What is Machine Learning

Machine learning is a branch of artificial intelligence (AI) and is what allows computers to continuously learn and improve. What makes machine learning stand out is the fact that computers are able to ‘learn and improve’ on their own and not have to be programmed and re=programmed.

Machine learning is concerned with the creation of computer programs that can access data and learn on their own. Learning starts with data such as examples, direct experience, or teaching so that we can seek patterns in data and make better decisions in the future based on the examples we provide. The main goal of implementing machine learning is to allow computers to learn on their own, without the need for human interaction, and adapt their activities accordingly.

What is IoT?

The Internet of Things (IoT) is a network of physical devices (‘things’) that are embedded with sensors, software, and other technologies. What exactly makes these physical objects unique? It has to be their ability to connect and exchange data with other devices and systems over the internet. Such devices range in complexity from common household items to sophisticated industrial instruments.

Experts predict that by 2020, there will be more than 10 billion connected IoT devices, and 22 billion by 2025. Oracle has a device partner network. Connecting these diverse products and attaching sensors to them gives devices a level of digital intelligence that will make the life of the everyday user a little easier. This pushes devices to be able to convey real-time data without involving a person.

Why use Machine Learning for IoT?

Machine learning can reduce human errors, allowing collected data to provide real-time insights. Insights produced allow IoT devices to fulfill their full potential. Machine learning and IoT provide insights that are hidden in data for rapid automated responses and improved decision-making. By applying machine learning, IoT devices are able too predict and behave based on their own.

There’s never a dull moment when it comes to big data and artificial intelligence. Their benefits and future potential open doors to innovation across industries. Meanwhile, businesses are increasingly adding sensors in the hopes of increasing efficiency and lowering costs. According to InData Labs‘ machine learning consultants, without a suitable data management and analysis plan, are only making more noise and filling up more servers without being exploited to their full potential.

How is machine learning used for IoT?

1. Smart home

Smart home devices can range from everyday items such as smart lighting and tvs. So why integrate IoT sensors, machine learning models, machine learning algorithms, and big data analytics? The purpose of this integration is to make the home more helpful and responsive to the user’s needs and routines. IoT platforms can help by predicting them by collecting data in real-time, instead of having to entirely rely on commands and manually programmed routines.

2. Smart cars

Autonomous vehicles might only still be in the testing phase but have made a reality through in the automotive industry. By using supervised machine learning models and algorithms, car manufacturers are able to implement and monitor the way vehicles behave when faced with a wide range of scenarios and for developing advanced driver-assistance systems.

A few examples where machine learning is being used when driving a car include:

• Proactive detection and classification of objects
• Driver monitoring
• Driver replacement
• Sensor fusion
• Vehicle powertrain

The goal of machine learning is to mimic how the human brain analyzes information in order to develop logical responses. Machines require an algorithm if they rely on learning, training, or experience. Furthermore, when we gain more knowledge, we alter our reflexes, improve our skills, and begin to use our efforts judiciously. The goal of machine learning is to replicate this self-regulatory behavior in machines.

Data analysis automation

Consider the case of automobile sensors. When a car is on the move, hundreds of data points are recorded by inbuilt sensors. The data collected must be processed in real-time to avoid the possibility of accidents while also providing comfort to passengers. Because a human analyst would be unable to undertake such a task for each car, automation is the only option.

Machine learning allows the vehicle’s central computer system, similar to the central nervous system of a human, to learn about potentially harmful scenarios, such as speed and friction characteristics, and activate safety systems when required.

The predictive power of Machine Learning

Looking back at the case of automobile sensors as an example, the true value of IoT rests not just in detecting the immediate threats, but also in finding common patterns. For instance, the system may learn about a motorist who makes too tight corners or has trouble parallel parking. The system then constructs itself in being able to assist the driver by providing more instructions.

What makes machine learning unique to IoT? The most useful characteristic of machine learning for IoT is it’s the ability to recognize anomalies and automatically raise red flags. It improves accuracy and efficiency as it gains more knowledge about a phenomenon. Google’s HVAC system is a wonderful illustration of how to save electricity.

How has ADL helped with IoT?

Axiata IoT Platform: The Axiata IoT Platform presents a universal atmosphere that provides smart powerful IoT solutions and facilitates IoT device vendors to successfully onboard devices and device management. This cloud-based, agile, open-source platform is “right-sized” for the Axiata marketplace. By using this IoT platform, device data can be collected and performed analytics on real-time data. The main objective of this platform is to facilitate generic IoT user cases of the Telco Operators which include Developer, Enterprise, and Consumer domains.  

Smart Greenhouse (mAgri): Magri stands as a revolution in agriculture, creating a self-regulating, microclimate suitable for plant growth through the use of sensors, actuators, automation, and monitoring/control systems that optimize growth conditions and automate the growing process. 

Dialog Smartlife: SmartLife is a home automation mobile application that allows users to plan & control their home devices using their own mobile device making household management convenient and controllable, by being integrated with IoT services. This application controls smart homes, making it flexible to support multiple device vendor platforms eliminating the need to go through multiple apps.

Smart City: A smart city solution caters users with IoT devices related data by monitoring areas that use different types of electronic Internet of things sensors and devices to collect data and then use insights gained from that data to manage assets, resources and services efficiently, in return using that data to better improve the operations across the city. Widgets and dashboards will populate data based on the location while accessing throughout the city.

In conclusion

Massive amounts of data can be analyzed using machine learning. While it provides faster and more accurate results in identifying new revenue streams or risky threats, it does take a lot of time and resources to train it. Combining machine learning with artificial intelligence (AI) and cognitive technologies can improve its ability to process massive amounts of data. 

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