Project management: from Stone Age to DevOps - TopConf Tallinn 2017
TopConf Linz, 02/02/2016
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Transcript of TopConf Linz, 02/02/2016
the friction zone between probability, machine learning
and user experience in the consumer IoT
@BorisAdryan
http://knowledge.openboxsoftware.com/blog/the-evolution-of-business-intelligence
excerpt from
imagine!
modified, image from http://www.householdappliancesworld.com
health management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather monitoring
room occupancy
health management
air conditioning
smart heating
communications
security
entertainment
lighting controlweather monitoring
room occupancy
individual apps are NOT [ðə ˈfjuːʧə]
time
sleep monitor
schedule
location awareness
building control mobilitycapacity
weather
prioritisingplanning
provisioning
acquiring knowledge == learning
machine learning
creative thinking!=
decision makingwithstatistics + algorithms
==
raw datainformation
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”dynamicacquired
there’s no absolute truth out there
data
✓hard facts ✓ intuitive
probability
✓ likelihood of some hypothesis being true given the data
30 40 50 60 70average speed at this point [MPH]
time to target [min]
10
20
30
40
50
we have a sense for simple probabilities
p-value < 2.2e-16
confidence interval
FDR
posterior probability
95%
0.025
5%
explain this to your neighbour
Battery is going to die today, p < 2.7x10-3
simple x -> y mapping x
k
h
lq
w
g
f-> y mapping
your FitBit temperature
your friend’s dog
the car
computationally, your life is incredibly messy
datatemperature wind speed
wind direction precipitation air pressure airport code
airline aircraft
fully booked? avg delays
cancellations serve booze?
black box
trainingflights
cancelled in the past
classifierranked list of
relevant features
weight of features
thresholds for features
performance metric
new data
prediction
good decisions are based on
experience
machine learning is an iterative
process
training
classifier
performance assessment
good enough?
get on with life
mor
e da
ta fo
r tra
inin
g
data
noyes
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
things getting more creepy…
“Is there something you should tell me, Boris?I thought your wife was travelling…”
…when they’re conversational
life is becoming increasingly dependenton 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