Artificial intelligence or AI and machine learning are often used interchangeably. However, a deeper understanding of the two terms reveals that they are not quite the same. The common perceptions can give rise to confusion. However, since AI and ML seem to be the future, it seems imperative to understand them better.
The basic difference
Machine learning refers to the ability of machine algorithms to master a particular skill or acquire knowledge via experience. In the case of machine learning, big data sets are fed into the appliance, which allows it to determine common patterns.
For instance, the ML program is provided with images of skin conditions and their meanings. The algorithm of the application identifies patterns between the pictures and uses the information given to analyze skin conditions in the future. If later, a new skin image is fed to the program, the algorithm will compare it with the existing images to determine that the skin condition of this new picture.
Artificial intelligence is a bit different. In this case, the program acquires the knowledge and learns its application. The purpose of AI is to find the best possible solutions. It is the study wherein computers are trained to perform actions that humans do. The use of AI primarily comes into picture when adapting to new situations is required.
AI identifies the problems and looks for solutions. This phenomenon can better be compared to the working of the brain. Just like your mind analyzes particular issues and looks for a way out, computers with AI do the same.
Therefore, it is apparent that there is quite a difference between these two terminologies. While one uses experience and identifies patterns, the other uses analysis to reveal results. Both are considered valuable for technology with diverse applications.
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