Download - North americai iotskynet-v2
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THE RISE OF THE MACHINE - IS SKYNET CLOSER THAN EVER?
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JVM Developer DevOps practitionerDeveloper AdvocateRobot BuilderIoT speculator AI explorer @spoole167
Work at IBM’s UK Researchand Development Laboratory
MeansI get to play
with cool stuff
Steve Poole : IBM
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How close are we tobuilding Skynet?
And can anyone do it?
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“Skynet is a fictional neural net-based conscious group mind and artificial general intelligence (see also superintelligence)
system that features centrally in the Terminator franchise and serves as the franchise's main antagonist.”
https://en.wikipedia.org/wiki/Skynet_(Terminator)
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We live in interesting times
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Autonomous Robots
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Meet ‘Handle’ Another Robot from Boston Dynamics
http://www.bostondynamics.com/
Handle is 6.5 feet tall, can jump 4 feet and travels at speeds of up to 9 mph. and can travel up to 15 miles between charges
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Autonomous Cars
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Hyundai - The Empty Car Convoy
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Autonomous Drones
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The test of the world's largest micro-drone swarm in California in October2016 included 103 Perdix micro-dronesmeasuring around six inches launchedfrom three F/A-18 Super Hornet fighterjets,
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Large organisations of devices
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Bot Nets
500,000+ hijacked internet-connected thingslike cameras, lightbulbs, and thermostatslaunched the largest DDoS attack everagainst a top security blogger
IOT
An integrated end-to-end solution that enablesyour apps to communicate with, control, analyze,and update connected electronics
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50 Billion devices connected to the internet by 2020
Wikipedia: “Skynet gained self-awareness after it had spread into millions of computer servers all across the world”
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AI’s that can beat humans at Chess, Go and even Jeopardy
Jan 2016Feb1996 Feb 2011
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Computers we can talk to
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Apple’s Siri Google’s AssistantAmazon’s Alexa
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Computers that can understand the world
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https://github.com/karpathy/neuraltalk2
Dynamic Image Captioning
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Computers that can recognise you
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Q: How long do we have left?In the movie (set in 1984) the first Terminator came from 2024
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A: AI is not scary in the way you might imagine
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The Nature of AI: a worked example
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CanIbuildaterminator?
Mywifesays“no”
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CanIbuildaterminator?
Toolate!
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CanIbuildarobotthatdoessomethingelse?
Naturallymycolleaguessuggestweshouldbuildabeerdeliverysystem
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Terminator4J RobotDelivery4J
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MyRobot
• Needstobeabletodeliveracantoanindividualwithinaspecificamountoftimeiebeforethedrinkgetstoowarm
• Needstotakeverbalinstructionsnokeyboardsthankyou
• Needstobeabletocommunicatewiththetargetindividualie”hereisyourdrink”
• Plusallthesimplestufflikefindingitswayaroundaroom
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MyRobotneeds“senses”
Hear->microphoneSpeak->loudspeakerLocate->position&orientation?Touch->ultra-sonicrangesensor?Vision->cameraTemperature->temperaturesensor?
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Mytoolkit
• Java• OpenCV• Cuda4J• ApacheSpark• DeepLearning4j• Neuroph• Processing• Gazebo
• RaspberryPI• RobotHAT• Robotchassiswithwheels• TiSensorTag• Ultrasonicrangefinder• Webcam• Loudspeaker• Microphone• Batteriesxmany,many
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MyRobot
DistanceSensor
Wheels
TiTag
Camera
Raspberry PIRobot HATBattery
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MyRobot
DistanceSensor
Wheels
TiTag
Camera
Drink
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TiSensorTag
• Bluetoothenabled
• 9axismotionsensor• IRthermopiletemperaturesensor• Digitalmicrophone• Magnetsensor• Humiditysensor• Pressuresensor• Lightsensor
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Howhardcanitbe?
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Somethingsimple– directioncontrol
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LearningwithNeuralNets
• Simpleexample
• Iftargettotheleft- turnleft• Iftargettotheright– turnright• Iftargetstraightahead– moveforwards• Iftargetwithin5cm- stop
Target
DistanceSensor
Wheels
0o
180o
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LeftMotor=backRightMotor=forward
LeftMotor=forwardRightMotor=back
LeftMotor=forwardRightMotor=forward
LeftMotor=stopRightMotorstop
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Psudocode
ifdistancetotarget<=5cm//closeleftmotor=stoprightmotor=stop
elseifangletotarget>355oor<5o //straightaheadleftmotor=forwardrightmotor=forward
else ifangle<180o //ontherightleftmotor=forwardrightmotor=backward
Else//ontheleftleftmotor=backwardrightmotor=forward
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Motordirectionplot(distanceignored)
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
+0.5Forward
-0.5Backwards
Angleoftarget
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0.5 0.7
0.52
ValuesfromNodes
Areweighted0.10.9
summed
=(0.5*0.9)+(0.7*0.1)=0.45+0.07=0.52
Andasignmoidtransferfunctionapplied
1.0./(1.0+exp(-0.52))
0.6271477663131956
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0.5 0.7
0.52
0.1- 0.010.9+0.02
0.6271477663131956
Wetakeexpectedanswer 0.7
Andadjusttheweights
Dependingontheircontribution
=(0.5*0.92)+(0.7*0.09)=0.46+0.063=0.523
0.6278489986434628Andrepeatafewtimes
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Myfirstneuralnet
Inputnode“direction”
Hiddenlayer
Outputmode“rightmotor”
Outputmode“leftmotor”
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Myfirstneuralnet
• Allinputandoutputisbetween0&1• Usesa“sigmoidfunction”asthetrigger• Usesabackpropagationmethod• Usessupervisedtraining.
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Motordirection50sampledata
-0.07
-0.035
0.
0.035
0.07
0.105
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
+0.5Forward
-0.5Backwards
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Motordirection500sampledata
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
+0.5Forwards
-0.5Backwards
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Motordirection5000sampledata
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
-0.5Backwards
+0.5Forwards
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Motordirection50000sampledata
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
-0.5Backwards
+0.5Forwards
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Motordirection500000sampledata
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
-0.5Backwards
+0.5Forwards
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Motordirection5000000sampledata
-0.8
-0.5
-0.3
0.
0.3
0.5
0.8
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
-0.5Backwards
+0.5Forwards
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But..
ErrorsinyourtrainingcancauseerrorsintheAI
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Motordirectionplot(distanceignored)
-0.8
-0.5
-0.3
0.
0.3
0.5
0.8
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
Ihadabuginthetrainingalgorithm
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Motordirection500000sampledata
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
WhichtheNNcopied
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Whataboutotherneuralnetstructures
1x2x2?1x12x2?
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Motordirection500000sampledata1x2x2
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
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Motordirection500000sampledata1x12x2
-0.75
-0.5
-0.25
0.
0.25
0.5
0.75
0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330 345
Left Right
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Trainingcosts
• Morenodes->moretime(noguaranteeofincreasedaccuracy)• Moreaccuracy->moretraining->(noguaranteeofincreasedaccuracy)
• Increasedcomplexityofproblem->morenodes—>moretime->(noguaranteeofincreasedaccuracy)
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We’veseenasimpleNNlearningtheoutputofanalgorithm
• Thealgorithmisasortofmathematicalfunction
• Italready‘knows’howtomaptheinputvaluetosomeoutputvalues
• TheNNistrainedtofindthesamefunctionmappingina3dimensionalspace.
• Somewhereinthespaceof(angle,rightmotor,leftmotor)thereistherightanswer.
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Nowwe’lladdindistance• we’recreatinganewalgorithmthatmapsavalueina4dimensionalspace
• Angle,distance,leftmotor,rightmotor
Distance
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Mysecondneuralnet
Inputnode“distance”
Hiddenlayer
Outputmode“rightmotor”
Outputmode“leftmotor”
Inputnode“direction”
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AIVisualiserScreenshot
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Poorlytrained
Loop 5000 timespick random angleget expected answerget answer from neural netapply positive & negative feedback
Loop throw angles 0..360get expected answerget answer from neural netapply positive & negative feedback
Properlytrained
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Nextsteps.
• Addintheothersensors• Temperature,proximitysensor,acceleration,light,compassdirectionetc
Distance
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I’mgoingtoneedabiggerneuralnet
left motor right motor I’m stuck No drink drink too warmdrink delivered
?
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HowdoIdecideonthestructureofthenetwork?
HowdoItrainthisthing!!!
HowdoIvisualisehowtheAI‘thinks’?
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Training=Simulation+Neuroevolution
• Gazeboforrobotsimulation• UsegeneticalgorithmstorepresentNeuralNetwork.• Definefitnessselectionsandaddincrementally• Fastesttodeliver• Nearesttotarget• abletospotbeingstuck…
• Evolve.• Willletyouknowwhenthisworks.• Unsupervisedtraining- survivalofthefittest
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Terminator4J RobotDelivery4J
• Trainingisstupidlyhard• Ihadn’tappreciatedhowhard.• YouneedsomethinglikeGazebo
• ButitstilltakessignificanttimetotrainandAI• PlusvisualisingtheAIdecisionprocessisdifficultifnotimpossible
http://gazebosim.org/
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Computer Vision : face recognition
• How do you turn a picture of a face into a ‘key’ ?
• Especially when the face is at a different distance or relative angle?
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“face landmark estimation"Convert the image into HOG format
Identify key points around the face.
Train an AI to find those points in an image
Use the located points to morph the face image into a standard form
Invented by Vahid Kazemi and Josephine Sullivan.
http://www.csc.kth.se/~vahidk/papers/KazemiCVPR14.pdf
http://sharky93.github.io/docs/gallery/auto_examples/plot_hog.html
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Once you can identifythe key parts of a face
you can morph the image in other interesting ways
face landmark estimation
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‘Encode” the face • With a standardised face image
• Identify the ‘important’ parts of the face so you can do pattern matching
• The challenge
• We don’t know what the ‘important’ parts are
• The answer
• Use a Neural Network to work it out.
• http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_089.pdf
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VoicetoText
• Humansareverygoodatunderstandingspokenwords:-)• Computershaveahardertime
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http://cmusphinx.sourceforge.net/
Sphinx4
ApureJavaspeechrecognitionlibrary
WatsonSpeechtoText
JavaAPItoonlinespeechrecognitionsolution(freetier)
https://speech-to-text-demo.mybluemix.net/
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Results
“love they do is”
“Open the pod bay doors hal.”
Speaker 0: Open the pod baydoors hal.
Speaker 1: Sorry. Great I can.
“Open the pod bay doors hal. I’m sorry Dave. I can’t do that”
Sphinx4 Watson
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SpeechtoWords
• Accuratetranslationofspeechtowordscanbeverygood• It’salmostasaccurateashumans• Itwillsoonbemuchbetter.• Withoutgoodmicrophonesresultscanbemixed!• Behindthescenesthereisstillasignificantamountofcomputepowerbeingused.
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Practically
• HowwouldIgetmyrobottounderstandcommandslike
• “takethisdrinktoRobinthecorner”• “takethisdrinktotheladytalkingtomywife”• “givethisdrinktothemanwearingtheHawaiianshirtandblackshorts”
• HowdoIextractmeaning?
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ChatBots
User: "Alexa, open History Buff"Alexa: "History Buff. What day do you want events for?"User: "August thirtieth."Alexa: "For August thirtieth, in 2003, [...] . Wanna go deeper in history?"User: "Yes."Alexa: "In 1995, Bosnian war [...] . Wanna go deeper in history?"User: "No."Alexa: "Good bye!"
https://github.com/amzn/alexa-skills-kit-java
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ChatBots:alittlebitofacheatGetFirstEventIntent get events for {day}GetFirstEventIntent give me events for {day}GetFirstEventIntent what happened on {day}GetFirstEventIntent what happenedGetFirstEventIntent {day}
GetNextEventIntent yesGetNextEventIntent yupGetNextEventIntent sureGetNextEventIntent yes please
AMAZON.StopIntent noAMAZON.StopIntent nopeAMAZON.StopIntent no thanksAMAZON.StopIntent no thank you
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Chatbots
• Mapintenttoactions• WorkoutyourintentusingvariouslevelsofNaturalLanguageParsing
• Hardcodedstringmatching• Templates(likeAlexa)• Trainedsentencemapping(likeWatsonConversationService)
• Respondtoyouappropriately• Don’thaveanyintrinsicunderstandingofyourintent(yet)
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Artificial Intelligence
• Relies on ‘good’ data representations
• Relies on appropriate internal data structures
• Relies on the ‘right’ sort of training
• Embeds knowledge inside its data structures
• Very quickly becomes opaque to humans
• Needs significant processing power
This is‘art’ not
science
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GPUstotherescue?• GPU’scanmassivelyreduceneuralnettrainingandexecutiontimes.
• TheyareideallysuitedtoparallelprocessingthecalculationsofaNeuralNet.• Youcanuseyourownorbuytimeinthecloud• ForinstanceAmazonoffersan16xNVIDIAK80GPUinstanceinECSat$14/hour• Youget,nominally,x10performanceimprovements
• MultiplebindingsforGPUsformostlanguages
• IBMJVMhasintrinsicsupportforGPU’s.JITcanutiliseGPUsunderthecoversforappropriateoperations.
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How long do we have left?In the movie (set in 1984) the first Terminator came from 2024
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Checkpoint• Buildingyouowncognitiverobotorserviceishard
• Ittakestime.• Ittakesdata.• Ittakespatience.
•WhatAItechniquesaregivingusistheabilitytominedataeffectivelyandbeabletoteachsystemsto‘understand’thatdata
• It’sclearthatcompaniesarecreatingAI’sthatwillbesignificantassets.• It’salsoclearthatdifferentiatorwillbethequalityandquantityofdatausedtotraintheAI
•Nosignofsentienceyet.
•However…
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• WedesigntheAI• WetraintheAI• Ourdesires/agendas/biasescaneasilygetencodedintotheAI
• Howdowe‘trust’AI’s?
TheSkynetideastillhasanachillesheel…
Don’tworryaboutSkynetyet.
WorryabouthowwelearntounderstandandvisualisetheAIswe’vecreated.
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TheAchillesheelofSkynet
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MyRobotuses6AAbatteries
• Givesitafewhoursofrunning
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HowmanyAAbatterieswouldyouneedtorunaTerminator?
• Here’stheHumanequivalent
• NumberofAAbatteriestoplaythepianoforanhour?
• NumberofAAbatteriestowalkforanhour?
• NumberofAAbatteriestostandupandpaintawallforanhour?
91
127
152
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Yuckyquestion
Howmuchenergy(innumberofAAbatteries)isstoredin1kgofhumanfat?
1869
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Soactuallyhumanbeingsareprettyefficientbatteries?
Maybewe’reinthewrongmovie?
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Thank you
any questions?