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Leveraging IoT Active Device Using Microcomputing

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Leveraging IoT Active Device Using Microcomputing

Take a look at microcomputing and how artificial intelligence such as Alexa uses it.

· AI Zone ·
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EdgeVerve’s Business Applications built on AI platform Infosys Nia™ enables your enterprise to manage specific business areas and make the move from a deterministic to cognitive approach.

How does Alexa respond to your question when you ask, “Alexa! What’s the news?” Alexa cares for you by setting an ambient room temperature and even saves the power when you are away. The smart solutions remain connected with you every hour notwithstanding of the ongoing event. From keeping the log on every activity connected to the smart network to keeping track of every request, the smart devices flawlessly perform the functions called to present.

Smart devices like Alexa, Atmos are IoT-based with a fabric of Artificial Intelligence (AI) and upgraded system inbuilt utilities.

What gives life to IoT devices? Is it just AI algorithms or something better that is never mentioned?

Perhaps the technology left to sing out was the collaborated role of AI and microcomputing. Microcomputing is an evolved form of computing involved in the task to make our lives easier and better than ever before.

Microcomputing — a Decades-old term with new potentials

Microcomputing today is seen very differently than it was a few decades back where it was merely defined as a sophisticated set of functional microprocessors. Today, microcomputing is realized as an advanced processor devised to support Artificial Neural Network(ANN) and Recurrent Neural Network(RNN).

Elaborating roles of advanced processors and AI

Alexa does what we ask it to do. Sometimes, it does few works intelligently by just realizing our absence and saves time and money as well. It’s not only Alexa; that’s the story of every smart device designed to make our life smarter.

Do you know the story behind a curtain? How is every instruction converted in command that gets processed? Let’s do it.

  1. Data is the core around which the entire functionaries run. The data can be business insights, hind data, exploratory analysis, and collected data (structured or unstructured).
  2. The gathered data is auto distributed across horizontally scalable architecture, which is a new concept in data re-distribution. This structure is defined as in-memory computing.
  3. Now, machine learning plays the critical role by training the existing data on many layered neural networks. This is materialized by the fusion of AI algorithms and type of stored dataset.
  4. Datasets vary as per the choice of the process. System Administrators classify them as cold data stored on hard disk, warm data stored in flash storage, and hot data stored in RAM.
  5. Disk data, warm data, and hot data do not support the requisites required for AI. This is where Intel had to step-in. Intel Optane is sandwiched between SSD and RAM to access the data more swiftly.
  6. The collected data (structured or unstructured) from the sensor is sent to the SSD and finally to RAM through an Optane layer. However, organizations don’t need an actual hardware device to test the application.
  7. Once materials are done, it is the time to set everything virtually. It is done with IoT application editor page, which is already created by the blue mix.
  8. Series of interconnected nodes are created in which a node will take the temperature readings from the temperature sensor. This will, in turn, send the reading to another node which will decide where to send the data.
  9. On subjective analysis by the system of nodes, the instructions are converted into actions or sometimes the required data.

The entire process engages the microcomputing components and thus, brings to light the credit sharing with artificial intelligence’s algorithm.

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Topics:
microprocessors ,internet of things (iot)testing ,iot analytics ,ram disk ,iot devices ,sensor accelerometer ,sensor data ,sensor-to-the-cloud connectivity ,sensor technology ,iot adoption

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