by Kamalika Some on October 27, 2018
The world has witnessed the most exciting high-tech projects integrating the knowledge from two or more well-established and fast-growing technology applications including applying machine learning to filter and analyse huge datasets harnessed from the Internet of Things (IoT). To reach its full potential, IoT harnesses inputs from artificial intelligence to include all sorts of sensors and smart devices plunged into the internet to exchange data with each other. This industry is growing phenomenally and is expected that in the years till 2022 there will be around 50 billion devices connected to the network, an enormous 140% increase when compared to 2018 and this number could reach a mammoth 1 trillion devices in 2035.
This massive upsurge will lead to an exceptional rise in the amount of data which is exchanged making it nearly impossible to be analysed deploying traditional methods. 90% of the online data has been generated in the last two years; making organisations across the world feel a shortage of data analysts. The big question is can machine learning be deployed to help with data sorting and analysis?
Machine learning to Ease Analysis
Machine learning is an important effort being made in the broader field of artificial intelligence. Machine learning scientists and engineers aim to replicate the learning process as the human mind does. Machine learning imagines the human brain as a powerful computer, with a combination of a number of external signals as inputs, a summation of these signals being the outputs. For the human mind, the same input as signals would not always result in the same output in terms of action, behaviour or process. The human physical neural pathways are adapting and changing as per the experience and feedback received. While in machine, learning happens when algorithms are updated independently through calculating input signals and how the output is determined.
When it is said that some software’s are capable of self-learning, that corresponds that these software’s can update the algorithm themselves, based on historical results and feedbacks. In short, machine learning software is given the objective and the raw data as inputs, while they are programmed to find the right algorithm that will result in satisfying the objective is their job.
With all this in mind, how can machine learning be employed to help the IoT industry?
Automating Data Analysis
The biggest benefit that machine learning brings to IoT is the automation of analysis of humongous amounts of data generated and exchanged. Instead of a human data analyst going through the tedious process of manually analysing all these data, looking for patterns and anomalies, a well programmed and implemented machine learning algorithm can make this task easy by deploying completely reversed top-down approach in analysis. In other words, given a desired output or outcome, the machine can find the factors and variables that are supposed to lead to this desired output.
Predictive analysis in Machine Learning
Through an understanding of regular patterns and algorithm updates, the software becomes self-sufficient to be able to predict the future desired or undesired events. A system, which is often supervised by a human engineer or scientist, is automatically triggered by the relevant input data, through the formula that it came up with all by itself. The software programme can easily recognize inconsistencies and anomalies that may have taken human eye ages to discover by just looking at the raw data.
A machine learning system is not there just to recognize any abnormal behaviour, but additionally to help the organisations understand and establish long-term trends bringing together a huge job of processing, selecting, recognizing, sorting and associating a vast amount of data collected to make comprehensive and meaningful predictions.
Prescriptive Power of Machine Learning
The machine learning systems don’t just have the predictive power but prescriptive as well, as they can predict future events through the algorithms they have built to help in making devices and systems working on the IoT network more productive. The algorithms can provide assistance for making future predictions and also determining which factors and parameters should be changed in order to reach closer to the desired outcome.
There is no doubt that more and more custom software development companies have preferred machine learning solutions to improve IoT analysis.
With path-breaking changes, there is still a long way to go for machine learning technologies, as it still cannot do without human guidance and feedback. To make these systems particularly effective in data analysis there is a need for continual corrections and supervision, especially when it comes to the amount of the massive data generated by IoT.
To keep them on the right track it is imperative to add human experience and intuition to the self-learning systems. Guiding these machine learning algorithms to automate data analysis is the only way to get an effective IoT analysis for a disruptive future that lies ahead.