Keynote at Smart Home Conference

Smart Home 2017 | Roland Hänggi | 21.03.2017 | © Electrosuisse 1
Roland Hänggi
Senior Electronics Architect, IBM Global Electronics Industry
European CTO IBM Electronics Industry
The future of the IoT will be cognitive –
Implications for the Smart Home

Is this a experience ?

This is a experience !
Die letzten >35 Jahre haben wir
Technologie entwickelt und unser
möglichstes getan diese als
Innovation zu verkaufe.
Die Heutige jungen benutz Z.B. ihr
Mobiel Phone einfach ohne über
die Technik nachzudenken. Sie
kaufen es weil es Cool ist oder sie
optisch anspricht aber nicht wegen
den Technischen Spezifikationen.
Davon sind wir alle betroffen,
benutzen und nicht nachdenken
wie geht dies !

Contextual intelligence is a precondition for
the Smart Home vision
Contextual intelligence
Making appliances understand what a person or other
appliances in the household are doing for making the user
aware of the overall environment or smooth the working
process across various devices. An intelligent / smart
appliance thus is understanding related events that serve a
specific consumer purpose and are part of a user behavior
that is analyzed for predictive activities and preventing
dangerous situations.
Relevance to home appliances:
§ Disabling gas if no adult is around
§ Raise an alarm if a pan/cook pot is positioned
unsafe

The Internet of Things roadmap for Smart Homes
Source: Parks Associates Webcast – Internet of Things: Smart Home Success through Bundled Services

By 2020, there will be 80 billion connected
devices worldwide.
Worldwide: 10 connected
devices for every household
by 2020
Worldwide: 5 connected
devices for every user by
2020
5 billion Internet users by
2020
Approx. 500 devices with
unique digital identities per
square km by 2020 for the
Internet of Things

The entertainment room & the kitchen are
perceived as most exciting smart home areas
in the house
Source: Icontrol 2015 State of the Smart Home Report

8
Connected
Appliance
CA-informed customer
service
Messaging
Platform
Accelerate detection of
production & quality issues
Diagnostics &
repair Analytics
Increase first call complete %
Production
Analytics
Avert unnecessary part
replacement
Partner Consumables
Warranty Extensions
Replacement Appliances
Customer
Analytics
•eComm Platform (partner vendor
capable)
•Campaign execution &
management
•Next Best Action
•Price optimization
•Etc.
Customer Interface Understand End
Customers
Call Center
Solution
Design based on
extensive use data
Use & Design
Analytics
Sales to CA
owners
Portals
Connected Appliances includes the use
cases

Example: Evaluate capabilities of robots to interact with
humans
Robot as sales staff at an appliance store
(1) Find customers
(2) Approach
(3) Estimate the customer
(4) Assemble sales plans
(5) Lead conversation
(6) Product Q&A and demo
(7) Confirm
inventory &
delivery
Full-text
recognition
People
perception
POMDP
settings
Proactive
Q&A
Goal-oriented
task flow
Customer
recognition
External
devices
People
tracking
Age and gender
estimation
Word-level
recognition
Computer vision
Speech technology
Computer vision
Sensing
Control
Actuators
Dynamics
Computer vision
Machine learning
Speak
Speech synthesis
Character design
Gestures
Motion
Character design
Reactive
Q&A
Autonomic level
flow
External
systems
e.g., CRM
e.g., remote controller

IoT in Insurance: The Connected Insurer
Today, we see 3 main areas where the insurers are focused
1. Connected Home for risk mitigation
2. Connected Car for driving behavior and services
3. Connected Life for health and wellness and disease
management
Connected Home
Connected Life
Connected Car

How to make a Smarter Home intelligent
• Assuming to have in 2020 80 billon of connected devices, the
difference how to improve them. (Structured Data)
• Sensor Data only is not enough we need to correlate them with
globally available information.
• Global available data are mainly unstructured.
• Also Humans nowadays like to communicate with with technology in
natural language.
• To process natural language and unstructured data cognitive
computing is needed.

Cognitive systems are fundamentally
different from what we have today
Adapt and make sense of all data; “read”
text, “see” images and “hear” natural
speech with context
Understand
Reason Interpret information, organize it and
offer explanations of what it means,
with rationale for the conclusions
Learn Accumulate data and derive
insight at every interaction,
perpetually

Complexity of IoT solutions IoT is a
monumental programming challenge
Programmable computing thrives
in prescribed, predictable
scenarios but is too limited for
the complex IoT landscape.
Cognitive systems aren’t programmed.
They learn from virtually every interaction
and the surrounding context to unleash the
potential of the IoT.

Cognitive computing can relieve the cognitive
overload from data volume & complexity

Advanced analytics integration in IoT apps
Textual Analytics
Natural Language Processing
Video / Image Analytics
Machine Learning

Watson Cognitive Services: APIs available
Language
• AlchemyLanguage
• Concept Expansion
• Concept Insights
• Conversation
• Document Conversion
• Language Translation
• Natural Language Classifier
• Personality Insights
• Relationship Extraction
• Retrieve and Rank
• Tone Analyzed
Speech
• Speech to text
• Text to speech
Vision
• AlchemyVision
• Visual Insights
• Visual Recognition
Data Insights
• AlchemyData News
• Tradeoff Analytics
http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud/services-catalog.html

Question as a string
“Will the storm hit job site #123
tomorrow ?”
Question’s class (e.g.
temp, rain, snow, wind etc.)
Class=‘weather’
Class=‘snow’
WAV files
WAV files
(Class,
Location, Time)
(‘Tomorrow’)
Weather data for specified
time and location
Chances of stormy weather in
Detroit tomorrow is 20%‘
Stores DB
Job site #123 is in Detroit MI
Mixing cognitive and “standard” analytics for
a solution with well-defined scope

“For Valentines Day, I want to take my wife to Paris. Can you book a Hilton Hotel for me?”
CognitiveEnablesHuman-centricAnalytics

Web Search Engine

Web Search Engine Siri
“Calling Mr. Valentine”

Web Search Engine Siri
“Calling Mr. Valentine”
Cognitive Interaction
§ Do you also need flight bookings
to Paris?
§ Do you need flowers in the
room?
§ Do you want a dinner
reservation?

Celebration
Couples
Flowers
Trip
Dinner
Romantic
Feb 14
…
“Watson101”–howItWorks–BuildingSemantic
Networks

Celebration
Couples
Flowers
Trip
Dinner
Romantic
Feb 14
…
Networks
From
To
Plane
Train
Car

Celebration
Couples
Flowers
Trip
Dinner
Romantic
Feb 14
Trip
Destination
Plane
Car
Hotel
Schedule
City
Europe
Destination
Romantic
Family
Hotel
Celebrity
Paris
London
…………
Networks

Celebration
Couples
Flowers
Trip
Dinner
Romantic
Feb 14
Trip
Destination
Plane
Car
Hotel
Schedule
City
Europe
Destination
Romantic
Family
Hotel
Celebrity
Paris
London
…………
Networks
In reality, Watson is a bit more complicated than this:
• Connections have „weights“ to them – if the answer is identified as correct during a learing
phase, links become stronger; if it is wrong, links become weaker
• Same applies to the semantic network itself. As Watson‘s knowledge base grows, semantic
networks become broader and deeper
• Today Watson has semantic networks that have thousands, sometimes millions of terms to
them

Typical Home appliance today or near future
All this home appliance are connected and programmable but not cognitive.
They only understand structured sentence and not natural Language

Some example to illustrate the difference
• Asking a question about the weather with those devices looks like
– What's the weather outside/Baden?
– What’s the Weather here and in Munich on Wednesday till
Friday?
– Please book me a hotel room in Munich?
• Using natural language you could ask
– Do I need to carry an umbrella when I travel to Baden?
– I’m traveling to Munich on Wednesday how should I dress me u
– up?
• Do you have already a flight booked?
• Do you need a hotel room?
• …

Some more cognitive use case for a smarter
home
• Assuming all this device has an camera, speaker and microphone
they could act as watch dogs.
– With personalized profiles of each person in the house hold
recognizing the voice picture and personal preference and
habits.
– Somebody enters the room camera uses face recognition
service to recognize who it is of if the person are not living at this
home.
– Somebody enters the home and says something microphone
uses voice recognition service to recognize who it is of if the
person are not living at this home.

Some more cognitive use case for a smarter
home (cont.)
• Assuming all this device are fully connected and person comes
home after stressful day.
– With personalized profiles of each person in the house hold
recognizing the voice picture and personal preference and
habits.
– Person says with angry voice “I had a horrible day, I need a cool
beer”
– Voice mood analysis recognizes stress, tone analyzer
recognizes stress based on the wording in the sentence.
– Adjusting the response voice with an more assuasive voice and
response based on the profile information.
–

In the dress room
• How shall I dress me up today
– Weather information, casual, business, weekend
– Keyword area
• Location
• Day of the week
• Profile information
• I’m going on vacation to Dubai next Sunday for 10 days.
– Weather information what is needed because of temperature ,
season related, vacation as dress indicator,
– Keywords are
• Vacation
• Location Dubai
• Weather at location
• Next Sunday (exact date)
• Duration
• Profile information

In the bad room
• What's the weather forecast for today
– Keyword area
• Location
• time
• I want to book a restaurant
– Keyword area
• Location
• Restaurant using profile information
• Please order me a taxi for
– Keyword area
• Location
• Normal taxi, UBER using Profile info.

Teaching instead of programming

Keynote at Smart Home Conference

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