Notable Differences among IoT, ML, and AI
The Internet of Things (IoT)
Prior to diving deep into this question, we must understand the technicalities of these terms: The IoT (internet of things), ML (machine learning), and AI (artificial intelligence). Firstly, we are going to touch upon the fundamentals of the IoT. It would be easier to latch onto the internet of things if you are familiar with devices such as smart watches, GPS navigation devices, smart security systems, fitness trackers, and so on. Recently, the concept of smart homes has become a topic of interest. How these pieces of equipment function? Because all these devices are connected to the internet and designed in such a way that they generate large sets of data, which, in turn, are used to better the functions of equipment later.
Machine Learning (ML)
Now as we discussed earlier, the datasets that are generated by the internet of things build on logical interpretations or solutions, which can either be used by programmers or the process of machine learning. However, real machine learning turns up after the process of programming when the computer or machine itself predicts and categorises the variety of data and then monitors standard deviations from the original results in order to generate data, which is automatically used to take relatively improved action. The main objective of ML is to enable computers or machines to learn and perform without manual assistance. In addition, there are other methods of ML algorithms such as supervised, semi-supervised, and unsupervised.
The major difference between supervised and unsupervised ML algorithms is that the former uses labelled data and the latter uses unlabelled data for training. However, semi-supervised ML algorithms use both, mainly unlabelled data to a greater extent so that the machine demonstrates a high percentage of learning accuracy. Many organisations show the strong inclination to translate their business processes to machine learning methodologies in order to transform large amounts of data into automated processes that can yield faster, profitable results and detect risks beforehand.
On the other hand, no matter how artificial intelligence sounds overwhelming, the IQ of present-day artificial intelligence is blatantly zero. In simple language, AI is the final stage of both the IoT and ML because it deals with decision-making. As the data is processed and cleaned every time, it is ready for use in a new set of improved actions, but making this decision on its own is the function of artificial intelligence. In other words, when associating cognitive functions with machines, as it were, programmers capacitate machines to make decisions without human interference.
The Ideal Combination of IoT, ML, and AI
Now we must look at how all these functions blend in with one another in order to deliver groundbreaking innovations. The epitome of their composite includes smart homes and self-driving cars, which use data as a basis for continuous improvement in the customer experience. For an easy understanding, we ought to consider these examples in detail. As we know, self-driving cars are one of the best examples of the internet of things, which subsequently generate a barrage of data that can either be used as training data for cars itself in the case of supervised machine learning or allow cars to identify data patterns by themselves in the case of unsupervised machine learning. However, supervised machine learning would be the better choice if we have to make notable improvements in the performance of self-driving cars.
Kitting these cars out with their own decision-making ability, making them a perfect example of artificial intelligence. Every time taking a drive, the car generates a continuous stream and new patterns of data that helps to make improvements in the safety of passengers, that is to say, the more drives the self-driving car takes, the safer it becomes for passengers.
The internet of things, machine learning, and artificial intelligence are interrelated to one another, and the IoT and ML can be considered to be the subsets of AI. Most importantly, without one another, these technologies cannot be developed to the next level.