Research and Innovation
Machine Intelligence:
An Investigation in the
Application of
Hierarchical Temporal
Memory
L. Salemi, Professor
Centre for Construction and
Engineering Technologies
May 2009
Introduction
Project was approved in Oct. 2008
• Seed Funding - $6,250
• OCE Connections Grant - $3,000
Student participation from 3 programs
• T146 Electro-Mechanical Engineering Technician
• T147 Computer Systems Technology
• T121 Mechanical Engineering Technology - Design
Introduction – The Team
Research Assistants
• Clayton Wozney
• Steven Irwin
• Michael Joyette
• Olek Kushnarenko
• Scott Vannan
• Terence D'Cunha
• Avinash Singh
• Intiaz Abdulla
Section
T121
T146
T146
T146
T146
T147
T147
T147
Status
Paid Researcher
Course Credit
Course Credit
Volunteer
Volunteer
Course Credit
Course Credit
Course Credit
• Volunteers - Albert So, Bruno D’Agostino
Introduction – Industry Involvement
Company
Status
Industrial Technical Services
– Reynold Ramdial
– Amit Setti
In Kind Sponsor
•
Grace Instrumentation & Controls
– Terry Grace
Equipment Donation
•
Hoskin Scientific
– Marc DeGrace
Technical Advisor
•
Hatch Engineering
– Dennis Phair
Technical Advisor
Equipment Donation
•
ISA Toronto Section
– Currie Gardner
Presented at the ISA Technical
Conference during the Ontario
Process and Automation
•
Technical Support
Show
April 2009
Introduction – The Plan
Phase 1: Oct – Dec 08
C. Wozney
Paid Researcher
Investigate
HTM
Technology
Create
Workspace
Rm. C504A
Wozney to
manage
Phase 2
6 - Student Volunteers (T146)
Students to
Work for
Course credit
Phase 2: Jan – May 08
Collect
Data
Plan B
Build the
Infrastructure
Simulate
Remote
Site
Control
Objective
Apply Intelligence to Building Automation
Applications
Use one of the classrooms to collect data
•
•
•
•
HVAC (Heating, Ventilation, and Air Conditioning)
Lighting (Occupancy based)
Security (Access control, intrusion)
Security Cameras
Incorporate intelligence to
•
•
•
Turn off the lights when no one is in the room
Lower the temperature
Monitor room occupancy
Research Question
How can we make a machine intelligent?
But first, what is intelligence?
• Human Intelligence
• Machine Intelligence
• Artificial Intelligence
• Military Intelligence
There is no universal definition
Research – The Challenge
Intelligence Test: Which one is flat?
Research Question
Answer: All of the above are flat
Does intelligence lie in the senses of the beholder? Yes/No?
•
•
•
Our 5 primary sensors provide an abundance of data
Our intelligence forms the conclusion (BELIEF)
Where is this “intelligence” located and how can we make
a machine do it?
Research – The Answer
Hierarchal Temporal Memory
(HTM)
• Developed by Jeff Hawkins founder of
Numenta and inventor of palm pilot & treo
• HTM is modeled after the neocortex
• Data is fed to neuron-like networks that
learn to recognize patterns and sequences
that change over time
• When presented with “new” data the HTM
is good at predicting what it is
• www.numenta.com Book: On Intelligence
Research – The Technology
• NuPIC (Numenta Platform for Intelligent Computing)
• Vision4 Demo program was designed using HTM networks
What’s this?
Research – The Technology
Vision4 Demo program was trained to recognize 4
different images
• Sail Boat
• Rubber Duck
• Cell Phone
• Cow
Research – The Technology
Its not perfect but neither are we.
Sailboat ???
Research – The Technology
• More detail provides better recognition
It’s a duck
Research – The Technology
• HTM is capable of recognizing several variations
Cow in the background
How far away are we?
• Not a question of if,
but when!
• Next 400 years?
• Only 400 years have
passed since we
thought the earth
was flat.
"I visualize a time when we will be to robots what dogs are to humans.
And I'm rooting for the machines." - Claude Shannon (1916 - 2001)
Industry Problem
Phase 2 - How to apply HTM intelligence to
Building Automation applications
Identifying an industry problem was difficult
• Many “smart” systems already out there
• HTM was beyond our scope – now what?
• HTM would be hard to sell to industry partners
without something to demo
Leo’s Problem
• Engage students and comply with course
outlines (course credit for research work)
• Build something that we could demonstrate
to attract industry partners
• No Clayton – No HTM
• Go with Plan B
Methodology – Plan B
Plan B – Make sure Plan A works
Build the infrastructure to collect real time data in room C504A
• Simulate something that is used in industry (remote water
pumping station)
• Be able to monitor and control the site remotely via the web
• Use current technologies plus add some extra’s
– SCADA (Supervisory Control And Data Acquisition)
– Security Alarm and Video Surveillance
– Process Cameras for operators
– Full network integration for each subsystem
• Incorporate intelligence between all of the subsystems
Results
Remote monitoring & control of pumping station
– Operator has full control of station (typical)
– Process cameras allow operators to view the station
as if they were present
– Surveillance Alarm & Cameras connected via the
web and VOIP system (24/7 monitoring station)
– SCADA system used to the control process
– More features to be added
Infrastructure Testing Lab
VOIP
Operator Terminals
Security
System
REMOTE
SITE
Pumping Station
SCADA
Control
System
Surveillance Cameras
Process Cameras
Lessons Learned
Benefits gained
• Excellent learning experience for students
and professor
• Infrastructure Testing Lab – a place for us
to work and others to utilize
• Opportunity to learn new technologies and
add to curriculum
• Interdivisional co-operation
• Industry Partners
Lesson Learned
Bumps along the way
• Hard to convince some of the course
coordinators to let students do this for a
course credit (T147 was the exception)
• Uncertainty of the use of room 504A makes
it difficult to plan future projects
Future Research
• Full integration of sub-systems using an OPC
data manager
• Train the HTM using the remote site data
• Work with Video Analytics
• Design and build sensors that are HTM-ready
• Attracted an industry sponsor who is
interested in using solar power in a remote
site application
Questions
Thank you and Acknowledgements
Meadow Larkins and the ARI team
The student research team
Reynold Ramdial and Amit Setti from
Industrial Technical Services
Members of the technical advisory
committee
Jeff Litwin for supporting our efforts
ISA Toronto for allowing us to present at
their technical conference in April