Infusion of AI – data science in IoT

A very popular phrase among data scientists and machine learning engineers is "AI is the new electricity" said by Prof Andrew Ng in NIPS 2017, we can expand it as follows: If AI is the new electricity, data is the new coal, and IoT the new coal-mine.

IoT generates an enormous amount of data; presently, 90% of the data generated isn't even captured, and out of the 10% that is captured, most is time-dependent and loses its value within milliseconds. Manually monitoring this data continuously is both cumbersome and expensive. This necessitates a way to intelligently analyze and gain insight from this data; the tools and models of AI provide us with a way to do exactly this with minimum human intervention. The major focus of this book will be on understanding the various AI models and techniques that can be applied to IoT data. We'll be using both machine learning (ML) and DL algorithms. The following screenshot explains the relationship between Artificial Intelligence, Machine Learning, and Deep Learning: 

By observing the behavior of multiple things, IoT (with the help of big data and AI) aims to gain insight into the data and optimize underlying processes. This involves multiple challenges:

• Storing real-time generated events
• Running analytical queries over stored events
• Performing analytics using AI/ML/DL techniques over the data to gain insights and make predictions