AWS Community Day CPH - Three problems of Terraform
Model-based Research in Human-Computer Interaction (HCI): Keynote at Mensch und Computer 2010
1. Ed
H.
Chi
Principal
Scientist
and
Area
Manager
Augmented
Social
Cognition
Area
Palo
Alto
Research
Center
@edchi
echi@parc.com
2010-09-13 Mensch und Computer 2010 Keynote
1
Image from: http://www.flickr.com/photos/ourcommon/480538715/
2. Early
fundamental
contributions
from:
– Computer
scientists
interested
in
changing
how
we
interact
with
information
– Psychologists
interested
in
the
implications
of
these
changes
The
need
to
establish
HCI
as
a
science
– Adopt
methods
from
psychology
– Dual
purpose:
understand
nature
of
human
behavior
and
build
up
a
science
of
HCI
techniques.
9/13/10 HCIC "Living Lab" 2
4. Problem:
– Intellectual
over-‐specialization
The
Memex
Extend
the
powers
of
the
human
mind
with
technology
– Individuals
could
attend
to
greater
spans
– Facile
command
of
all
recorded
knowledge
– Sharing
of
knowledge
gained
2010-09-13 Mensch und Computer 2010 Keynote 4
5. Graphical User Interface
chartered
to
create
the
architecture
of
Laser Printing
information
&
the
office
of
the
future
Ethernet
invented
distributed
personal
computing
-‐ Bit-mapped Displays
established
Xerox’s
laser
printing
business
-‐ Distributed File Systems
Page Description Languages
created
the
foundation
for
the
digital
revolution
-‐
First Commercial Mouse
Object-oriented Programming
WYSIWYG Editing
Distributed Computing
VLSI Design Methodologies
Optical Storage
Client/Server Architecture
Device Independent Imaging
Cedar Programming Language
2010-09-13 Mensch und Computer 2010 Keynote 5
6. Fitts’
Law
Models
of
Human
Memory
Models
of
Human
Attention
Interruptability
Cognitive
and
Behavorial
Modeling
Perception
and
Navigation
…
2010-09-13 Mensch und Computer 2010 Keynote 6
7. We
know
motion
in
the
periphery
is
more
noticeable
than
in
the
foveal
region
[DaVinci].
Now
think
about
research
and
products
that
involve
animations
or
flashing
icons.
2010-09-13 Mensch und Computer 2010 Keynote 7
8. We
know
that
people
can
Block
out
the
irrelevant
content
quite
easily
Until
it’s
semantically
meaningful
or
important
to
you
Hey,
Jurgen!
UIST 2004 8
9. Characteriza*on
Models
Evalua*ons
Prototypes
Characterize
activity
with
experiments,
ethnography,
log
analysis
Model
interaction
dynamics
and
interface
variations
Prototype
tools
to
increase
benefits
or
reduce
cost
Evaluate
prototypes
with
users
2010-09-13 Mensch und Computer 2010 Keynote
9
12. Characteriza*on
Models
Evalua*ons
Prototypes
Characterize
activity
with
experiments,
ethnography,
log
analysis
Model
interaction
dynamics
and
interface
variations
Prototype
tools
to
increase
benefits
or
reduce
cost
Evaluate
prototypes
with
users
2010-09-13 Mensch und Computer 2010 Keynote
12
13. human-‐information
interaction
is
adaptive
to
the
extent:
MAXIMIZE
[ Net Knowledge Gained
Costs of Interaction ]
2010-09-13 Mensch und Computer 2010 Keynote 13
14. Scent Values:
Start users at Probabilities of
page with Transition Examine user patterns
some goal
Flow users
through the
network
2010-09-13 Mensch und Computer 2010 Keynote 14
15. Characteriza*on
Models
Evalua*ons
Prototypes
Characterize
activity
with
experiments,
ethnography,
log
analysis
Model
interaction
dynamics
and
interface
variations
Prototype
tools
to
increase
benefits
or
reduce
cost
Evaluate
prototypes
with
users
2010-09-13 Mensch und Computer 2010 Keynote
15
16. A
store
that
knows
your
goal.
Over
50%
reduction
in
task
time.
2010-09-13 Mensch und Computer 2010 Keynote 16
17. Identify
tasty
pages
Waft
scent
backward
along
links
– Loses
intensity
as
it
travels
XC4411 copier
Features:
XC4411 features
digital copiers XC5001 remote diagnostics
color copiers
copiers ...
back
fax machines
other maintenance remote
diagnostics
...
2010-09-13 Mensch und Computer 2010 Keynote 17
18. Partial information goal: 62 copies/min.
“remote diagnostic
technology”
Remainder of
information goal: 92 copies/min.
“speed >= 75”
2010-09-13 Mensch und Computer 2010 Keynote 18
19. Associated Entries
underlined in red
2010-09-13 Mensch und Computer 2010 Keynote 19
20. Conceptually highlight any relevant
User first type search keywords: passages and keywords
“anthrax symptoms”
Draw user attention
2010-09-13 Mensch und Computer 2010 Keynote 20
21. Characteriza*on
Models
Evalua*ons
Prototypes
Characterize
activity
with
experiments,
ethnography,
log
analysis
Model
interaction
dynamics
and
interface
variations
Prototype
tools
to
increase
benefits
or
reduce
cost
Evaluate
prototypes
with
users
2010-09-13 Mensch und Computer 2010 Keynote
21
22. (times capped at five minutes)
10/12 subjects preferred ScentTrails
2010-09-13 Mensch und Computer 2010 Keynote 22
25. Descriptive:
clarify
terms,
key
concepts
Explanatory:
reveal
relationships
and
processes
Predictive:
about
performance
and
situations
Prescriptive:
convey
guidance
for
decision
making
in
design
by
recording
best
practice
Generative:
enable
practitioners
to
create,
invent
or
discover
something
new
2010-09-13 Mensch und Computer 2010 Keynote 25
26. Bongwon
Suh,
Gregorio
Convertino,
Ed
H.
Chi,
Peter
Pirolli.
The
Singularity
is
Not
Near:
Slowing
Growth
of
Wikipedia.
In
Proc.
of
WikiSym
2009.
Oct,
2009.
Florida,
USA
2010-09-13 Mensch und Computer 2010 Keynote 26
27. Number of Articles (Log Scale)
http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia’s_growth
2010-09-13 Mensch und Computer 2010 Keynote 27
30. *In thousands Monthly Active Editors
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31. *In thousands Monthly Active Editors
2010-09-13 Mensch und Computer 2010 Keynote 31
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33. Monthly Ratio of Reverted Edits
2010-09-13 Mensch und Computer 2010 Keynote 33
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35. Preferential
Attachment:
Edits
beget
edits
– more
number
of
previous
edits,
more
number
of
new
edits
Growth rate depends on:
N = current population
r = growth rate of the population
N(t) = N 0 ⋅ e rt
dN
= r⋅ N
dt
Growth rate Current
of population €
population
€
2010-09-13 Mensch und Computer 2010 Keynote 35
36. Biological
system
– Competition
increases
as
population
hit
the
limits
of
the
ecology
– Advantage
go
to
members
of
the
population
that
have
competitive
dominance
over
others
Analogy
– Limited
opportunities
to
make
novel
contributions
– Increased
patterns
of
conflict
and
dominance
2010-09-13 Mensch und Computer 2010 Keynote 36
37. r-‐Strategist
– Growth
or
exploitation
dN N
– Less-‐crowded
niches
/
produce
many
= rN(1− )
offspring
dt K
K-‐Strategist
– Conservation
[Gunderson & Holling 2001]
– Strong
competitors
in
crowded
niches
/
invest
more
heavily
in
fewer
offspring
€
2010-09-13 Mensch und Computer 2010 Keynote 37
38. Ecological
population
growth
model
– Also
depend
on
environmental
conditions
– K,
carrying
capacity
(due
to
resource
limitation)
dN N
= rN(1− )
dt K
€
2010-09-13 Mensch und Computer 2010 Keynote 38
39. Follows
a
logistic
growth
curve
New Article
2010-09-13 Mensch und Computer 2010 Keynote 39
40. Carrying
Capacity
as
a
function
of
time.
2010-09-13 Mensch und Computer 2010 Keynote 40
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45. Source: Hypertext 2008 study on del.icio.us (Chi & Mytkowicz)
2010-09-13 Mensch und Computer 2010 Keynote 45
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47. Joint
work
with
Rowan
Nairn,
Lawrence
Lee
Kammerer,
Y.,
Nairn,
R.,
Pirolli,
P.,
and
Chi,
E.
H.
2009.
Signpost
from
the
masses:
learning
effects
in
an
exploratory
social
tag
search
browser.
In
Proceedings
of
the
27th
international
Conference
on
Human
Factors
in
Computing
Systems
(Boston,
MA,
USA,
April
04
-‐
09,
2009).
CHI
'09.
ACM,
New
York,
NY,
625-‐634.
2010-09-13 Mensch und Computer 2010 Keynote 47
48. Semantic Similarity Graph
Web
Tools
Reference
Guide
Howto
Tutorial
Tips
Help
Tip Tutorials
Tricks
2010-09-13 Mensch und Computer 2010 Keynote 48
49. Tags URLs
P(URL|Tag)
P(Tag|URL)
Spreading
Activation
in
a
bi-‐graph
Computation
over
a
very
large
data
set
– 150
Million+
bookmarks
2010-09-13 Mensch und Computer 2010 Keynote 49
50. 2010-09-13 Mensch und Computer 2010 Keynote 50
51. 2010-09-13 Mensch und Computer 2010 Keynote 51
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53. Dellarocas,
MIT
Sloan
Management
Review
2010-09-13 Mensch und Computer 2010 Keynote 53
54. (1)
Generate
new
tools
and
systems,
new
techniques
(2)
Generate
data
that
looks
like
real
behavioral
data
2010-09-13 Mensch und Computer 2010 Keynote 54
55. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
56. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
43% users engaged in pre-search social Social Interactions
interactions.
GATHER REQUIREMENTS refining
the
reasons for interacting: to get advice, guidelines, feedback,
FORMULATE REPRESENTATION
requirements
or search tips
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
3 types of search: informational search provides a
150 reports of unique search experiences
compelling caseBfor social search support.
mapped to a canonical model of social search.
step B step
process
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
59% users engaged in post-search sharing.
ORGANIZE DISTRIBUTE
reasons for interacting: thought others might be interested,
to get feedback, out of obligation
to self 15% to proximate 87% to public 2%
others others
57. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
• instant 31%
messaging69% to personal social
(IM) Social Interactions
connections near the search box
refining
GATHER REQUIREMENTS
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
• step A clouds from domain FORAGING
tag step A experts
search
• step B users’ search trails process feedback)
other (for
step B
• related search terms (for feedback) Similar to: Glance; Smyth"
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
• sharing tools built-in to (search) site Spartag.us"
• collective tag clouds (for feedback)
ORGANIZE DISTRIBUTE
Mr. Taggy"
to self 15% to proximate 87% to public 2%
others others
58. All
models
are
wrong!
– Some
are
more
wrong
than
others!
So
what
are
theories
and
models
good
for?
They’re
a
summary
of
what
we
think
is
happening
– Ways
to
describe
and
explain
what
we
have
learned
– Predicts
user
and
group
behavior
– Helps
generate
new
novel
tools
and
systems
2010-09-13 Mensch und Computer 2010 Keynote 58
59. 2010-09-13 Mensch und Computer 2010 Keynote 59
60. Word connectivity
Human Movement Study: Fitts’ law
MT = a + b Log2(Dsi/Wi + 1)
18000
English Letter Corpus
16000
14000
12000
10000
(News, chat etc)
8000
6000
4000
[Zhai et al., 2000, 2002]
2000
0
sp E T A H O N S R I D L U W M C G Y F B P K V J X Q Z
Slide adopted from
Mary Czerwinski Keynote
UIST 2004
“Fitts-digraph energy”
27 27
Pij ⎡ ⎛ Dij ⎞ ⎤
t = ∑ ∑ ⎢ Log2 ⎜ +1⎟ ⎥ W ( A →B) = e
−ΔE
kT
if ΔE >0
i=1 j =1 IP ⎣
⎝ Wi ⎠ ⎦
=1 if ΔE ≤ 0
Metropolis “random walk”
optimization
Alphabetical tuning
UIST 2004 60
€ €
61. Between
just
getting
things
done
vs.
finding
out
the
science
2010-09-13 Mensch und Computer 2010 Keynote 61
62. A B
Bucket Testing or A/B Testing [Kohavi et al]
63. Characteriza*on
Models
Evalua*ons
Prototypes
Evalua*ons
Prototypes
Design,
Prototype,
Learn;
If
you
can,
you
should
codify
your
findings
so
that
others
can
Then
Re-‐design,
Prototype,
Learn
replicate
it,
learn
from
it,
predict
Sometimes
that’s
all
you
can
do.
behavior
from
it.
The
basis
of
a
true
scientific
field
2010-09-13 Mensch und Computer 2010 Keynote
63
64. 2010-09-13 Mensch und Computer 2010 Keynote 64
65. Research
Vision:
Understand
how
social
computing
systems
can
enhance
the
ability
of
a
group
of
people
to
remember,
think,
and
reason.
http://asc-‐parc.blogspot.com
http://www.edchi.net
echi@parc.com
WikiDashboard.com
MrTaggy.com
Zerozero88.com
66. 2010-09-13 Mensch und Computer 2010 Keynote 66
67. Appropriate
for
the
occasion
2010-09-13 Mensch und Computer 2010 Keynote 67
68. Poor heuristic
Good heuristic
2010-09-13 Mensch und Computer 2010 Keynote 68
69. Solo
Cooperative (“good hints”)
2010-09-13 Mensch und Computer 2010 Keynote 69
70. Social Tagging Creates Noise
• Synonyms
• Misspellings
• Morphologies
People use different tag
words to express similar
concepts.
2010-09-13 Mensch und Computer 2010 Keynote 70
71. Database Lucene
• Delicious • P(URL|Tag) • Serve up search
• Ma.gnolia • P(Tag|URL) results
• Tuples of • Pre-computed
• Other social cues bookmarks • Bayesian Network patterns in a fast • Well defined APIs
• [User, URL, Tags, Inference index
Time]
Crawling MapReduce Web Server
Web
Server
UI Search
Frontend Results
• MapReduce:
months
of
computa*on
to
a
single
day
• Development
of
novel
scoring
func*on
2010-09-13 Mensch und Computer 2010 Keynote 71
72. framing
Before Search
externally-motivated self-motivated
searchers searchers the context
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
73. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
After Search
28% 72%
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
74. externally-motivated self-motivated framing
the context
Before Search
searchers searchers
31% 69%
Social Interactions
GATHER REQUIREMENTS refining
the
requirements
FORMULATE REPRESENTATION
28% 13% 59%
During Search
navigational transactional informational
FORAGING
step A step A search
process
step B step B
“evidence file”
TRANSACTION SENSEMAKING
search product /end product
28% 72%
After Search
DO NOTHING TAKE ACTION
ORGANIZE DISTRIBUTE
to self 15% to proximate 87% to public 2%
others others
75. For
example,
for
information
diffusion,
it’s
theory
of
influentials
[Gladwell,
etc.]
– reach
a
small
group
of
influential
people,
and
you’ll
reach
everyone
else
Figure From: Kleinberg, ICWSM2009
2010-09-13 Mensch und Computer 2010 Keynote 75
76. From: Sun et al, ICWSM2009
2010-09-13 Mensch und Computer 2010 Keynote 76