The importance of machine-learning and artificial intelligence technologies
The Internet of Things – connected by networks to computing systems-has received enormous attention over the past five years. We have to understand how exactly IoT technology can create real economic value. In order to understand the full potential of the Internet of Things, we need to look at IoT in an entirely new way i.e., from short-sighted use cases to a broader and deeper application of IoT. To make the Internet of Things useful, the data coming from different nodes can be integrated into a data warehouse which helps us to analyse the trend forecasting. The better name would be “Internet of Systems”, with which we can begin to see the data to better understand the relationships and dependencies of complex systems. Companies and Individuals can benefit from integrating large amounts of data and analysing the data which improves understanding of data, and thus makes efficient decision making. The IoT has large potential in developing economies and increased profits.
Data-driven decision making:
The organizations load the large data sets from IoT into systems that use machine-learning algorithms which helps in making predictions and decision making in automated way. The importance of machine-learning and artificial intelligence technologies is their ability to automate and augment complex decision making. The main success areas of IoT and AI-based decision making is e-commerce. The revenue growth, increased customer size, customer purchases, accuracy of sale forecast results, product optimization and improved customer-relationship management are the benefits of IoT and AI-based decision making. The companies which use data-driven decision making are, on average, 5% more productive and 6% more profitable than competitors.
With the gradual increase of IoT and AI based technologies in data driven decision making, the privacy issue has become a core problem. We have to consider who we want to access the information. But, if we keep data inaccessible to anyone at all, this would prevent us from analysing customer behaviours, crime and fighting diseases. An exponential growth in data rate causes difficulty in securing each and every portion of critical data. Misusing of IoT data can have irreversible consequences, so there is a need to minimize risk by employing suitable techniques to manage the security and privacy of interconnected devices. In order to succeed in automated data-driven decision making, there is a need not just to develop data management, but also to protect the sensitive information.
Another challenge is to integrate and analyse different types of data such as structured data, unstructured or semi-structured data. Integrating different data types is a complex task in merging different systems.
IoT became one of the most profound transition in technology. IoT provides several data warehouse opportunities by analysing the huge amounts of data for decision making. Now a days, everything from phone, watches, cars are available in smart versions. The innovations in IoT are really exciting, and in some cases life-changing.
For example, The Evolution of Autonomous Transportation,
The evolution of autonomy is going big, and transportation is at the head of the innovation parade. The concept of IoT and self-driving cars is to imagine every vehicle on the road have been getting technologically more sophisticated for collecting data about everything from road and weather conditions to driver behaviour. This is nowhere different from what each of us, gathers and stores in our minds as we travel. The only difference is smart vehicles, send data to the cloud, and that information is used to improve the software systems in all the connected vehicles.
The potential impact of IoT technology to address safety, efficiency, the environment are overwhelming. Here are some of the major ideas which will be powered by IoT in the evolution of autonomous transportation,
1. Crash response: Self-driving cars send real-time data about the crash to the emergency teams along with the vehicle location. This can save lives with immediate actions.
2. Car problem diagnosis: Self-driving cars diagnosis the car and generates data that can predict a problem even before a part fails, which prevents from any breakdown and helps us to take preventive steps.
3. Convenience Services: The convenience services such as remote door unlock, find the stolen vehicle all these are possible with self-driving cars.
4. Traffic Management: This technology provides real-time traffic, transit, and parking data to the transportation agencies, making it easier managing traffic and congestion.
5. Enhanced Safety: Enhanced safety is important to the future of automobile and transportation sector. Self-driving cars use sensors, GPS, high-definition cars to make real-time safety decisions for the vehicle and thus have potential to reduce traffic deaths.