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IOT as a metaphor
for
Shape of things to come . . .
PG Madhavan, Ph.D.
Seattle, USA
PG Madhavan - Copyright 2016 1
PG Madhavan: Data Science Activity SUMMARY
2PG Madhavan - Copyright 2016
PG Madhavan: Shaping IoT as the framework for 21st century industry . . .
“DO MORE at HIGHER QUALITY with BETTER UX”
(Incr...
 How to PROACTIVELY address client pain-points.
 Data Science, IoT, Machine Learning, . . . a unified framework.
• How t...
PG Madhavan - Copyright 2016 5
OBJECTIVES Benefits
1. Increase Throughput: “Do more”
2. Continuous Improvement: “Higher qu...
S&P500 SECTORS
COMMON FEATURES:
1. Data generating mechanism
2. IT connectivity and
3. Decision making software
“IoT” as ...
PG Madhavan - Copyright 2016 7
Internet of Things
Engineering
IT
“DO MORE at HIGHER QUALITY with BETTER UX!”
PG Madhavan - Copyright 2016 8
1. Learning in real-time.
• Just like children mature over time . . .
2. As machines age, I...
* Minimum Viable Product
IoT Sales Accelerator
PG Madhavan - Copyright 2016 9
Mission: Develop solutions for
projects that...
 IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science).
 IoT = DO MORE at HIGHER QUALITY with BETTER UX...
PG Madhavan - Copyright 2016 11
IMHO, IoT Data Science revolution will be more like the
“printing press” revolution . . .
...
Getting into the weeds
More details . . .
Dynamical ML, IoT Data Science, applications, algorithm & a roadmap
PG Madhavan ...
13
“Hard Core”:
Machine Learning
• Mapping from input to output.
• Map: Linear or Nonlinear; Static or Dynamic.
• Ontology...
PG Madhavan - Copyright 2016
Machine Learning =
In plain English: What is the likely Class that these
measured Attributes ...
Data Science TYPES
“INDUSTRY” DS “BUSINESS” DS “SOCIAL” DS
Use Case example: AssetManagement
Recommendation
Engine
Natural...
“INDUSTRY” Data Science: IoT e2e platform
Predictive Maintenance
(Paper, Steel, Manufacturing, . . .)
Front-end h/w & DSP
...
17
Retail
example:
ANY
Business:
• Collect
purchase
evidence
• Purchasepropensitycanvary
betweenproduct/service
Categories...
PG Madhavan - Copyright 2016 18
DYNAMICAL Machine Learning: a step-change . . .
Machine Learning TODAY:
• Learn a Static m...
 DYNAMIC Learning when available!
Recurrent Kernel-projection Time-varying Kalman system:
 “RKT-Kalman” or “Rocket” Kalm...
20
DRIVING Data Science Products & Business Solutions Roadmap . . .
GROUP
Recommendation
Engine
1. Retailer & CPG
2. Produ...
New Book & Contact Information
SYSTEMS Analytics: Adaptive Machine Learning
WORKBOOK
https://www.amazon.com/dp/1535541520/...
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IoT as a metaphor!

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IoT as the technology framework that underpins ALL businesses and industries of the 21st century! Role of DYNAMICAL machine learning . . .

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IoT as a metaphor!

  1. 1. IOT as a metaphor for Shape of things to come . . . PG Madhavan, Ph.D. Seattle, USA PG Madhavan - Copyright 2016 1
  2. 2. PG Madhavan: Data Science Activity SUMMARY 2PG Madhavan - Copyright 2016
  3. 3. PG Madhavan: Shaping IoT as the framework for 21st century industry . . . “DO MORE at HIGHER QUALITY with BETTER UX” (Increase throughput; Continuous improvement; Mass customization) Continuous Improvement <requires> Continuous Learning <requires> DYNAMICAL machine learning! DYNAMICAL ML = State-space data model + Real-time Recursive learning algorithms + In-Stream processing IoT Data Science revolution will be more like the “printing press” revolution . . . a disruptive change in problem solving leading to a smarter society.
  4. 4.  How to PROACTIVELY address client pain-points.  Data Science, IoT, Machine Learning, . . . a unified framework. • How to conceptualize and execute. Objective: “DO MORE at HIGHER QUALITY with BETTER UX” PG Madhavan - Copyright 2016 4 IOT as a metaphor for shape of things to come . . .
  5. 5. PG Madhavan - Copyright 2016 5 OBJECTIVES Benefits 1. Increase Throughput: “Do more” 2. Continuous Improvement: “Higher quality” 3. Mass Customization: “Better UX” IoT today:
  6. 6. S&P500 SECTORS COMMON FEATURES: 1. Data generating mechanism 2. IT connectivity and 3. Decision making software “IoT” as a TECHNOLOGY FRAMEWORK that underpins ALL businesses and industries of the 21st century. PG Madhavan - Copyright 2016 6
  7. 7. PG Madhavan - Copyright 2016 7 Internet of Things Engineering IT “DO MORE at HIGHER QUALITY with BETTER UX!”
  8. 8. PG Madhavan - Copyright 2016 8 1. Learning in real-time. • Just like children mature over time . . . 2. As machines age, IoT adapts to NEW normal. • IoT designed for long-term use. 3. Underlying system “states” provide a meta-model. • A more stable description; less False Positives. 4. System “states” as “Digital Twin”! • Continuous “closed-loop” performance improvement. Continuous Improvement <requires> Continuous Learning <requires> DYNAMICAL machine learning!
  9. 9. * Minimum Viable Product IoT Sales Accelerator PG Madhavan - Copyright 2016 9 Mission: Develop solutions for projects that clients anticipate in 6 to 12 months & accelerate Sales of high-value contracts (retain component IPR). † Continuous closed-loop Improvement with DYNAMICAL Machine Learning
  10. 10.  IoT = (Network of Sensors & Devices) + IT + (Engineering Data Science).  IoT = DO MORE at HIGHER QUALITY with BETTER UX.  IoT = Technology framework that underpins ALL businesses and industries of the 21st century. PG Madhavan - Copyright 2016 10 IOT as a metaphor for shape of things to come . . .
  11. 11. PG Madhavan - Copyright 2016 11 IMHO, IoT Data Science revolution will be more like the “printing press” revolution . . . Printing press created a disruptive change in “memorization”; On the printed page, information lasts forever (well, almost). Similarly, IoT Data Science is creating a disruptive change in “intelligence” via augmentation based on data and Math. . . . similar to “evidence-based” medicine vis-a-vis traditional “observational” medicine practice.
  12. 12. Getting into the weeds More details . . . Dynamical ML, IoT Data Science, applications, algorithm & a roadmap PG Madhavan - Copyright 2016 12
  13. 13. 13 “Hard Core”: Machine Learning • Mapping from input to output. • Map: Linear or Nonlinear; Static or Dynamic. • Ontology tree with 3 branches: Bayes Theorem, Cover Theorem & Neuroscience + ad hoc. “The Flesh”: Statistical Design of Experiments • Before: o Data selection – non-homogeneity! o Factorial designs? o Bootstrap? • After: o Validation. o Accuracy, Sensitivity, Specificity, ROC. o Combining results – ensemble methods. Data Science – an applied Science! Applied aspects of . . . 1. Probability Theory: • Bayesian methods (L2 optimal). • . . . . n. Optimization Theory: • L2 optimization by far. • L1 optimization creating buzz . . . PG Madhavan - Copyright 2016 Data Science as an avocado . . .
  14. 14. PG Madhavan - Copyright 2016 Machine Learning = In plain English: What is the likely Class that these measured Attributes belong to? In Probability speak: What is the Conditional Expectation of Class given Attributes? E[ y | x]  “BAYESIAN estimation” From Training Set {y, x}, estimate Joint Density. 14 E[y | x] Bayes’ Theorem Naïve Parzen estimator Neuroscience Perceptron Kohonen, Hopfield Multilayer NN Cover Theorem RBF SVM Kernel regression Deep Learning Bootstrap Consensus  TRAINING Set, {y, x} Hence the need for BIG Data! Machine Learning ONTOLOGY and Unification:
  15. 15. Data Science TYPES “INDUSTRY” DS “BUSINESS” DS “SOCIAL” DS Use Case example: AssetManagement Recommendation Engine Natural Language Processing Time resolution: Near real-time Intermittent Real-time Processing: Signals Transactions Audio/ Visual Learning: Dynamical Static Reinforcement Algorithm example: Kalman Filter Random Forest Deep Neural Network Data Science TYPES . . .  Data Science tuned to each vertical “ENGINEERING” (applied) Data Science or “EDS” PG Madhavan - Copyright 2016 15
  16. 16. “INDUSTRY” Data Science: IoT e2e platform Predictive Maintenance (Paper, Steel, Manufacturing, . . .) Front-end h/w & DSP Security solutions (LLC) Wi-Fi, Networking, Software & Standards Pub-Sub; MQTT Cellular phone chipsets & software GSM standards; MVNO Predictive & Prescriptive Analytics • IoT – Predictive Maintenance. • M2M – elastic middleware. • Smart shopping list on mobile phone. • Group Recommendation Engine & Uplift modeling  Real-time analysis; digital signal processing; recursive estimation; “lambda” architecture PG’s work PG Madhavan - Copyright 2016 16
  17. 17. 17 Retail example: ANY Business: • Collect purchase evidence • Purchasepropensitycanvary betweenproduct/service Categories • Segment WITHIN Categoriesby purchase evidence & not by universalbehaviorbuckets • UNMET DemandAnalytics • Knowmoreaboutwhat a customer needs& wantsthaneventheydo. . .! • Provide product/servicewhenandwhere needed • Target “Persuadables” • IncreaseConversion probabilityandlower overall customer engagement expense. BIG Data Know thy END customer! Find product/service variety that the customer will want TARGET consumers/clients rather than “spray & pray”! Customer RETENTION Customer ACQUISITION PG Madhavan - Copyright 2016 “BUSINESS” Data Science: Framework for ANY for product or service business
  18. 18. PG Madhavan - Copyright 2016 18 DYNAMICAL Machine Learning: a step-change . . . Machine Learning TODAY: • Learn a Static mapping between inputs and outputs using Training Set. • Then move this map into “production”.  Implicit Assumption: Relationship between the input & output (= the underlying “system”) remains unchanged in the future  STATIC! DYNAMICAL ML = State-space data model + Real-time Recursive learning algorithms + In-Stream processing
  19. 19.  DYNAMIC Learning when available! Recurrent Kernel-projection Time-varying Kalman system:  “RKT-Kalman” or “Rocket” Kalman! Generalized Dynamical Machine Learning K-SMOOTHER Offline operation: K-PREDICTOR K-FILTER K-FORECASTER STATES CLASS LABEL PREDICTION DELAY  “Lambda” architecture  Apache Flink PG Madhavan - Copyright 2016 19
  20. 20. 20 DRIVING Data Science Products & Business Solutions Roadmap . . . GROUP Recommendation Engine 1. Retailer & CPG 2. Product Assortment . 3. E-commerce warehouse. 4. Ad buy mix. 5. . . . Models ad hoc State-space Models Network Models Spatio-temporal Models 2015 2016 2017 2018 Closed-loop & Real-time 1. IoT 2. Retail business solutions. 3. Mobile management. 4. Machine monitoring. 5. Fraud detection. 6. Payment monitoring. 7. CRM. 8. Gaming. 9. JIT marketing. 10. . . . Network flow 1. Viral marketing 2. Infection spread monitoring. 3. Health monitoring. 4. “Process control”. 5. . . . Global effects 1. Large scale effects. 2. Dynamic flow control. 3. . . . PG Madhavan - Copyright 2016
  21. 21. New Book & Contact Information SYSTEMS Analytics: Adaptive Machine Learning WORKBOOK https://www.amazon.com/dp/1535541520/ 21 +1-425-440-1487 pgmad@live.com PG Madhavan - Copyright 2016

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