As embedded engineering veers towards cognitive AI and digital knowledge, IoT devices will go beyond being a mere collection of sensors to becoming really intelligent.
With the advent of Internet of Things (IoT), the line between humans and machines is blurring. The communication ability between machines and humans has become location independent with the possibility of remote connection, accessible through mobile. A few decades ago this was primitive but thanks to technological advancements and proliferation of the Internet, devices are more connected than ever.
It all starts with Data
Today, enterprises are digitally connected which signals an important opportunity to redefine business and outlook by gaining insights from the vast amount of data generated via millions of connected devices. With business finding true value in the adoption of IoT, Gartner suggests that by 2020, there will be 20.4 billion connected things in the world.
The massive growth of applications as part of digital transformation is letting enterprises unearth terra bytes of data which can enable better performance in enterprises. The right analytics on incoming data can help enterprises with actionable intelligence which can be used to deliver the desired business outcomes.
Intelligence at the Edge
Currently, businesses have adopted IoT devices to collect and transmit data to the on-premises server or the cloud for further analysis. There are three major issues with this process:
- Most of these devices operate in silos and do not communicate with each other
- There isn’t sufficient bandwidth or connectivity, especially in the industrial locations, to rely upon
- Lot of data generated and makes it difficult to analyze on time and make quick decisions. This makes it difficult for enterprises to collect, correlate, and analyze data for actionable intelligence
The solution is to have intelligent IoT devices that can filter data, analyze and take decisions at an individual machine level or sensor level, thereby saving time and efforts. For this to happen, the devices need to have embedded software systems that can help these devices function intelligently. In other words, we are looking at the application of cognitive computing or cognitive AI at the edge.
Cognitive AI enabling decisions
Cognitive learning at the edge of the device network enables decision-making at the sensor level rather than at a cloud or server resulting in a faster decision-making process. Cognitive computing technology or Cognitive AI goes beyond data analysis to comprehend large amounts of data, apply reason, extract valuable insights, and finally provide actionable intelligence. Cognitive AI also enables a continuous learning process through interactions with humans and machines and thus, in a way, replicates human learning process.
Cognitive AI and digital information at the device or sensor level offer unparalleled opportunities for enterprises to make smart business decisions without much delay giving them the required edge in the market. The speed at which decision can be taken makes the difference in few use cases while some may not require real-time but definitely a reduction in the computing and storage.
What the Future holds
According to Forbes, 80% of enterprises already have some form of AI (machine learning, deep learning) in production while 30% of enterprises are planning on expanding their AI investments over the next 36 months.
Some major chip companies are looking at incorporating AI software on to their chips while software companies are building advanced AI that can be embedded into off-the-shelf chips in the market. The embedded AI pre-built libraries will be available soon in market as software and hardware components. With the market and players gearing up for cognitive AI, it could reimagine the plant operation performance going forward.
Getting to a stage where sensors act as intelligent devices is still in the offing but embedded software, cognitive AI, and digital information will take IoT to the next level. The advancement in technology (hardware and software) would ease the realization of Artificial Intelligence and Machine Learning at edge level. In the future, enterprises will be able to harness the complete potential of IoT devices through cognitive AI which will not only help analyze tons of data but also diagnose issues and provide solutions in real-time right at the edge.
Author: Channabasavaraj Raravi, Senior Solutions Architect – Software, Product Engineering Services
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