Io t and machine learning smart cities
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Io t and machine learning smart cities

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my presentation for IoT and Machine Learning for Smart cities

my presentation for IoT and Machine Learning for Smart cities

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  • 1. Copyright : 0 Futuretext Ltd. London Future trends IoT and Machine Learning @ajitjaokar ajit.jaokar@futuretext.com
  • 2. Copyright : 1 Futuretext Ltd. London Ajit Jaokar - Machine Learning for IoT and Telecoms futuretext applies machine learning techniques to complex problems in the IoT (Internet of Things) and Telecoms domains. We aim to provide a distinct competitive advantage to our customers through application of machine learning techniques Philosophy: Think of NEST. NEST has no interface. It’s interface is based on ‘machine learning’ i.e. it learns and becomes better with use. This will be common with ALL products and will determine the competitive advantage of companies. Its a winner takes all game! Every product will have a ‘self learning’ interface/component and the product which learns best will win!
  • 3. Copyright : 2 Futuretext Ltd. London Ajit Jaokar - www.futuretext.com @AjitJaokar ajit.jaokar@futuretext.com
  • 4. Copyright : 3 Futuretext Ltd. London Ajit Jaokar - Data is the new oil ...
  • 5. Copyright : 4 Futuretext Ltd. London Ajit Jaokar - The meek shall inherit the earth .. BUT not it’s 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!)
  • 6. Copyright : Futuretext Ltd. London Ajit Jaokar - Source: MIT / Smithsonian http://www.smithsonianmag.com/innovation/better-traffic-light-timing-will-get-you- there-faster-180952123/ a) State of Play b) IoT c) Machine Learning d) What is unique with IoT and Machine Learning
  • 7. Copyright : 6 Futuretext Ltd. London Ajit Jaokar - www.opengardensblog.futuretext.com World Economic Forum Spoken at MWC(5 times), CEBIT, CTIA, Web 2.0, CNN, BBC, Oxford Uni, Uni St Gallen, European Parliament. @feynlabs – teaching kids Computer Science. Adivsory – Connected Liverpool
  • 8. Copyright : 7 Futuretext Ltd. London Image source: Guardian Image source: Guardian
  • 9. Copyright : 8 Futuretext Ltd. London
  • 10. Copyright : 9 Futuretext Ltd. London Ajit Jaokar IOT - THE INDUSTRY- STATE OF PLAY
  • 11. Copyright : 10 Futuretext Ltd. London Ajit Jaokar State of play - 2014 • Our industry is exciting – but mature - Now a two horse race for devices 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 of Mobile, kickstarter, health apps and iBeacon and ofcourse NEST(acquired by Google)
  • 12. Copyright : 11 Futuretext Ltd. London Ajit Jaokar Stage One: Early innovation 1999 - 2007 Regulatory innovation – net neutrality - Device innovation (Nokia 7110 and Ericsson t68i) - Operator innovation (pricing, bundling, Enterprise) - Connectivity innovation (SMS, BBM) Content innovation (ringtones, games, EMS, MMS) - Ecosystem innovation (iPhone) Stage two: Ecosystem innovation - iPhone and Android (2007 – 2010) Social innovation - Platform innovation - Community innovation - Long tail innovation - Application innovation
  • 13. Copyright : 12 Futuretext Ltd. London Ajit Jaokar Phase three: Market consolidation – 2010 - 2013 And then there were two ... Platform innovation and consolidation Security innovation App innovation Phase four – three dimensions – 2014 .. Horizontal apps (iPhone and Android) Vertical (across the stack) – hardware, security, Data Network – 5G and pricing
  • 14. Copyright : 13 Futuretext Ltd. London Ajit Jaokar Many of the consumer IOT cases will happen with iBeacon in the next two years
  • 15. Copyright : 14 Futuretext Ltd. London Ajit Jaokar And 5G will provide the WAN connectivity 5G - Source – Ericsson
  • 16. Copyright : 15 Futuretext Ltd. London 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
  • 17. Copyright : 16 Futuretext Ltd. London Ajit Jaokar Hotspot 2.0
  • 18. Copyright : 17 Futuretext Ltd. London Ajit Jaokar Three parallel ecosystems IOT is connecting things to the Internet – which is not the same as connecting things to the cellular network! The difference is money .. and customers realise it IOT local/personal (iBeacon, Kickstarter, Health apps) M2M – Machine 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
  • 19. Copyright : 18 Futuretext Ltd. London Ajit Jaokar IOT – INTERNET OF THINGS
  • 20. As the term Internet of Things implies (IOT) – IOT is about Smart objects For 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 occupied Now, if it is able to know who is sitting – it could co-relate more data by connecting to the person’s profile If it is in a cafe, whole new data sets can be co-related (about the venue, about who else is there etc) Copyright : 19 Futuretext Ltd. London Thus, IOT is all about Data .. IoT != M2M (M2M is a subset of IoT)
  • 21. Sensors lead to a LOT of Data (relative to mobile) .. (source David wood blog) By 2020, we are expected to have 50 billion connected devices To put in context: The first commercial citywide cellular network was launched in Japan by NTT in 1979 The milestone of 1 billion mobile phone connections was reached in 2002 The 2 billion mobile phone connections milestone was reached in 2005 The 3 billion mobile phone connections milestone was reached in 2007 The 4 billion mobile phone connections milestone was reached in February 2009. Gartner: IoT will unearth more than $1.9 trillion in revenue before 2020; Cisco thinks there will be upwards of 50 billion connected devices by the same date; IDC estimates technology and services revenue will grow worldwide to $7.3 trillion by 2017 (up from $4.8 trillion in 2012). Copyright : 20 Futuretext Ltd. London
  • 22. 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 world’s population will be living in towns and cities. By 2030 this number will swell to almost 5 billion, with urban growth concentrated in Africa and Asia with many mega-cities(10 million + inhabitants). By 2050, 70% of humanity will live in cities. That’s a profound change and will lead to a different management approach than what is possible today Also, 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 of GDP. Copyright : 21 Futuretext Ltd. London
  • 23. Copyright : 22 Futuretext Ltd. London Ajit Jaokar Machine Learning
  • 24. Copyright : 23 Futuretext Ltd. London What is Machine Learning? Mitchell's Machine Learning Tom Mitchell in his book Machine Learning “The field of machine learning is c oncerned with the question of how to construct computer programs 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 at tasks 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) and How we will evaluate its results (P). A programmers perspective: Machine Learning involves: a) Training of a model from data b) Predicts/ Extrapolates a decision c) Against a performance measure.
  • 25. What Problems Can Machine Learning Address? (source Jason Brownlee) Copyright : 24 Futuretext Ltd. London ● Spam Detection: ● Credit Card Fraud Detection • Digit Recognition: ● Speech Understanding: ● Face Detection: • Product Recommendation: ● Medical Diagnosis: ● Stock Trading: • Customer Segmentation • Shape Detection .
  • 26. Copyright : 25 Futuretext Ltd. London Types of Problems •Classification: Data is labelled meaning it is assigned a class, for example spam/nonspam or fraud/nonfraud. The decision being modelled is to assign labels to new unlabelled pieces of data. This can be thought of as a discrimination problem, modelling the differences or similarities between groups. •Regression: Data is labelled with a real value rather than a label. Examples that are easy to understand are time series data like the price of a stock over time. The decision being modelled is the relationships between inputs and outputs. Clustering: Data is not labelled, but can be divided into groups based on similarity 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 of propositional rules (antecedent/consequent or ifthen). Often necessary to work backwards from a Problem to the algorithm and then work with Data. Hence, you need a depth of domain experience and also algorithm experience
  • 27. What Algorithms Does Machine Learning Provide? Copyright : 26 Futuretext Ltd. London Regression Instance-based Methods Decision Tree Learning Bayesian Kernel Methods Clustering methods Association Rule Learning Artificial Neural Networks Deep Learning Dimensionality Reduction Ensemble Methods
  • 28. Copyright : 27 Futuretext Ltd. London Ajit Jaokar IoT and Machine Learning
  • 29. Basic idea of machine learning is to build a mathematical model based on training data(learning stage) – predict results for new data(prediction stage) and tweak the model based on new conditions What type of model? Predicitive, Classification, Clustering, Decision Copyright : Futuretext Ltd. London Oriented, Associative IoT and Machine Learning  On one hand - IoT creates a lot of contextual data which complements existing processes  On the other hand – the 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) 28
  • 30. IoT : from traditional computing to .. Gone from making Smart things smarter(traditional computing) to a) Making dumb things smarter .. and b) living things more robust 3 Domains: Consumer, Enterprise, Public infrastructure 1) Consumer – bio sensors(real time tracking), Quantified self – focussing on benefits 2) Enterprise – Complex machinery (preventative maintenance), asset efficiency – reducing assets, increasing efficiency of existing assets. More from transactions to relationships(real time context awareness). 3) Public infrastructure(Dynamically adjust traffic lights). Dis-economies of scale(bad things also scale in cities) – Thanks John Hagel III Copyright : 29 Futuretext Ltd. London
  • 31. Copyright : 30 Futuretext Ltd. London Three key areas: a) Move from exception handling to patterns of exceptions over time.(are some exceptions occurring repeatedly? Do I need to redsign my product, Is that a new product?) – b) Move from optimization to disruption – ownership to rental ship (Where are all these 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
  • 32. Copyright : Futuretext Ltd. London Sensor fusion  Sensor fusion is the combining of sensory data or data derived from sensory data from disparate sources such that the resulting information is 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/ 31
  • 33. Copyright : Futuretext Ltd. London Deep learning  Google's acquisition of DeepMind Technologies  In 2011, Stanford computer science professor Andrew Ng founded Google’s Google Brain project, which created a neural network trained with deep learning algorithms, which famously proved capable of recognizing high level concepts, such as cats, after watching just YouTube videos--and without ever having been told what a “cat” is.  A smart-object recognition algorithm that doesn’t need humans http://www.kurzweilai.net/a-smart-object-recognition-algorithm-that-doesnt-need-humans A feature construction method for general object recognition (Kirt Lillywhite, Dah-JyeLee n, BeauTippetts, JamesArchibald) 32
  • 34. Real time: Beyond ‘Hadoop’ (non hadoopable) the BDAS stack Copyright : 33 Futuretext Ltd. London 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
  • 35. Copyright : 34 Futuretext Ltd. London Real time (Stream processing)
  • 36. Copyright : 35 Futuretext Ltd. London
  • 37. http://jvns.ca/blog/2014/06/19/machine-learning-isnt-kaggle-competitions/ Machine learning isn't Kaggle competitions Kaggle(a site where you compete to solve machine learning problems) Understand the business problem If you want to predict flight arrival times, what are you really trying to do? Some possible options: • Help the airline understand which flights are likely to be delayed, so they Copyright : 36 Futuretext Ltd. London can fix it. • Help people buy flights that are less likely to be delayed. • Warn people if their flight tomorrow is going to be delayed
  • 38. • How accurate does my model really need to be? What kind of false Copyright : 37 Futuretext Ltd. London positive rate is acceptable? • What data can I use? If you’re predicting flight days tomorrow, you can look at weather data, but if someone is buying a flight a month from now then you’ll have no clue. Choose a metric to optimize Let’s take our flight delays example. We first have to decide whether to do classification (“will this flight be delayed for at least an hour”) or regression (“how long will this flight be delayed for?”).
  • 39. Decide what data to use Let’s say I already have the airline, the flight number, departure airport, plane model, and the departure and arrival times. Should I try to buy more specific information about the different plane models (age, what parts are in them..)? Really accurate weather data? The amount of information available to you isn’t fixed! You can get more! Clean up your data Once you have data, your data will be a mess. In this flight search example, there will likely be: airports that are inconsistently named - missing delay information all over the place - weird date formats - trouble reconciling weather data and airport location Copyright : 38 Futuretext Ltd. London
  • 40. Clean up your data Once you have data, your data will be a mess. In this flight search example, there will likely be • airports that are inconsistently named • missing delay information all over the place • weird date formats • trouble reconciling weather data and airport location Copyright : 39 Futuretext Ltd. London Build a model! Put your model into production Measure your model’s performance
  • 41. Copyright : Futuretext Ltd. London Ajit Jaokar - Source: MIT / Smithsonian http://www.smithsonianmag.com/innovation/better-traffic-light-timing-will-get-you- there-faster-180952123/ “Standard” optimization approaches minimize costs while meeting demand • Additional environmental objectives – Minimize carbon footprint – Meet pollution reduction targets • Additional challenge – capturing uncertainty, such as: Population growth and urban dynamics – Rainfall – Renewable energy sources – Energy costs Types of decisions: • The routes to create or expand • The combination of transport modes • The capacity of each route “How sensitive is the investment plan to Population growth (“How will a 10% increase in population affect our carbon footprint?”)?”
  • 42. Copyright : 41 Futuretext Ltd. London Ajit Jaokar - www.futuretext.com @AjitJaokar ajit.jaokar@futuretext.com