Four steps to succeed with the artificial intelligence of things | Control Engineering

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Some technologies are inevitably bound together. Artificial intelligence (AI) and the Internet of Things (IoT) are a perfect example of two technologies that complement each other and should be tightly connected.

The combination is AIoT (the artificial intelligence of things), and it already exists in our daily lives but we seldom recognize it. Think Google Maps, Netflix, Siri and Alexa, for example.

AIoT is creating new value for organizations across a broad spectrum of industries – from manufacturers and retailers, to energy, smart cities, health care, and beyond.

And more organizations are taking notice. Gartner predicts that by 2022, more than 80% of enterprise IoT projects will include an AI component, up from only 10% today.

So, how can you realize success with AIoT?

Analytics is an important practice that is proving big wins for organizations. And AIoT helps analytics get to the next level.

AIoT helps make rapid decisions and uncover deep insights as it “learns” from massive volumes of IoT data.

Looking beyond the physical infrastructure of the intelligent IoT – the sensors, cameras, network infrastructure and computers – there are 4 essential steps that underpin a successful deployment:

1. Think real-time analytics – Use event stream processing to analyze diverse data in motion and identify what’s most relevant.

2. Deploy intelligence where the application needs it – Whether in the cloud, at the network edge, or at the device itself.

3. Combine AI technologies. AI capabilities such as object identification or processing natural language by themselves are valuable; used in synergy, they are indomitable.

4. Unify the complete analytics lifecycle. From streaming the data, filtering it, scoring the data using the model, and storing relevant results to continuously improve the system.

Data comes in many forms. Some data is constantly changing and in motion (such as temperature inside a data center) and other data is more discrete data (such as customer profiles and historical purchase data). This reality calls for analytics to be applied in very different ways for various purposes. For example:

High-performance analytics does the heavy lifting on data at rest, in the cloud or otherwise in storage. Streaming analytics analyzes large amounts of diverse data in motion, where only a few items are likely to be of interest, the data has only fleeting value, or when speed is critical, such as sending alerts about an impending collision or component failure. Edge computing enables a system to act on the data immediately, at the source, without pausing to ingest, transport or store it – a must for many uses in the sensor driven world of IoT devices and services. It’s a multi-phase analytical approach. The key principle to remember is that not all data points are relevant and not all need to be sent to permanent storage. Sometimes the question calls for complex analytics. Sometimes speed is more important. Sometimes the data must be analyzed at the edge. And sometimes it needs to go back to a data center. The analytics infrastructure must be flexible and scalable to support all those needs today and into the future. To realize the highest returns with AIoT, look beyond deploying a single AI technology. Take a platform approach where multiple AI capabilities work together, such as machine learning and deep learning for natural language processing and computer vision. Much of the value of the AI-empowered IoT is the promise to act now. Make customers the right offer before they look away. Detect the suspicious transaction before it is approved. Help that self-driving car maneuver through the busy intersection without crashing into other moving vehicles. Do it now. Latency matters. Many types of sensors and devices cannot wait for data or commands from the cloud. And for other uses, it just isn’t necessary. For monitoring, diagnosing and acting on individual pieces of equipment, such as home automation systems, it makes sense to do the analysis as close to the device as possible. Sending locally sourced, locally consumed data to a faraway data center causes needless network traffic, delayed decisions, and drain on battery powered devices. With the exponential increase in IoT devices and their data volumes – along with demand for low latency – the trend is to move analytics from traditional data centers toward devices on the edge – the things – or to other computer resources close to the edge and the cloud. To achieve value from the connected world, the AIoT system first needs access to diverse data to sense what is important as it is happening. Next, it must distill insights from the data in rich context. Finally, it must get rapid results, whether to alert an operator, make an offer, or modify a device’s operation. Successful IoT implementations will link these five supporting capabilities across the full analytics life cycle: Data analysis on the fly – This is the event stream processing piece as described earlier. Event stream processing analyzes huge volumes of data at very high rates (in the range of millions per second) – with extremely low latency (in milliseconds) – to identify events of interest. Real-time decision making/real-time interaction management – The streaming data about an event of interest – such as a car’s constantly changing location, direction, destination, environment and more – goes into a recommendation engine that triggers the right decision or action. Big Data analytics – Getting intelligence from IoT devices starts with the ability to quickly ingest and process massive amounts of data – most likely in a distributed computing environment. Being able to run more iterations and use all your data – not just a sample – improves model accuracy. Data management – IoT data may be too little, too much and certainly in multiple formats that have to be integrated and reconciled. Solid data management can take IoT data from anywhere and make it clean, trusted and ready for analytics. Analytical model management – Model management provides essential governance across the life cycle of analytical models, from registration to retirement. This ensures consistency in how models are managed – the means to track the evolution of models and ensure that performance does not degrade over time. When you think IoT or think AI, the takeaway is clear: First, if you’re deploying IoT, deploy AI with it. Second, if you’re deploying AI, think about the gains you can make by combining it with IoT. And finally, either one has value alone, but they offer their greatest power when combined. IoT provides the massive amount of data that AI needs for learning and AI transforms that data into meaningful, real-time insights on which IoT devices can act.

Original content can be found at blog.mesa.org.

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