Tuesday, August 6, 2019

Relation of AI and Data Analytics withIoT

AI is fast becoming the ‘must have’ for any business to handle the complexity of Cloud and the Internet of Things (IoT). Today’s AI engineers and Data Analytics teams are constantly working to improve management of IT resources for better speed and scalability.

To clearly understand how AI and Data Analytics impact the adoption and performance of the IoT applications, we have to first understand the basic foundation of these connected infrastructures and how they differ from traditional IT platforms.

What is IoT and how it relates to AI and Data Management?

Big Data companies are hiring professionals from data analytics training in Bangalore. Their aim is to design, build and adopt seamlessly with their IoT approach.IoT systems consist of one or more inter-related computing devices, software programs, and unique sensors to detect digital signals, also called UIDs. Also called as Digital Twin to wireless connectivity, IoT is referred to as Machine-to-Machine communication in all major research programs.

Taking M2M to the next phase of hyper-growth and super-connectivity would be a mammoth event in the human race.

At its foundation, IoT will secure thousands of billions of AI-based data sensors that will drive SCADA models. Right from automation to cognitive learning and augmented reality, these SCADA for IoT applications are dependent on human-focused interactions created by trained data analytics professionals.

AI and IoT relation can be explained in four steps.
Step 1: Understand the neural activity of how M2M works. Data analysts need to continually assess the complex environment in Cloud and AIOps to design advanced IoT systems.
Step 2: Data Analytics, based on what M2M are processing.
Step 3: Decision making, based on what the M2M communication analyzed.
Step 4: Finally, the loop is closed on AI ML interactions with IoT devices based on the physical action.

For example, the physical action for aIoT robot would be to lift an object. For a connected car, it would be to park safely. For an IoT drone, it could be to capture the image of a pest wandering in the field. These actions can be governed based on the software input and primary AI ML algorithms that are supervising the entire process.

If you look through the lens of AI and ML development teams, you would realize that machines are harder to train and manipulate. Due to complexity of Neural Networking, we are yet to see an AI software succeed in successfully simulating human brain and performing real time actions without a pause. The time lag is what taking the shine off AI-based IoT models. And, here the opportunities are galore for data analytics professionals.

A software at the heart of AI ML algorithm is the secret to design IoT that can help M2M migrate successfully into human space, and deliver on speed, accuracy, and above all, experience.

By 2023, there will be an advanced level of AI, what we will call as Social AI. Social AI will make these IoT models look sophisticated, and yet, very much human.

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