iot and machine learning ciuf ajit jaokar

8/12/2019 IoT and Machine Learning CIUF Ajit Jaokar

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Copyright : Futuretext Ltd. London0

Slides for my talk on IoT and Machine LearningComputational Intelligence Unconference UK July 2014 -

http://ciunconference.org/uk/2014/schedule.php@ajitjaokar

[email protected]

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Machine learning, Real time algorithms for IoT and Machinelearning algorithms for Smart cities
http://ciunconference.org/uk/2014/schedule.phpmailto:[email protected]://www.futuretext.com/http://www.futuretext.com/mailto:[email protected]://ciunconference.org/uk/2014/schedule.php


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Ajit Jaokar

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Machine Learning for IoT and Telecomsfuturetext applies machine learning techniques to complex problems in theIoT (Internet of Things) and Telecoms domains.

We aim to provide a distinct competitive advantage to our customers throughapplication of machine learning techniques

Philosophy:Think of NEST. NEST has no interface. Itsinterface is based on machine learning i.e. it

learns and becomes better with use. This will becommon with ALL products and will determinethe competitive advantage of companies. Its awinner takes all game! Every product will have aself learning interface/component and the

product which learns best will win!


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Ajit Jaokar

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[email protected]@futuretext.com
http://www.futuretext.com/mailto:[email protected]:[email protected]://www.futuretext.com/


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Ajit Jaokar

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Data is the new oil ...


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Ajit Jaokar

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The meek shall inherit the earth .. BUT not its mineral rights!Data is out there and is free (Open data). It provides no competitive advantages.Finding patterns in data is the holy grail (the oil!)


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Ajit Jaokar

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www.opengardensblog.futuretext.com

World Economic ForumSpoken at MWC(5 times), CEBIT, CTIA, Web 2.0,CNN, BBC, Oxford Uni, Uni St Gallen, EuropeanParliament. @feynlabs teaching kids ComputerScience. Adivsory Connected Liverpool
http://www.opengardensblog.futuretext.com/http://www.opengardensblog.futuretext.com/


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Image source: Guardian

Image source: Guardian
http://www.opengardensblog.futuretext.com/wp-content/uploads/2012/08/smart-cities-and-demand-for-data-scientists.jpghttp://www.opengardensblog.futuretext.com/wp-content/uploads/2012/08/smart-cities-and-demand-for-data-scientists.jpg


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Ajit Jaokar

IOT - THE INDUSTRY- STATE OF PLAY


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Ajit Jaokar

State of play - 2014 Our industry is exciting but mature - Now a two horse race fordevices with Samsung around 70% of Android Spectrum allocations and G cycles are predictable - 5G around 2020 50 billion connected devices by 2020 ITU world Radio communications Conference, November 2015. IOT has taken off .. not because of EU and Corp efforts but because ofMobile, kickstarter, health apps and iBeacon and ofcourse NEST(acquiredby Google)


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Ajit Jaokar

Stage One: Early innovation 1999 - 2007

Regulatory innovationnet neutrality - Device innovation (Nokia7110 and Ericsson t68i) - Operator innovation (pricing, bundling,Enterprise) - Connectivity innovation (SMS, BBM)Content innovation (ringtones, games, EMS, MMS) - Ecosysteminnovation (iPhone)

Stage two: Ecosystem innovation - iPhone and

Android (20072010)

Social innovation - Platform innovation - Communityinnovation - Long tail innovation - Applicationinnovation


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Ajit Jaokar

Phase three: Market consolidation2010 - 2013

And then there were two ...Platform innovation and consolidationSecurity innovation

App innovation

Phase fourthree dimensions2014 ..Horizontal apps (iPhone and Android)Vertical (across the stack)hardware, security, DataNetwork5G and pricing


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Ajit Jaokar

Many of the consumer IOT cases will happen with iBeacon in the nexttwo years


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Ajit Jaokar

And 5G will provide the WAN connectivity 5G - Source Ericsson


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Ajit Jaokar

Samsung Gear Fit named Best Mobile Device of Mobile World

Congress

Notification or Quantification? Displays (LED, e-paper,Mirasol, OLED and LCD) - Touchscreen or hardware controls? -

Battery life and charging
http://androidandme.com/2014/03/news/samsung-gear-fit-named-best-mobile-device-of-mobile-world-congress/http://androidandme.com/2014/01/sponsored/whats-next-in-wearable-tech/


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Ajit Jaokar

Hotspot 2.0


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Ajit Jaokar

Three parallel ecosystemsIOT is connecting things to the Internet which is not the same asconnecting things to the cellular network!The difference is money .. and customers realise it

IOT local/personal(iBeacon, Kickstarter, Health apps)

M2MMachine to Machine

IOT pervasive(5G, Hotspot 2.0)

Perspectives 2014 2015(radio conf) 2020(5G, 2020) 2014 iBeacon (motivate retailers to open WiFi) Hotspot 2.0 connect cellular and wifi worlds

Default wifi and local world? Operator world (Big)Data, Corporate, pervasive apps really happen

beyond 2020 So 5G will be timed well. The ecosystems will develop and they will be

connected by 5G


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Ajit Jaokar

IOT INTERNET OF THINGS


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As the term Internet of Things implies (IOT) IOT is about Smart

objectsFor an object (say a chair) to be smart it must have three things- An Identity (to be uniquely identifiable via iPv6)- A communication mechanism(i.e. a radio) and- A set of sensors / actuators

For example

the chair may have a pressure sensor indicating that it is occupiedNow, if it is able to know who is sitting it could co-relate more data byconnecting to the persons profileIf it is in a cafe, whole new data sets can be co-related (about the venue,about who else is there etc)

Thus, IOT is all about Data ..

IoT != M2M (M2M is a subset of IoT)


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Sensors lead to a LOT of Data (relative to mobile) .. (source David

wood blog)

By 2020, we are expected to have 50 billion connected devicesTo put in context:The first commercial citywide cellular network was launched in Japan by NTTin 1979The milestone of 1 billionmobile phone connections was reached in 2002

The 2 billionmobile phone connections milestone was reached in 2005

The 3 billionmobile phone connections milestone was reached in 2007

The 4 billionmobile phone connections milestone was reached in February

2009.

Gartner: IoT will unearth more than $1.9 trillionin revenue before 2020; Cisco thinksthere will be upwards of50 billion connected devicesby the same date; IDC estimatestechnology and services revenue will grow worldwide to$7.3 trillion by 2017(upfrom$4.8 trillion in 2012).
http://www.gartner.com/newsroom/id/2636073http://share.cisco.com/internet-of-things.htmlhttp://www.idc.com/getdoc.jsp?containerId=prUS24671614http://www.idc.com/getdoc.jsp?containerId=prUS24366813http://www.idc.com/getdoc.jsp?containerId=prUS24366813http://www.idc.com/getdoc.jsp?containerId=prUS24671614http://share.cisco.com/internet-of-things.htmlhttp://www.gartner.com/newsroom/id/2636073


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So, 50 billion by 2020 is a large number

Smart cities can be seen as an application domain of IOT

In 2008, for the first time in history, more than half of the worldspopulation will be living in towns and cities.By 2030 this number will swell to almost 5 billion, with urban growthconcentrated in Africa and Asia with many mega-cities(10 million +

inhabitants).By 2050, 70% of humanity will live in cities.

Thats a profound change and will lead to a different management approachthan what is possible todayAlso, economic wealth of a nation could be seen as Energy +Entrepreneurship + Connectivity (sensor level + network level +

application level)Hence, if IOT is seen as a part of a network, then it is a core component ofGDP.


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Ajit Jaokar

Machine Learning


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What is Machine Learning?

Mitchell's Machine LearningTom Mitchell in his book Machine Learning Thefield of machine learning is concerned with the question of how to construct computerprograms that automatically improve with experience.

formally:A computer program is said to learn from experience E with respect to

some class of tasks T and performance measure P, if its performance attasks in T, as measured by P, improves with experience E.

Think of it as a design tool where we need to understand:What data to collect for the experience (E)What decisions the software needs to make (T) andHow we will evaluate its results (P).

A programmers perspective:Machine Learning involves:a) Training of a model from datab) Predicts/ Extrapolates a decisionc) Against a performance measure.


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What Problems Can Machine Learning Address? (source JasonBrownlee)

Spam Detection: Credit Card Fraud Detection Digit Recognition: Speech Understanding: Face Detection: Product Recommendation:

Medical Diagnosis: Stock Trading: Customer Segmentation

Shape Detection.


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Types of Problems

Classification: Data is labelled meaning it is assigned a class, for examplespam/nonspam or fraud/nonfraud. The decision being modelled is toassign labels to new unlabelled pieces of data. This can be thought of as adiscrimination problem, modelling the differences or similarities between groups.

Regression:Data is labelled with a real value ratherthan a label. Examples that are easy to understand are time series data like the price ofa stock over time. The decision being modelled is the relationships betweeninputs and outputs.

Clustering: Data is not labelled, but can be divided into groups based onsimilarity and other measures of natural structure in the data.

An example from the above list would be organising pictures by faces without

names, where the human user has to assign names to groups, like iPhoto on the Mac.

Rule Extraction:Data is used as the basis for the extraction ofpropositional rules (antecedent/consequent or ifthen).Often necessary to work backwards from a Problem to the algorithm and then work withData. Hence, you need a depth of domain experience and also algorithm experience


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What Algorithms Does Machine Learning Provide?

RegressionInstance-based Methods

Decision Tree Learning

Bayesian

Kernel Methods

Clustering methods

Association Rule LearningArtificial Neural Networks

Deep Learning

Dimensionality Reduction

Ensemble Methods


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Ajit Jaokar

IoT and Machine Learning


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Copyright : Futuretext Ltd. London

Basic idea of machine learningis to build a mathematical model based on

training data(learning stage) predict results for new data(predictionstage) and tweak the model based on new conditions

What type of model? Predicitive, Classification, Clustering, Decision

Oriented, Associative

IoT and Machine Learning

On one hand - IoT creates a lot of contextual data which complements existingprocesses

On the other handthe Sheer scale of IoT calls for unique solutions

Types of problems:

Apply existing Machine Learning algorithms to IoT data

Use IoT data to complement existing processes Use the scale of IoT data to gain new insights

Consider some unique characteristics of IoT data (ex streaming)

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IoT : from traditional computing to ..

Gone from making Smart things smarter(traditional computing) to

a) Making dumb things smarter .. andb) living things more robust

3 Domains:

Consumer, Enterprise, Public infrastructure

1) Consumerbio sensors(real time tracking), Quantified selffocussing onbenefits

2) EnterpriseComplex machinery (preventative maintenance), asset efficiencyreducing assets, increasing efficiency of existing assets. More from transactions torelationships(real time context awareness).

3) Public infrastructure(Dynamically adjust traffic lights). Dis-economies ofscale(bad things also scale in cities)Thanks John Hagel III


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Three key areas:

a) Move from exception handling to patterns of exceptions over time.(aresome exceptions occurring repeatedly? Do I need to redsign my product, Is that anew product?)

b) Move from optimization to disruptionownership to rental ship (Where are allthese dynamic assets?)

c) Move to self learning: Robotics: From assembly line to self learning

robots(Boston Dynamics), autonomous helicopters

Four examples of differences:Sensor fusion - Deep Learning - Real time - Streaming


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Sensor fusion

Sensor fusionis the combining of sensory data or data derived from

sensory data from disparate sources such that the resulting informationis in some sense better than would be possible when these sources were

used individually. The data sources for a fusion process are not specified

to originate from identical sensors. Sensor fusion is a term that covers a

number of methods and algorithms, including: Central Limit Theorem,

Kalman filter, Bayesian networks, Dempster-Shafer

Example: http://www.camgian.com/http://www.egburt.com/

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http://www.camgian.com/http://www.egburt.com/http://www.egburt.com/http://www.egburt.com/http://www.camgian.com/http://www.camgian.com/http://www.camgian.com/


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Deep learning

Google's acquisition ofDeepMind Technologies

In 2011, Stanford computer science professor Andrew Ng foundedGoogles Google Brain project, which created a neural network trainedwith deep learning algorithms, which famously proved capableofrecognizing high level concepts, such as cats, after watching justYouTube videos--and without ever having been told what a cat is.

A smart-object recognition algorithm that doesnt need humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humansA feature construction method for general object recognition (Kirt Lillywhite,Dah-JyeLee n, BeauTippetts, JamesArchibald)

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http://deepmind.com/http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?_r=0http://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humanshttp://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?_r=0http://deepmind.com/


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Real time:Beyond Hadoop (non hadoopable) the BDAS stack

BDAS Berkeley data analytics stack

Spark an open source, in-memory, cluster computing framework.Integrated with Hadoop(can work with files stored in HDFS)Written in Scala


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Real time (Stream processing)


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Ajit Jaokar

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[email protected]@futuretext.com
http://www.futuretext.com/mailto:[email protected]:[email protected]://www.futuretext.com/


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@[email protected]

Sign up at www.futuretext.comto get copies of papers on IoT andMachine learning, Real time algorithms for IoT and Machine

learning algorithms for Smart cities
mailto:[email protected]://www.futuretext.com/http://www.futuretext.com/mailto:[email protected]

iot and machine learning ciuf ajit jaokar

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