Obviously AI, but how?

Answer - IoT won’t work without artificial intelligence. The Internet-of-Things provides us with tons of sensor data. However, the data by themselves do not deliver value unless we can turn them into actionable, contextualized information. AI (Machine learning), Big data and data visualization techniques allow us to gain new insights by batch-processing and off-line analysis. Real-time sensor data analysis and decision-making is often done manually, but to make it scalable, it is preferably automated. Artificial Intelligence provides us the framework and tools to go beyond trivial real-time decision and automation use cases for IoT. The real benefits of IoT could only surface when AI is combined together, both at the server side as well as at devices.

As the Internet of Things (IoT) continues its run as one of the most popular technology catchwords of the year. IoT will harvest a treasure trove of big data – data that can help cities forecast accidents and crimes, provide doctors real-time insight into information from pacemakers or biochips, enable optimized productivity across industries through predictive maintenance on equipment and machinery, make truly smart homes with connected appliances and provide critical communication between self-driving cars. The opportunities that IoT brings to the table are endless. As the rapid expansion of devices and sensors connected to the Internet of Things continues, the sheer volume of data being created by them will increase to an another level.

In an IoT situation, machine learning can help companies take the billions of data facts they have and boil them down to what’s really meaningful. The general evidence is the same as in the retail applications – review and evaluate the data you’ve collected to find patterns or similarities that can be learned from, so that better decisions can be made. For example, wearable devices that track your health are already a budding industry – but soon these will evolve to become devices that are both inter-connected and connected to the internet, tracking your health and providing real-time updates to a health service. In order to analyze the data immediately as it’s collected to exactly identify previously known and never-before seen new patterns, machines that are capable of generating and aggregating this big data must also be used to learn normal behaviors for each patient and track, uncover and flag anything outside the norm that could indicate a critical health issue. The realization of IoT depends on being able to gain the insights hidden in the vast and increasing seas of data available. Since current approaches don’t scale to IoT volumes, the future understanding of IoT’s promise is resting on machine learning to find the patterns, correlations and variances that have the potential of enabling improvements in almost every surface of our daily lives.

Artificial intelligence will be functionally necessary to exercise the vast number of connected “things” online, and will be even more significant in making sense of an almost endless amount of data streamed in from these devices.