Thingmonk 2015

20
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

Transcript of Thingmonk 2015

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

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

app overload makes us dumb

source lost, seen on Twitter

we want intelligent

thingsthat talk

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

excerpt from

time

sleep monitor

schedule

location awareness

building control mobilitycapacity

weather

prioritisingplanning

provisioning

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

a solid scientific

foundation

no magic involved

enchantment has

acquiring knowledge == learning

machine learning

creative thinking!=

decision makingwithstatistics + algorithms

==

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

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

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

the hypothesis itself is a

mathematical model

explain this to your neighbour

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

from https://hello.is

the issue with missing data

given all relevant features, machine learning can discover the causality between them

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 dependent on

probabilities andabstract quantities

@BorisAdryan

adding to our anxiety of uncertainty,the conversational IoT may potentially feel repetitive, disruptive and intrusive!