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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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  • Research and InnovationMachine Intelligence: An Investigation in the Application of Hierarchical Temporal MemoryL. Salemi, Professor Centre for Construction and Engineering TechnologiesMay 2009

  • IntroductionProject was approved in Oct. 2008Seed Funding - $6,250OCE Connections Grant - $3,000

    Student participation from 3 programsT146 Electro-Mechanical Engineering TechnicianT147 Computer Systems Technology T121 Mechanical Engineering Technology - Design

  • Introduction The Team Research AssistantsSectionStatusClayton WozneyT121Paid ResearcherSteven IrwinT146Course CreditMichael JoyetteT146Course CreditOlek KushnarenkoT146VolunteerScott VannanT146VolunteerTerence D'CunhaT147Course CreditAvinash SinghT147Course CreditIntiaz Abdulla T147 Course Credit

    Volunteers - Albert So, Bruno DAgostino

  • Introduction Industry InvolvementCompany Status

    Industrial Technical ServicesIn Kind Sponsor Reynold RamdialAmit Setti Technical SupportGrace Instrumentation & ControlsEquipment Donation Terry Grace

    Hoskin ScientificMarc DeGrace Technical Advisor

    Hatch EngineeringTechnical AdvisorDennis PhairEquipment Donation

    ISA Toronto SectionPresented at the ISA TechnicalCurrie Gardner Conference during the Ontario Process and Automation ShowApril 2009

  • Introduction The PlanPhase 1: Oct Dec 08C. WozneyPaid ResearcherInvestigateHTMTechnologyStudents to Work for Course credit Phase 2: Jan May 08Create WorkspaceRm. C504A6 - Student Volunteers (T146)Plan BWozney to managePhase 2

    Build the InfrastructureCollect DataSimulate Remote Site Control

  • ObjectiveApply Intelligence to Building Automation ApplicationsUse one of the classrooms to collect dataHVAC (Heating, Ventilation, and Air Conditioning)Lighting (Occupancy based)Security (Access control, intrusion)Security CamerasIncorporate intelligence to Turn off the lights when no one is in the roomLower the temperatureMonitor room occupancy

  • Research QuestionHow can we make a machine intelligent?But first, what is intelligence? Human Intelligence Machine Intelligence Artificial Intelligence Military IntelligenceThere is no universal definition

  • Research The Challenge Intelligence Test: Which one is flat?

  • Research QuestionAnswer: All of the above are flatDoes intelligence lie in the senses of the beholder? Yes/No?

    Our 5 primary sensors provide an abundance of dataOur intelligence forms the conclusion (BELIEF)Where is this intelligence located and how can we make a machine do it?

  • Research The AnswerHierarchal Temporal Memory (HTM)Developed by Jeff Hawkins founder of Numenta and inventor of palm pilot & treoHTM is modeled after the neocortexData is fed to neuron-like networks that learn to recognize patterns and sequences that change over timeWhen presented with new data the HTM is good at predicting what it iswww.numenta.com Book: On Intelligence

  • Research The TechnologyNuPIC (Numenta Platform for Intelligent Computing)Vision4 Demo program was designed using HTM networksWhats this?

  • Research The TechnologyVision4 Demo program was trained to recognize 4 different images Sail BoatRubber DuckCell PhoneCow

  • Research The TechnologyIts not perfect but neither are we.Sailboat ???

  • Research The TechnologyMore detail provides better recognitionIts a duck

  • Research The TechnologyHTM is capable of recognizing several variationsCow 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 difficultMany smart systems already out thereHTM was beyond our scope now what?HTM would be hard to sell to industry partners without something to demo

  • Leos ProblemEngage students and comply with course outlines (course credit for research work)Build something that we could demonstrate to attract industry partnersNo Clayton No HTMGo with Plan B

  • Methodology Plan BPlan 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 webUse current technologies plus add some extrasSCADA (Supervisory Control And Data Acquisition)Security Alarm and Video SurveillanceProcess Cameras for operatorsFull network integration for each subsystemIncorporate intelligence between all of the subsystems

  • ResultsRemote monitoring & control of pumping stationOperator has full control of station (typical)Process cameras allow operators to view the station as if they were presentSurveillance Alarm & Cameras connected via the web and VOIP system (24/7 monitoring station)SCADA system used to the control processMore features to be added

  • Infrastructure Testing Lab REMOTE SITESCADAControl SystemSecuritySystemSurveillance CamerasVOIPProcess CamerasOperator TerminalsPumping Station

  • Lessons LearnedBenefits gainedExcellent learning experience for students and professor Infrastructure Testing Lab a place for us to work and others to utilizeOpportunity to learn new technologies and add to curriculumInterdivisional co-operation Industry Partners

  • Lesson LearnedBumps along the wayHard 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 ResearchFull integration of sub-systems using an OPC data managerTrain the HTM using the remote site dataWork with Video AnalyticsDesign and build sensors that are HTM-readyAttracted an industry sponsor who is interested in using solar power in a remote site application

  • QuestionsThank you and AcknowledgementsMeadow Larkins and the ARI teamThe student research teamReynold Ramdial and Amit Setti from Industrial Technical ServicesMembers of the technical advisory committeeJeff Litwin for supporting our effortsISA Toronto for allowing us to present at their technical conference in April

    Phase 1 Oct. Dec 2008 Clayton Wozney served as paid researcher to investigate the application of HTM in industrial applicationsStudent volunteers helped out in cleaning out storage room for the ITLPhase 2 Jan May

    The project was divided in 2 partsThe research team consisted of students from 3 groups

    Investigate how to incorporate machine intelligence into building and automation applications

    There is no universal consensus that defines intelligence

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