Potentially creepy human-computer interactions in the future of the consumer IoT. Lots of raw data need to be analysed and are represented as result of machine learning exercises. However, consumers are likely scared of probabilities. How can UX address these issues?

1. it’s none of
your effing
business
Computers cannot think.
Machines have to learn.
From us.
We will have to have a
conversation, James.
Why are you late, James?
James? I asked why you
are late.@BorisAdryan

2. modified, image from http://www.householdappliancesworld.com
health
management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather
monitoring
room occupancy

3. health
management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather
monitoring
room occupancy
app overload makes us dumb

4. source lost, seen on Twitter
we want
intelligent
things
that talk

5. http://knowledge.openboxsoftware.com/blog/
the-evolution-of-business-intelligence
excerpt from

6. time
sleep
monitor
schedule
location
awareness
building
control
mobilitycapacity
weather
prioritising
planning
provisioning

7. raw data
information
knowledge
actionable
insight
action
reaction
barometric pressure, temperature, coordinates, schedule, …
snow storm coming, airport hotel, need to travel
flying and snow don’t go together
rebook
flight
“context”
structure
rules
“conversational”
dynamic
acquired

8. a solid
scientific
foundation
no
magic
involved
enchantment
has

9. acquiring
knowledge
== learning
machine
learning
creative
thinking
!=
decision making
with
statistics +
algorithms
==

10. blog post at https://iot.ghost.io/is-it-all-machine-learning

11. there’s no absolute
truth out there
data
✓ hard facts
✓ intuitive
probability
✓ likelihood of some hypothesis
being true given the data

12. 30 40 50 60 70
average speed at this point [MPH]
time to target
[min]
10
20
30
40
50
we have a
sense for
simple
probabilities

13. data
temperature
wind speed
wind direction
precipitation
air pressure
airport code
airline
aircraft
fully booked?
avg delays
cancellations
serve booze?
black
box
training
flights
cancelled
in the past
classifier
ranked list of
relevant
features
weight of
features
thresholds for
features
performance
metric
new data
prediction

14. the hypothesis
itself is a
mathematical
model
explain this to
your neighbour

15. good decisions
are based on
experience
machine learning
is an iterative
process
training
classifier
performance
assessment
good enough?
get on with life
moredatafortraining
data
no
yes

16. from https://hello.is

17. the issue with
missing data
given all relevant features,
machine learning can discover
the causality between them

18. self-learning systems
will have to seek
‘missing’ data
other than saying ‘urgent
meeting’ in the calendar,
how can the system know
it’s really urgent?
…preemptively

19. things getting
more creepy…
“Is there
something you
should tell me,
Boris?
I thought your wife
was travelling…”
…when they’re
conversational

20. life is becoming
dependent on
probabilities and
abstract quantities
@BorisAdryan
adding to our anxiety of
uncertainty,
the conversational IoT may
potentially feel repetitive,
disruptive and intrusive!