My talk at Smart IoT London. About adding 'context' for data analytics in the consumer IoT, touching on machine learning, hidden variables, and UX/UI of communicating probabilities.

1. How ‘smart’ is the IoT?
Do we want it any smarter?
(Be careful what you wish for…)
@BorisAdryan

2. industry is easy
Internet replaces wire
It’s all about the
context
M2M
consumer
IoT
defined I-P-O
like it’s 1975
context
context
context
Is this hot?the consumer
is not

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

4. 2016: a little reality check

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

6. health
management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather
monitoring
room occupancy
individual apps are NOT the future

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

8. David Rose: enchanted objects
as UX paradigm
modified, image from http://enchantedobjects.com

9. a solid
scientific
foundation
no
magic
involved
enchantment
has

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

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

12. 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

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

14. 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

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

16. 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

17. the hypothesis
itself is a
mathematical
model
explain this to
your user

18. 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

19. from https://hello.is

20. the issue with
missing data
given all features,
we can discover
the causality
between them

21. 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

22. most of us will want to
be ‘spied on’ for the
sake of convenience
but as consumers
we need to be in
the know

23. http://iot.ghost.io

24. it’s none of your
effing business

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

26. life is becoming increasingly dependent
on probabilities and abstract quantities
@BorisAdryan
adding to our anxiety of uncertainty,
the conversational IoT may potentially
feel repetitive, disruptive and intrusive!
quantitative and computational thinking
is going to become an essential skill