Machine Learning: How it Works & How its Used

The ability for a computer to mimic human intelligence has resulted in a game-changing attribute that could reinvent the pace of digital technology; that is of course, the ability for machines to make decisions from the data that they have been given, in an independent manner. Similarly, this is called “Machine Learning” and it makes up a critical part of what is currently understood as artificial intelligence.

The Humble Beginnings of Artificial Intelligence

Traditionally, machines would possess a step-by-step process of thinking, which would be installed into the computer’s hardware, so that it knew automatically what instructions to follow. Rather than solving problems by itself and by using its own logic, the programmers themselves wired machines to respond to certain situations in with their own preprogrammed logic.

Everything changed when University of Illinois engineering student Arthur Samuel chose to program a computer to respond uniquely to new sets of stimuli. That stimulus came in a unique form itself: checkers. Instead of preprogramming the responses that the computer would take, Samuel instead gave the machine advice from the top ranked checkers player in the world, so that it would “think” for itself and make moves based on this advice.

The Efficiency of Algorithms

Eventually, machines learned to assemble data through the use of algorithms, which are large groups of mathematical instructions formatted through a step by step process. An example of which would be an algorithm that directs a machine to collect images that feature similar objects within them. A more powerful mechanism that AI uses is called “deep learning”, which organizes its “thinking” into a system known as a neural network; essentially, this is a hyper efficient way that data can be organized and processed through a machine, resulting in an imitation of human-like thinking.

Currently within the functionality of deep learning, data moves quickly through nodes or connection in one direction. As data passes through nodes, each layer of nodes become more complex, or “deeper”, resulting in the ability for the machine to build upon previously limited information and essentially learn new concepts from such data. So rather than simple rudimentary responses to stimuli, the deep learning process allows for machines to review and learn from data and make decisions based on that information without the help from a human.

Current Examples

Recently, the basic applications of artificial intelligence have been showing up within a variety of daily mechanisms used today, specifically in regards to software. An example of this is streaming services, which automatically looks for content which the user might enjoy based on previously selected content.

Machine learning is also being used within a variety of different disciplines; in America, an engineering system is currently being used to suggest and analyze hundreds of different engine designs, after which the best one might be taken and produced based on the efficiency of the model. Furthermore, machine learning is also being used within the area of medicine in identifying diseases based on the shape of the detected virus.

Probably the most disconcerting example of this technology being used within everyday life is within video surveillance technology. Because having a single person viewing a multitude of surveillance screens is rather impractical, computers have instead taken up the task. Indeed, video surveillance operated by artificial intelligence makes it possible to analyze behavior and compare it with data of previously monitored criminal behavior, essentially being able to predict a crime before it occurs. The system can then alert staff so as to avoid a possible tragedy.

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