam
Not Spam
The goal is to predict discrete
values, e.g. {1,0}, {True, False},
{spam, not spam}.
Regression algorithms
House-
Details
Algorithm
Price of
House
The goal is to predict continuous
values, e.g. home prices, weather
temperatures
A big part of ML
is about classification

34. image recognition
Chihuahua or Muffin?
Most image recognition is
about classification

35. image recognition
Real time
At multiple scales
For a varying number of
recognizable elements
Realtime Multi-Person 2D Human Pose Estimation

36. What about language?

37. is language like images?
Images can be
recognized
because their data
can be encoded
Can we do the same with language?

38. translation versus conversation
Do you have the time?
Translation goal:
Produce an equivalent
Conversation goal:
Understand the meaning
Avez-vous l’heure? It’s 7pm.Yes

39. statistical translation

40. statistical translation
Each word of the sentence can have several meanings.

41. statistical translation
I try | to run | at | the prettiest | open space.
I want | to run | per | the more tidy | open space.
I mean | to forget | at | the tidiest | beach.
I try | to go | per | the more tidy | seaside.
I want | to go | to | the prettiest | beach.
The algorithm compares the possible translations against existing ones.
The algorithm picks the translation with the highest probability.

42. statistical translation
Input
Measurements
of input
sentence
Output I want to go to the prettiest beach.

43. statistical translation

44. Audio Text
conversational interfaces
Machine learning is a crucial
part of these interfaces

45. new challenges and disciplines
• recognizing intent
• understanding context
• voice and tone
• shaping conversations in a
humane and ethical way
}Linguistics
Ethics

46. intent - what does it all mean?
types of meaning
understand the wordsliteral:
understand the actual meaningimplied:
Do you have the time?
metaphors & metonymiesreferenced:
Wall Street is in crisis

48. Elements that make
this artificial:
• Not picking up intent
„give me a spot on saturday“
• Literal repetition

49. context
context is even harder than intent
• the sequence in time
• understanding the surroundings
• semantic context
homonymy: 🦇 is not a 🏏

50. voice and tone: change registers
we adapt the way we speak to the
situation we’re in
Depending on:
• how serious the situation is
• how formal it is
• how we are connected to the person
Conversational interfaces need to take
this into account.
This is a design task
Yes
Sporty
Neutral
Date Night
Ready for your style?
How would you describe your style?
I'd totally raid your closet...
Sporty is my style!
Do you wear colors or nah?
Fab, I bet you look great in everything!
Where are you going in your hot new
outfit?

51. The designers’ role:
Assisted Intelligence

52. Designers are content experts
Icons by Sarah Rudkin
Developers
Build the machine
Domain experts
Have the domain
specific knowledge
Designers
• Content oversight for training:
What makes good training data?
• Mediator between engineering and domain
experts
• Ethical considerations

53. ethics matter
Machines learn from us
We choose what to teach
We need to
• challenge and stress test from a diverse
point of view
• put humans before technology
(once again)
• bring our principles of what good
design is to the AI world
This is a design task

54. Machine Learning is
everywhere
Learn to see its opportunities
Get a seat at the table now
Understand the implications
of using machine learning
Bring Design principles into the
mix to make empowering and
ethical products

55. Thanks!
I’m @bratwurstkomet
I like kitten and ice cream

56. Resources
A visual introduction to machine learning
http://www.r2d3.us
Machine Learning is Fun!
(the perfect series of articles to get you started)
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
30 Free Courses: Neural Networks, Machine Learning, AI
https://www.datasciencecentral.com/profiles/blogs/neural-networks-for-machine-learning
Watson Knowledge Studio
https://www.ibm.com/watson/developercloud/doc/wks/wks_overview_full.shtml
2 Minutes Papers: a youtube channel dedicated to condensing the results of scientific papers on artificial intelligence.
https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg
Realtime Multi-Person 2D Human Pose Estimation
https://www.youtube.com/watch?v=pW6nZXeWlGM
BoxCar 2D: Computation Intelligence Car Evolution (Needs Flash)
http://boxcar2d.com/
Google AI Experiments
https://experiments.withgoogle.com/collection/ai