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Research and Research and Innovation Innovation Machine Machine Intelligence: An Intelligence: An Investigation in Investigation in the Application the Application of Hierarchical of Hierarchical Temporal Memory Temporal Memory L. Salemi, Professor Centre for Construction and Engineering Technologies May 2009
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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 - PowerPoint PPT Presentation
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Page 1: Research and Innovation

Research and InnovationResearch and Innovation

Machine Intelligence: Machine Intelligence: An Investigation in the An Investigation in the

Application of Application of Hierarchical Temporal Hierarchical Temporal

MemoryMemory

L. Salemi, Professor Centre for Construction

and Engineering Technologies

May 2009

Page 2: Research and Innovation

IntroductionIntroduction

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

Page 3: Research and Innovation

Introduction – The TeamIntroduction – The Team

Research Assistants Section Status• Clayton Wozney T121 Paid Researcher• Steven IrwinT146 Course Credit• Michael Joyette T146 Course Credit• Olek Kushnarenko T146 Volunteer• Scott Vannan T146 Volunteer• Terence D'Cunha T147 Course Credit• Avinash Singh T147 Course Credit• Intiaz Abdulla T147 Course Credit• Volunteers - Albert So, Bruno D’Agostino

Page 4: Research and Innovation

Introduction – Industry InvolvementIntroduction – Industry Involvement

Company Status

• Industrial Technical Services In Kind Sponsor – Reynold Ramdial– Amit Setti Technical Support

• Grace Instrumentation & Controls Equipment Donation – Terry Grace

• Hoskin Scientific– Marc DeGrace Technical Advisor

• Hatch Engineering Technical Advisor– Dennis Phair Equipment Donation

• ISA Toronto SectionPresented at the ISA Technical– Currie Gardner Conference during the Ontario Process and

Automation ShowApril 2009

Page 5: Research and Innovation

Introduction – The PlanIntroduction – The PlanPhase 1: Oct – Dec 08Phase 1: Oct – Dec 08

C. WozneyPaid Researcher

InvestigateHTM

Technology

Students to Work for

Course credit

Phase 2: Jan – May 08Phase 2: Jan – May 08

Create WorkspaceRm. C504A

6 - Student Volunteers (T146)

Plan B

Wozney to managePhase 2

Build the Infrastructure

Collect Data

Simulate Remote

Site Control

Page 6: Research and Innovation

ObjectiveObjective

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

Page 7: Research and Innovation

Research QuestionResearch Question

How can we make a machine intelligent?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

Page 9: Research and Innovation

Research QuestionResearch Question

AnswerAnswer: All of the above are flat: 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?

Page 10: Research and Innovation

Research – The AnswerResearch – 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

Page 11: Research and Innovation

Research – The TechnologyResearch – The Technology

• NuPIC (Numenta Platform for Intelligent Computing)• Vision4 Demo program was designed using HTM networks

What’s this?

Page 12: Research and Innovation

Research – The TechnologyResearch – The Technology

Vision4 Demo program was trained to recognize 4 different images

• Sail Boat

• Rubber Duck

• Cell Phone

• Cow

Page 13: Research and Innovation

Research – The TechnologyResearch – The Technology

Its not perfect but neither are we.

Sailboat ???

Page 14: Research and Innovation

Research – The TechnologyResearch – The Technology

• More detail provides better recognitionIt’s a duck

Page 15: Research and Innovation

Research – The TechnologyResearch – The Technology• HTM is capable of recognizing several variations

Cow in the background

Page 16: Research and Innovation

How far away are we?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)

Page 17: Research and Innovation

Industry Problem 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

Page 18: Research and Innovation

Leo’s ProblemLeo’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

Page 19: Research and Innovation

Methodology – Plan BMethodology – Plan BPlan B – Make sure Plan A worksPlan B – Make sure Plan A works

Build the infrastructure to collect real time data in room C504A Build the infrastructure to collect real time data in room C504A

• Simulate something that is used in industry (remote water Simulate something that is used in industry (remote water pumping station)pumping station)

• Be able to monitor and control the site remotely via the webBe able to monitor and control the site remotely via the web• Use current technologies plus add some extra’sUse 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

Page 20: Research and Innovation

ResultsResults

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

Page 21: Research and Innovation

Infrastructure Testing LabInfrastructure Testing Lab

REMOTE SITE

SCADAControl System

SecuritySystem

Surveillance Cameras

VOIP

Process Cameras

Operator Terminals

Pumping Station

Page 22: Research and Innovation

Lessons LearnedLessons 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

Page 23: Research and Innovation

Lesson LearnedLesson 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

Page 24: Research and Innovation

Future ResearchFuture 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

Page 25: Research and Innovation

QuestionsQuestionsThank you and AcknowledgementsThank 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