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PG Madhavan's Blog Posts Tagged 'IoT' (5)

IoT Data Science & “DML” – match made in heaven?

DML stands for “Dynamical Machine Learning” (more in the book, “SYSTEMS Analytics for IoT Data Science”, 2017). This match is not surprising once you realize that DML & IoT are both based on the venerable Systems Theory. Let us dig deeper . . .

Consider IoT for industrial applications. A machine is instrumented with sensors, data are collected in real-time (or at intervals), communicated to the cloud where IoT Data Science…

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Added by PG Madhavan on September 11, 2017 at 12:30pm — No Comments

History as a guide to IoT growth trajectory

Internet of Things (IoT) has generated a ton of excitement and furious activity. However, I sense some discomfort and even dread in the IoT ecosystem about the future – typical when a field is not growing at a hockey-stick pace . . .

“History may not repeat itself but it rhymes”, Mark Twain may have said. What history does IoT rhyme with?

 I have often used this diagram to crisply define IoT.…

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Added by PG Madhavan on April 2, 2017 at 1:00pm — 1 Comment

NEXT Machine Learning Paradigm: “DYNAMICAL"​ ML

Dynamical ML is machine learning that can adapt to variations over time; it requires “real-time recursive” learning algorithms and time-varying data models such as the ones described in the blog,…

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Added by PG Madhavan on March 18, 2017 at 2:30pm — 1 Comment

IoT as a Metaphor

What exactly is “IoT”? Internet of Things, yes; but what does that mean?

Internet of Things is a structural definition; it says there are “Things” such as sensors and devices (on machines or people) connected together in a Network. So what? What does a Network of Sensors & Devices allow us to DO? What is the functional description of IoT?

Being able to connect things together is “table stakes” at the intelligence augmentation game. What…

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Added by PG Madhavan on October 16, 2016 at 9:30am — No Comments

ADAPTIVE Machine Learning

Machine Learning today tends to be “open-loop” – collect tons of data offline, process them in batches and generate insights for eventual action. There is an emerging category of ML business use cases that are called “In-Stream Analytics (ISA)”. Here, the data is processed as soon as it arrives and insights are generated quickly. However, action may be taken offline and the effects of the actions are not immediately incorporated back into the learning process. If we did, it is an…

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Added by PG Madhavan on May 20, 2016 at 5:30am — No Comments

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