How AI is impacting IoT in building smart applications

The applications of IoT are endless. From helping cities predict accidents and crimes to enabling optimized productivity across industries through predictive maintenance, IoT is creating a treasure trove of Big Data. Using AI in IoT is the only way to keep up with this IoT generated data and gain insights.

Our world is facing a rapid expansion of devices and sensors that are connected to the Internet. The sheer volume of data that gets created by them is increasing constantly. This data holds great value as it helps in deriving useful insight into what’s working well or what’s not. For example, pointing out conflicts that can arise in industries or providing insights into business risks and opportunities. The problem arises while finding out ways to analyze massive amounts of performance data and information coming from these devices. It is simply impossible for humans to understand and review terabytes of data. For improving speed and accuracy of analyzing data coming through sensors enabled devices, AI in IoT is used.

Cognitive Computing in IoT

This technology is a subset of AI that has the ability of learning from interactions with humans and their experiences. Cognitive computing is probabilistic; this enables cognitive systems to keep pace with the complexity and unpredictability of data generated by IoT devices. Businesses can use these systems for illuminating aspects of the IoT that were previously invisible, such as hidden patterns and insights culled from disparate sources, which allow businesses in making more informed decisions. Cognitive systems can also provide unbiased hypotheses, reasoned arguments and recommendations, which help in generating answers to numerical problems. Smart applications based on cognitive computing have the ability to understand an organization's goals, and thereby it helps in integrating and analyzing relevant data to help businesses achieve those goals.

Machine Learning in IoT

Machine learning is another subfield of AI that deals with the construction and study of systems, which have the ability of learning from data. Machine learning can help companies in taking billions of data points generated by IoT devices and boiling them down to what’s really meaningful. Machine learning systems can accurately identify previously known and never-before seen new patterns. Machine learning holds the promise of finding correlations and anomalies that have the potential of developing smart applications that can bring improvements across all facets of our daily lives.

Deep Learning in IoT

Deep Learning is a subfield of AI and machine learning that consists of a set of algorithms that have the ability to mimic the human brain. Deep Learning algorithms are now applied to several areas including image recognition, computer vision, pattern recognition, speech recognition, etc. Deep Learning algorithms are best suited for IoT devices and smart applications that involve large amounts of data and complex relationships between different parameters. It helps in solving intuitive problems by ignoring inputs that are irrelevant to the solution.

Internet of Things is growing rapidly and bringing out various applications. AI can help businesses in understanding what people want from the data generated by human beings. Thus, implementing AI plays an essential role in handling huge amounts of data and embedding intelligence in those devices.