Artificial intelligence and machine learning have become growing parts of technological advances over the past few years, impacting almost every sector from transportation to medicine. In the 20th century, when computer programs were first developed, each line of code had to be written by a programmer outlining exactly how the computer was to respond in any given situation. However, with machine learning, computers are starting to figure out the rules on their own.
According to Tricentis, learning requires following a set of rules presented to the machine as an algorithm. These systems are often used for math-based applications like accounting because it is easy to show the machine what to do when certain conditions are met. With learning systems for artificial intelligence, the machine can begin to add to the programmer-developed rules and come up with its own, potentially better, way of solving problems.
More recent approaches to machine learning are focusing highly on pattern recognition. This is especially the case in fields like medicine and transportation with the goal of creating fully autonomous machines that can solve problems like diagnosing cancer from a scan or expertly navigating a traffic situation.
Currently, AI needs to be fed information from sensors and image data that has already been labeled and processed by humans. This is an incredibly labor-intensive process. According to Deepen AI, a single hour's worth of driving data can take up to 800 man-hours to label and analyze. However, as technologies improve and the methods of machine learning become more robust, computers will begin to take on the analysis task themselves. Simple prototype traffic cameras are already in place in New York City, and similar programs are expanding to other places.
The iterative method uses an initial guess to generate a series of approximate answers that keep being refined. What makes machine learning particularly strong is its ability to improve over time, creating a final program whose details even experts in the field cannot explain. The key to this is using a dataset to repetitively test the program. For example, a simple program can be created that can differentiate pictures of numbers. Programmers can then test the machine with hundreds of photos, and based on the results of the test, the machine can alter its rules, take the test again, and see if it has improved.
Machine learning is becoming a growing part of the technology field, and its applications are only increasing each year. Ultimately, machines will not only learn enough to help us with day-to-day problems but will also understand how to teach themselves new techniques to keep up with human needs.
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