According to Forbes, the internet of things is the idea of connecting devices to each other or the Internet. Consider your smart home devices that work only when your phone is nearby or a light that switches on if any movement takes place around it. These are all examples of IoT.
There are security concerns related to IoT that sprung up when a cyber-attack on a power grid in western Ukraine took it down last year. Additionally, a wave of uncertainty and shock hit the globe when people realized that self-driving cars could be hacked. These incidences raise several questions on the safety and security of IoT.
However, these fears haven’t stopped companies from progressing down this road. If anything, they have increased their expenditure in not just combating these incidences, but also improving the technology further.
As it stands today, there is immense fragmentation in the IoT industry. This could no longer be in the case in 2018. More integrated IoT solutions are likely to come into the market this year that foster interoperability between devices by becoming part of open platforms or ecosystems to provide services depending on data generated from different sources and devices.
This trend of increased IoT value has caused companies to sit up and take notice. Even companies that wouldn’t jump into new trends are realizing the impact of AI and IoT. It is because of this that they are scrambling to hire experts who have taken an AI course. These AI engineers can help reduce their overheads and raise profit margins.
Smart cities and smart homes are two main areas that will focus on IoT. Due to the requirement for higher bandwidth in the smart home department to allow any IoT technology to work, there could be an upsurge in demand for mesh-like products which operate over a simpler network.
IoT growth combined with other systems and devices produce a bulk of data that need AI to develop insights from data.
Artificial intelligence has begun to make its place in IoT. One of the biggest examples of IoT devices is self-driving cars, and these cars that have limited autopilot capabilities like the Tesla are machine learning based. Skilled programming enables the makings of a self-driving car that understands the rules of the road, which efficiently manages all obstacles. However, these cars have shortcomings. Programmers have not been able to program for every possible variable which occurs when other drivers are introduced into the mix.
Therefore, AI is important in IoT. Now, a Tesla car gathers data in real time of other Tesla cars present on the road. The introduction of any new variable becomes easy to learn and share with other connected cars, securing autopilot mode for Tesla drivers.
Big Data, simply put, refers to the term given to large datasets. Data can be of any type of data, from medical data that hospitals provide to usage data from IoT based devices and everything that comes in between. Big Data helps industry leaders understand growing sales trends and customer behavior patterns to make improvements in existing services or products that further result in higher sales.
The use of predictive algorithms helps computers perform better data analysis and make predictions. However, a limited processing power prevents them from learning. Though a traditional computer can process thousands of data bytes, AI-powered computers can process large amounts of data, analyze trends, and provide results according to those trends.
Machine learning is in its growing stage. Therefore, it is not the perfect solution. In fact, machine learning came into the spotlight recently for all the wrong reasons. When Microsoft applied a version of its AI program to a Twitter account, Twitter trolls turned it into a racist mimic that resulted in the delivery of offensive messages to others. A well-regulated AI system becomes a valuable tool for the IoT industry. The two run parallel and their combination can remodel existing models while revamping industries by accurately predicting trends in the existing market.
Consider a business that merchandises in wearable technology that uses AI to predict what type of devices will sell in the next year. The right insights can help understand what a customer looks for, and what trend is going on, which will help companies make more money.
Bottom line: IoT combined with AI could decrease product inventory, which in turn could increase profits. The proper regulation of the system would allow machine learning to change the way we perceive things and is already changing we use devices.
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