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When Data Meets Device

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The algorithms being used in machine learning are not actually new; they're decades old, and many of them were first used in problems in systems engineering. As early as the 1990s, researchers realized that the field of AI was studying the same concepts with different terminology, but for a variety of factors it was the AI space that found the most success. Nevertheless, we're coming back full circle as we see the integration of data, software, and physical equipment starting to blend together. What comes next in the world of data? And how can we learn from the technology of the past?

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When Data Meets Device

  1. 1. Emily Gorcenski When Data Meets Device Looking forward to a data-driven physical world
  2. 2. A Brief History of Data Science
  3. 3. Which came first? Take a moment to mentally order these chronologically A. David Hasselhoff was born B. Rosalind Franklin discovered the double-helix structure of DNA C. The genetic algorithm was conceived
  4. 4. Which came first? Take a moment to mentally order these chronologically C. The genetic algorithm was conceived (1950 — Alan Turing) A. David Hasselhoff was born (1952 — Baltimore) B. Rosalind Franklin discovered the double-helix structure of DNA (1953 — London)
  5. 5. Which came first? Take a moment to mentally order these chronologically A. Deep learning neural networks were invented B. The last people on the moon depart C. Led Zeppelin IV releases
  6. 6. Which came first? Take a moment to mentally order these chronologically A. Deep learning neural networks were invented (October 1971 — 8-layer GMDH, Alexey Ivakhnenko) C. Led Zeppelin IV releases (November 1971 — Atlantic Records) B. The last people on the moon depart (December 1972 — Apollo 17)
  7. 7. Data science is older than you think The tools and methods have existed for decades. The technology to leverage at scale them has not. 1960 19661954 1972 1978 1984 Reinforceme nt learning for AI studied First appearance of the term “Deep Learning” First Evolutionary Algorithm Implementati on Back propagation invented for control systems Oracle V1 Implementati on
  8. 8. “[Parameter estimation algorithms] for achieving the minimum in (10), and the statistical properties of the type (15) are all of general character and well known for the more traditional model structures used. They have typically been reinvented and rediscovered in the NN literature and been given different names there. This certainly has had an alienating effect on the "traditional estimation" communities.” - J. Sjöberg, H. Hjalmarsson, L. Ljung, 1994
  9. 9. The Physical Space
  10. 10. Physical sectors are full of potential McKinsey’s 2015 MGI Digital America study shows what industries have the lowest level of digitalization. ● Study was done pre-IoT ● Study was done pre-Data Science revolution Agriculture and Hunting Mining Construction Health Care Basic Manufacturing Chemicals and Pharmaceuticals Transportation and Warehousing
  11. 11. Boeing 787 Dreamliner ● 500 GB per flight ● 554 airframes in service ~227 TB fleetwide, per flight! (That’s very roughly as much as YouTube generates daily, ish)
  12. 12. https://blogs.cisco.com/datacenter/internet-of-things-iot-data-continues-to-explode-exponentially-who-is-using-that-data-and-how Up to 1 exabtye—1000 PB—of data are generated in industrial devices daily 1 EB daily By 2020, there will be 30 billion connected devices 30 billion connected devices And less than half of structured data is used < 1% of unstructured data used Generating data doesn’t mean generating value— experts are needed ~26% of companies seeing value Industrial IoT
  13. 13. Data-Enabled Cars On-board Diagnostics Driver Comfort Telematics Self-Driving Predictive Maintenance ADAS Established High-end only In-development
  14. 14. Data to can save the world Climate change is real. We cannot afford to wait for generational improvements to efficiency. Cycle time for 1% gas turbine efficiency improvement: 18-120 months. 1% efficiency = millions of dollars in fuel 1% efficiency = millions of tons of CO2 1% efficiency = millions of cars off the road
  15. 15. How do we make this happen?
  16. 16. How data are used at various scales <=100 ms 10 s 1 min 1 hr 1 day 1 week 1 month 1 yr 10+ yr 1 unit 100 units 100000 units Real-time control systems Operator feedback Operational History (single run) Operational Lifecycle Real-time fleet monitoring Operational Logistics Fleet maintenance scheduling Service offering/product development Fleet purchasing/replacement Telematics Maintenance planning, recall Product development & engineering Aggregate over time Aggregate over units
  17. 17. Understand the Future by Looking Back
  18. 18. Dynamical Systems Modeling u x A dynamical system is a state-space model where the state x changes with respect to time and in response to the input u. Sometimes we call the model the plant process. A Linear Time Invariant (LTI) system is the most fundamental dynamical system. It can be modeled with a first order system of differential equations.
  19. 19. Dynamical Systems Modeling 𝚺 e xr + - We can affect the behavior of the system by measuring its state and creating a feedback loop. This allows us to set a reference signal r, which we compare to the state to obtain an error e, which we seek to minimize.
  20. 20. I D e Dynamical Systems Modeling 𝚺 xr + - A slightly more sophisticated approach is the Proportional-Integral-Derivative or PID controller. The PID controller uses a set of gains, KP, KI, KD that act like weights. P 𝚺 + + + u
  21. 21. Where else do we choose weights to minimize error?
  22. 22. PID Drone Altitude Control
  23. 23. PID Drone Altitude Control
  24. 24. PID Drone Altitude Control
  25. 25. PID Drone Altitude Control
  26. 26. Handling Noise with Kalman Filtering Use state estimate at step k−1 to predict state at step k Predict error covariance using process noise covariance Compute the measurement error and its covariance, using measurement noise covariance Compute the optimal Kalman gain Update state estimate, posterior covariance, and posterior residual
  27. 27. Handling Noise with Kalman Filtering Source: Wikipedia
  28. 28. Handling Noise with Kalman Filtering A Kalman filter is more or less just a Hidden Markov Model!
  29. 29. Control Theory Everywhere We’re building feedback systems all the time, but how can we design intentionally for them? ● Distributed Systems Monitoring We create feedback loops with monitoring tools; constrained Kalman filtering techniques can be leveraged to create probabilistic real-time anomaly detection. ● Predictive Maintenance As components wear, error accumulates, which manifests as increased control demand. This is a useful feature for a prediction method.
  30. 30. Modern machine learning algorithms and control theory have the same pedigree. Well-known Algorithms We’re generating immense amounts of data and doing almost nothing with it! The business case cannot be more clear: we can use data at many scales to make our systems and processes smarter. We’ve never had better tools to implement these techniques, and they can be extended. Tons of Data Clear Business Value Existing Tools Data Science 💞 Physical Computing
  31. 31. “The fruit [of Control Theory] is so low it is TOUCHING THE GROUND!” - Colm MacCárthaigh

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