The Smartest Smart Cities Will Benefit from the Confluence of AI and Big Data in IIoT

© 2018 Sohrab Modi. All rights reserved.
Pragmatic Use of Artificial Intelligence
in Smart Cities
Sohrab Modi
Sr. Vice President & Chief Technology Officer
Echelon Corporation
6/26/2018
1

Forward looking statements
The remarks I'm about to make, and the opinions I am about to
express, today are my own, and do not necessarily reflect the
views of Echelon Corporation or its board and
management. You should not construe my remarks to reflect
the current research and development or products plans of
Echelon, and Echelon retains all of its rights in its intellectual
property.

Agenda
1. Introduction to artificial
intelligence (AI)
4. Summary2. The symbiotic
relationship between
AI, IoT and lighting
3. Challenges to artificial
intelligence for smart
city deployments

What is Artificial Intelligence (AI)?
John McCarthy came up with the
name "artificial intelligence" in 1956.
• Early AI tried to understand the rationality behind
human behavior.
• For computers to rationalize human behavior,
humans first need to clarify their own logic. Ex:
• We say airplanes fly (like birds), but we don’t say
ships swim (like fish)
Classical early AI
• The scope and complexity challenges of this
approach quickly led into the “winter of AI”
(1988-1989)
• Given today’s perception of AI, we explore the
question,…. “what changed?”
*https://commons.wikimedia.org/w/
index.php?curid=1297606
*

Today’s classical perception of AI
AI means different things to different people.
To many people, AI is a term for “something wonderful”,
but one that is also confused with
* @book{Goodfellow-et-al-2016, title={Deep Learning}, author={Ian Good fellow and Yoshua Bengio and
Aaron Courville}, publisher={MIT Press}, note={url{http://www.deeplearningbook.org}}, year={2016} }
It’s because math is
the language of
machines)
Thankfully, no deep understanding of math is needed to
deploy AI, only an understanding of how AI can best
addresses the problem
*
Formulas, capable
of giving a
headache
Graphs resembling
Einstein's theory of
relativity
*

Two reasons to get really excited
about artificial intelligence.
We are touched by artificial intelligence everyday
and may not even know it
FICO have been using AI to mange risk for many years.
Speech recognition is quickly becoming a norm for
consumers.
All respective trademarks are owned by
their respective owners. All rights reserved
®
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• Transportation
• Healthcare
• Manufacturing
• Financial
Market Verticals
Artificial intelligence is everywhere
• Legal
• Education
• Agriculture
• Entertainment
/ Gaming
• Media/Social
• Retail
• Security
• Home

For the end user it’s about understanding &
managing their business like never before
Reporting what
happened in the past
Predicting the future
With artificial Intelligence , we go from
to
Predicting the future is dependent on
“learning” from the past

A simple problem: My electricity bill is too high!
Traditional Fix: Shut off all devices when not in use.
The fix may not solve the problem
– If someone is stealing your energy.
– Or if it’s related to peak energy usage costs.
– Or if it’s related to faulty devices, etc.
Data (from the past) now becomes the basic currency of
Artificial Intelligence

Big data
One way of doing this is by
broadening the “knowledge” in
the system through additional
data sets.
Successful AI predictions are dependent on data.
• The Industrial IoT (IIoT) is a natural source of valuable
data
• This resultant symbiotic relationship between AI and the
IIoT augments the predictive capabilities of AI
One value of “big data” lies in its ability to influence
AI decisions.

A farmer in California with “excellent” credit obtains a large bank loan in
2014.
• The farmer defaulted in May of 2017
• Could big data have helped AI ascertain risk
if the bank had expanded its data set to include “climate data”
• AI may have increased the risk profile based on drought
predictions in California in 2015 and 2016
Example: Enhancing AI through Big data

If AI is so “wonderful” then why do
Machines find this a hard
problem to solve.
Humans have a much
easier time solving such
problems.
Is this a cat?
Understanding this may lie in examining
the two approaches to Artificial Intelligence
Could this have been a panther?
– What % of recognition was influenced by the collar and
the background?
– What % of recognition was influenced by the initial
suggestion that this might be a cat ?

 But acquiring knowledge is an arduous process of
extracting information from human experts.
 Having no “inference capability” made it near
impossible to scale and adapt (ex: speech /
image patterns, etc.)
Initially solved problems described by a set of formal rules
that was applied to a large Knowledge base
A Knowledge-based approach to AI

 The ability of the system to acquire its own knowledge
through observations or learning
 To do this the system must not only recognize patterns
from raw data but also “learn” to adapt its behavior
from its experience.
Machine learning based approach to AI
This leads to its own sets of challenges:
 Learning with a purpose: How do you know that the
system has learned something?
 Performance: How do you measure how well the
system has performed?

– Energy monitored
at a point of
aggregation of
multiple devices.
– Identifies
appliance
signatures in the
overall power
signal.
* Srinivasan, D., Ng, W.S., and Liew, A.C. Neural-Network-Based
Signature Recognition for Harmonic Source Identification. IEEE
Transactions on Power Delivery, 21(1):398-405, 2006.
EXAMPLE:
An analytical approach to power consumption
Dispensed the need to have energy being
monitored at “every” point of consumption

Acquiring data is the essential
1st step in the decision process.
Extracting knowledge, to
derive the meaning behind
the data, is essential.
Getting to Intelligence by
analyzing the causes,
changes and effects to such
patterns
The system develops
“predictive” abilities, based
on the ability to apply
intelligence
Summarizing the four essential steps to AI
1 Data Acquisition
Lots of data
Knowledge
Recognition of different
patterns
2
Wisdom
Ability to anticipate and
react
4
Intelligence
Understanding the behavior
behind the patterns
3

Why Now?
Three developments resulted
in today’s perfect AI storm

#1 An emphasis on Machine learning
Foundation equations (rules) vs. learning.
#1 An emphasis on Machine learning
Foundation equations (rules) vs. learning.
#2 Solving real world problems
– Applied AI: Enables specific problem focused solutions
– General AI (AGI): Concentrates on the science of
intelligence
– Technical Singularity: Indulges in the art of super
intelligent machines
#3 The growth of big data
– Solving real world AI problems are probabilistic
approximations
– Big data improves the predictability of AI systems
© sohrab modi

The symbiotic relationship
between artificial
intelligence (AI)
and Internet of Things (IoT)

The Internet of Things (IoT)
Every prediction in IoT talks about billions of
things and trillions of dollars
$14.2
26
50
trillion by 2030 billion devices by 2020
$7.1
trillion by 2020
billion devices by 2020
IoT
Consumer IoT Industrial IoT
Insights (AI) through big data is the
underlying opportunity because of the IoT

A vertical solution of the IoTSMART CITIES |
Big data
Intelligence improves by
integrating different types of
data & logic
• Sustainability Management
• Asset Management
• eHealth
• eGov
Where do you start?
How do we get to a smart city?
• Traffic Management
• Facilities & Buildings Management
• Safety & Security Management

A pragmatic approach to smart cities and IoT
AN INTELLIGENT LIGHTING “PLATFORM”
• The platform becomes a conduit for
other devices/data
• The data becomes a conduit for
Artificial Intelligence
• Lights are pervasive in a city
• Connecting the lights makes
the “platform” pervasive
• This pervasive platform can
acts as a backbone to a
smart city.

From intelligent lighting platform to smart city
platform
Connectivity and application services platform
Lighting and Sustainability Management
Facilities & Buildings Management
Safety & Security Management
Traffic Management
Multiple media:
(PLC+RF+cellular
+WiFi, etc)
Multi-
protocol
based
Universal
gateway
Application
management
system
ServicesPlatform

• Lighting controls also rely on weather-driven decisions
• Could be used to modify the color of light under
changing weather conditions (white-out, etc.)
• Could be used for evacuation lighting in emergency
situations
Example: Weather adaptive lighting using big data
IBM Watson
IoT
White Bear Lake,
MN
The Weather Company

Example: A Smart City Cognitive Camera
• Edge-based AI to analyze per lane traffic patterns
and reduce bandwidth.
• Uses machine learning to “learn” about the
surrounding environments on each deployment.
• Analyze and plan transportation strategies.
• Detect and alert hazardous conditions, traffic
accidents, safety related issues, etc.

A natural assumption is that AI is so complex
that it must be wonderful !
4K Mask RCNN COCO Object detection and segmentation
https://www.youtube.com/watch?v=OOT3UIXZztE

Enterprise Parking Garage
and Lot Solution
© sohrab modi
• Uses AI to recognize the availability of parking spots
• Subscribers on smart phones could inform drivers in a
city looking for parking availability.
• Detect and alert safety/security related concerns

Challenges to artificial intelligence,
IoT and smart cities

Many Challenges To Artificial Intelligence
Why do we
need it?
When do
we use it?
How do we
enable it?
What will it
cost?
Who owns
the data?
What about
privacy issues?
What are the security implications?
How does one measure it ?

Lon M-Bus
BACnet Modbus
…
Legacy Systems
BLE Zigbee
WiFi Cellular
...
Emerging technologies
Today, end-to-end IoT connectivity issues is a problem for AI
Public Clouds
Supporting AI
Operational
Technologies (OT)
Information
Technologies (IT)
?
A confusing array of
connectivity options prevents
seamlessly linking the data
Challenges Unique To The Industrial World

In Summary:
Considerations towards smart city solutions
Plan for
the future
Identify the
problem you
are trying
to solve
Focus on best
way of solving
it – cost,
privacy,
security, etc.
1
2
3

Planning for artificial intelligence
#1: Identify the problem you are trying to solve
Prevention Optimization Personalization Prediction
• How well will this work under low light conditions?
• How well will it work at the edge?
• What infrastructure do I need to enable this?
• What will it cost?
#2: Focus on best
way to solving it –
cost, data privacy,
security, etc.
4K Mask RCNN COCO Object detection and segmentation

#3: Plan for the future
Where expanding the initial problem set is a given
Embrace
devices and
protocols
Extend
data and
business logic
Enhance
With Artificial
Intelligence
Consider an expandable smart city artificial intelligence/IoT platform
Embrace, Extend and Enhance

Educate yourself
Today we know
more,…
…but there is a lot
we don’t know

The Smartest Smart Cities Will Benefit from the Confluence of AI and Big Data in IIoT

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