Achieve IoT full potential with Artificial Intelligence (AI)
Exponential growth (IoT) vs. profound impact (AI)
The IoT Data Treasure Trove
IoT massive scale translates to an ever increasing amount of big data. This data is also very valuable – it can optimize productivity in manufacturing through predictive maintenance on equipment, it can supply doctors with real-time info from life supporting pacemakers or biochips and it can even help cities predict accidents and crimes. The possibilities that IoT brings to the table are endless.
However, the big problem is analyzing all data these connected devices create. It’s simply not possible for humans to use all of this data as it simply takes too much time. Speed and accuracy of big data analysis is critical for IoT, and the only way to do that is through Artificial Intelligence. With Machine Learning (ML), review and analysis of collected data is possible in order to find patterns or similarities that can be learned from, so that better decisions can be made. ML can analyze the data immediately as it’s collected to accurately identify previously known but also never-before seen new patterns.
Benefits of combining IoT with AI
The combination of IoT-AI has great impact to all Industrial IoT use cases, from airlines, to manufacturing and even smart buildings, but it is not restricted there. Non industrial IoT use cases such as personal health care and smart home, are greatly impacted as well.
Real-time monitoring can prevent disasters from occurring and hence raise overall safety. Think of a security camera using AI to distinguish between people, animals and vehicles, and take actions—such as turning on lights or sending an alert—based on what it senses, or smart sensors adjusting to the individual behaviours of users/ consumers by learning their preferences.
Given the scale and range of potential benefits, it’s hardly surprising that a combination of IoT and AI is so important for IoT success.
Predictive and Prescriptive Analytics with AI
IoT involves mainly time-series data, but data is most valuable when it can trigger an action. This introduces the need for real-time collection and analysis, to maintain a continuous flow of information.
Given the power and scalability of AI solutions, tasks that take humans weeks or months to complete, are now with AI actionable in minutes or seconds.
Also very important with IoT is post-event processing, such as seeking out patterns in data over time and running predictive and prescriptive analytics. Two questions need to be answered: “What will happen?” and “What should we do?“.
But prescriptive analysis is also very important at the edge. Intelligent sensors for example can suggest immediate action at the edges of an organization, thus avoiding outages and even disasters.