Your SlideShare is downloading. ×
0
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
IoT and machine learning - Computational Intelligence conference
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

IoT and machine learning - Computational Intelligence conference

3,503

Published on

Slides for IoT and Machine learning talk. Sign up at Sign up at www.futuretext.com to get forthcoming copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning …

Slides for IoT and Machine learning talk. Sign up at Sign up at www.futuretext.com to get forthcoming copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities

Published in: Data & Analytics
0 Comments
13 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
3,503
On Slideshare
0
From Embeds
0
Number of Embeds
12
Actions
Shares
0
Downloads
151
Comments
0
Likes
13
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Copyright : Futuretext Ltd. London0 Slides for my talk on IoT and Machine Learning Computational Intelligence Unconference UK July 2014 - http://ciunconference.org/uk/2014/schedule.php @ajitjaokar ajit.jaokar@futuretext.com Sign up at www.futuretext.com to get copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities
  • 2. Copyright : Futuretext Ltd. London1 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 : Futuretext Ltd. London2 Ajit Jaokar - www.futuretext.com @AjitJaokar ajit.jaokar@futuretext.com
  • 4. Copyright : Futuretext Ltd. London3 Ajit Jaokar - Data is the new oil ...
  • 5. Copyright : Futuretext Ltd. London4 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 : Futuretext Ltd. London6 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 : Futuretext Ltd. London7 Image source: Guardian Image source: Guardian
  • 9. Copyright : Futuretext Ltd. London8
  • 10. Copyright : Futuretext Ltd. London9 Ajit Jaokar IOT - THE INDUSTRY- STATE OF PLAY
  • 11. Copyright : Futuretext Ltd. London10 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 : Futuretext Ltd. London11 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 : Futuretext Ltd. London12 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 : Futuretext Ltd. London13 Ajit Jaokar Many of the consumer IOT cases will happen with iBeacon in the next two years
  • 15. Copyright : Futuretext Ltd. London14 Ajit Jaokar And 5G will provide the WAN connectivity 5G - Source – Ericsson
  • 16. Copyright : Futuretext Ltd. London15 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 : Futuretext Ltd. London16 Ajit Jaokar Hotspot 2.0
  • 18. Copyright : Futuretext Ltd. London17 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 : Futuretext Ltd. London18 Ajit Jaokar IOT – INTERNET OF THINGS
  • 20. Copyright : Futuretext Ltd. London19 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) Thus, IOT is all about Data .. IoT != M2M (M2M is a subset of IoT)
  • 21. Copyright : Futuretext Ltd. London20 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).
  • 22. Copyright : Futuretext Ltd. London21 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.
  • 23. Copyright : Futuretext Ltd. London22 Ajit Jaokar Machine Learning
  • 24. Copyright : Futuretext Ltd. London23 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. Copyright : Futuretext Ltd. London24 What Problems Can Machine Learning Address? (source Jason Brownlee) ● Spam Detection: ● Credit Card Fraud Detection • Digit Recognition: ● Speech Understanding: ● Face Detection: • Product Recommendation: ● Medical Diagnosis: ● Stock Trading: • Customer Segmentation • Shape Detection .
  • 26. Copyright : Futuretext Ltd. London25 Types of Problems •Classification: Data is labelled meaning it is assigned a class, for example spam/non-spam or fraud/non-fraud. 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 if-then). 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. Copyright : Futuretext Ltd. London26 What Algorithms Does Machine Learning Provide? 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 : Futuretext Ltd. London27 Ajit Jaokar IoT and Machine Learning
  • 29. Copyright : Futuretext Ltd. London 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 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. Copyright : Futuretext Ltd. London29 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
  • 31. Copyright : Futuretext Ltd. London30 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. Copyright : Futuretext Ltd. London33 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
  • 35. Copyright : Futuretext Ltd. London34 Real time (Stream processing)
  • 36. Copyright : Futuretext Ltd. London35
  • 37. Copyright : Futuretext Ltd. London36 Ajit Jaokar - www.futuretext.com @AjitJaokar ajit.jaokar@futuretext.com
  • 38. Copyright : Futuretext Ltd. London37 @ajitjaokar ajit.jaokar@futuretext.com Sign up at www.futuretext.com to get copies of papers on IoT and Machine learning, Real time algorithms for IoT and Machine learning algorithms for Smart cities

×