Cognitive Internet of Things: Making Devices Intelligent

Session A3
Cognitive Internet of Things:
Making Devices Intelligent
Swami Chandrasekaran
Executive Architect - CTO Office
IBM Watson Innovations
swamchan@us.ibm.com
@swamichandra

Please Note
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an individual user will achieve results similar to those stated here.
© 2014 IBM Corporation Mobility LIVE! 2014 2

Topics
• Preface
• Cognitive Computing
• Cognitive Computing & IoT Solutions/Apps
• What? – Why? – When?
– Observe
– Interpret & Evaluate
– Decide

Let’s get started
Preface

Consider the following questions for a moment
• How can I communicate and have a dialog with my connected
devices using natural language?
• Will the devices be able respond back to me with a degree of
confidence and contextually?
• Can these devices understand the end user at a deeper
psychographic segmentation level and alter the resonance of
the responses?
• How can I discover new insights from my customer / end user
interactions in a timely fashion?
• Will the wearables based apps learn from new data &
observations and get intelligent over time?

Chapter 1
Cognitive Computing

Not …
Yet !

Analytics Russian Doll
Cognitive
Tell me the best course of action
Prescriptive
What should I do to for the best outcome?
Predictive
What could happen?
Descriptive
What has happened?
Business Value

This figure resembling a droid was purely unintentional J
So what are the
Characteristics of a
Cognitive System
Scale in
Proportion
Engage in a
Dialog
Generate &
Evaluate
Hypothesis
Understand
Natural
Language
Provide
Supporting
Evidence
Ingest Variety
of (Big) Data
Respond
with
Degree of
Confidence
Learn with
Every
Interaction
Offer
Contextual
Guidance &
Insights
Support for
Decision
Making
Understand
user at a
Deeper level
Relate
between
Terms &
Concepts

Consider this Natural Language Question
A restaurant in
Chicago?
Several critics have raved about Zhivago and what a
masterpiece it was. Was it shown in Russia in 2001?
Are we talking about
Art or Sculpture or
Movie or Food?
Plain Number (or)
a Temporal
Reference?
Geographic
Reference?
Keyword search and expert systems are not able to recognize the subtleties,
idiosyncrasies, and ambiguities inherent in common human language

This is how a Cognitive System like IBM Watson would
respond with movies related content ingested as Corpus
… and other
possible answers
… and other possible
answers
With a level of
confidence … and Evidence

Cognitive systems enhance our abilities to observe, evaluate
and decide
Observe:
• Learns from a vast body of (unstructured) content
• Looks beyond the surface
• Understand Natural Language
Interpret & Evaluate:
• Generate & Evaluate hypotheses
• Finds relationships between terms and concepts
• Simplifies complex thinking
Decide:
• Understands with me at a deeper level
• Evaluates pros and cons. Helps me discover new ideas
• Lets me be myself & engages with me personally
Learning:
• Learns from every interaction
• Trains with experts and improves with feedback
Observe
Decide
Interpret
&
Evaluate

Chapter 2
Cognitive & IoT

Anatomy of an IoT Solution / Application - Setting Context
Systems Integration
Applications
Cognitive Services
Data at Rest Analytics
Focus of
the session
✔
Data Ingestion & Streaming Analytics
Connectivity Management
Network
Devices / Sensors
Platform &
Services
Connectivity
& Devices
Users of Things
Wizard’s
stitching the
perfect
Composable
Apps
Platform,
Services,
IoT Cloud
Providers of
Connectivity
Makers of Things

Introducing Cognitive Internet of Things (IoT)
• Provide capabilities for IoT apps & solutions to have cognition
• Allows IoT apps & solutions to exhibit characteristics such as,
– Deep natural language understanding
– Accurate & evidence based decisions
– Relating & linguistic analysis
– Maps euphemisms or colloquial terms
– Deeper understanding of user intrinsic preferences / characteristics
– Communicating with resonance
– Knowledge & relationships discovery
– Continuous learning

Now let’s see some examples of a how cognitive services &
capabilities can make IoT apps / solutions intelligent …

Chapter 3
Observe

Question & Answer
• Allow end applications users to converse using natural language
• Understand a question in NL, generate and evaluate hypothesis
and respond with degree of confidence and evidence
• Interpret questions & answers user questions directly based on,
– unstructured content (PDF, Word, HTML, TXT)
– primary data sources (brochures, web pages, manuals, etc.)
– selected and gathered into a body of corpus

Meet Rosy
• Recently purchased and
installed a smarter thermostat
• Very savvy smart phone user
• She has a question about
restricting only authorized
users to be able to configure
& access the thermostat
• Has a smart phone app that
allows her to pose questions in
natural language and have a
conversational dialog
I need to restrict the access to modify certain capabilities in the
thermostat. How can set it up?
Integrate via API
Q&A Dialog
Interact
using NL
Product Corpus
Manuals

Concept Expansion
• Allows IoT apps greater insight across multitude of unstructured documents
• Map euphemisms or colloquial terms to more commonly understood
phrases
• Analyze text and interpret its meaning based on usage in other similar
contexts
• For e.g., a semantic class, such as “drugs” can be expanded to,
– start seed terms à motrin, aspirin, keflex
– post expansion à allegra, lisinopril, metformin, aspirin, equagesic, cimetidine, fiorinal,
vancomycin, avelox, protonix, glimepiride, protonix, verapamil, norco, inderal, hctz, advair
• Well suited for expanding where the unstructured source text does not
contain well formed language (e.g., social media data, email, helpdesk
reports, and other less formal communications)

Meet Zhang
• Remotely monitored patient
• Can use an app to interact with
his healthcare providers
• English not first language
• Need for understanding
nuances in his less than formal
communications
• Concept Expansion service
returns a ranked list of
contextually similar terms
• Learned from the provided
'seed list' against the Zhang’s
interaction history
Interact
using NL
informally
Integrate via API
Concept Expansion
Interaction Seed List
History

Concept Expansion in action

Chapter 4
Interpret & Evaluate

User Modeling
• Use linguistic analytics to extract personality and social traits,
including Big 5, Values, and Needs, from the way that a person
communicates.
• Analyze any digital footprint that the user makes available, such as
email, text messages, tweets, forum posts, and more.
• Leverage cognitive and social characteristics with their
corresponding percentile values as the basis for analyzing
personality and social traits.
• IoT apps can leverage this for targeted customer and end user
interaction and acquisition via personality-driven engagements
(offers, recommendations etc.)

Meet Ravi
• Very vocal and maintains a strong
digital presence
• Has a long day at work !!
• Tweets with certain emotions and
walks to the car
• Car has done a psychographic
analysis of his digital footprint and
alters its response resonance
• Recommends or tunes to the
“comedy” radio channel
• Understands from past behavior,
the driver would want to go to the
gym
Walk to Car
Share Location &
User
Context
Modeling
Recommended
Infotainment
Prediction
Past
Interactions

Meet Ravi … again
• Very vocal and maintains a
strong digital presence
• Seeks recommendation for
happy hour
• Based on Ravi’s location fetch
digital tweets, blogs, FB
updates about relevant places
• Extract Big 5, Values, and needs
• Provide as input to a a
classification ML model to
recommend apt place for Ravi
Seek
recommendation
Share Location &
User
Modeling
Context
Prediction

User Modeling

Relationship Extraction
• Intelligently finds relationships between sentences components
(nouns, verbs, subjects, objects, etc.) in unstructured text
• Extract entities such as person, organizations, locations, devices,
events, etc., and relationships between them
• Relationships help easily understand the meaning & intent of
individual sentences and documents
• Enables automated processes to understand unstructured
content in healthcare, drug discovery, financial reports, news
and blog monitoring, etc.

Relationship Extraction in action

Chapter 5
Decide

Message Resonance
• Communicate with people with a style & words that suits them
• Analyzes content and score how well it is likely to be received
by a specific target audience.
• Analysis is based on content that’s been written by the target
audience itself (team sports fans, new product users, etc)
• Add capabilities within the IoT apps (smart TV, personal wellness)
to maximize the resonance of your messaging
• Enables IoT apps to best engage with end users and facilitate
consistent messaging

Message Resonance in action

Cognitive Cooking

How Cognitive Computing will help IoT Solutions & Apps?
• Overcome Complexity [1]
– Ingestion of 4V (volume, variety, velocity, veracity) data esp., unstructured ones
– Uncover hidden relationships (Bad weather à Tweets à Extract Insights)
• Getting the Big Picture
– Extract & Correlate patterns, concepts & relationships across content
– Expansion of concepts
• Augment our Senses [1]
– Converse in natural language
– Deeper understanding of the individual & group
– Chip modeled on the brain
• Support Discovery & Decision Making
– Hypotheses based inferences
– Offer contextual insights
– Support at point of decision making
1 - Smart Machines: IBM’s Watson and the Era of Cognitive Computing - John E. Kelly III and Steve Hamm

Some Interesting Links
• IBM Watson
– http://www.ibmwatson.com
• IBM Bluemix
– http://www.bluemix.net
• Node-RED
– http://nodered.org/
• Watson Ecosystem program
– http://www.ibm.com/smarterplanet/us/en/ibmwatson/ecosystem.html
• Growing social dialogue
– Twitter: @IBMWatson
• IBM Watson: A Platform for Innovation in the New Era of Computing
– http://asmarterplanet.com/blog/2014/04/ibm-watson-ecosystem-platform-innovation.html
• IBM unveils a computer that can argue
– http://finance.yahoo.com/blogs/the-exchange/ibm-unveils-a-computer-than-can-argue-181228620.html

Cognitive Internet of Things: Making Devices Intelligent

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