Artificial intelligence and machine learning are the concepts that we frequently experience both in daily and business life. Since we hear these technologies together most of the time, they are sometimes confused with each other.
However, these two concepts do not actually define the same functions. In this article, we explain definitions and differences between artificial intelligence and machine learning.
Intelligence developed by humans: Artificial intelligence (AI)
“Artificial” is used for describing the things made or produced by humans as a copy of something natural rather than naturally occurring. “intelligence”, on the other hand, means the ability to understand and think. The combination of these two words “artificial intelligence” is a concept that analyses human thinking methods and develops artificial instructions similar to these methods. In other words, it can be defined as the ability of computers to perform tasks as effective as humans. AI is not a system; it is integrated into systems. It supports machines to solve complex problems in a human-like way. Briefly, AI provides to solve problems, which normally require human intelligence, through computers.
When did the “artificial intelligence” concept emergence?
With the emergence of computer technologies, discussions around AI have started with the question of “Can machines think?” asked by Mathison Turing. However, the concept of AI was first put forward and defined in the conference of “Dartmouth College Artificial Intelligence” in 1956 under the leadership of John McCarthy. The first AI laboratory was established in 1959 at the Massachusetts Institute of Technology (MIT) by John McCarty and Marvin Minsky.
Can machines learn? : Machine learning (ML)
If you think that machine learning is another definition of artificial intelligence, you make a serious mistake. Machine learning is a branch of artificial intelligence technically, but it indicates the specific field of AI studies. It allows machines to learn on their own without explicitly programming and continuous control. ML provides a system to learn automatically and improve itself from previous experiences. ML has evolved through breakthroughs in AI. The first breakthrough was to teach computers how to learn and how to perform each possible task by realizing that it would be more efficient to give computers the information which is needed to complete tasks. The second crucial breakthrough, as you can imagine, is the invention of the internet. The invention of the internet provided a huge potential for information storage in an unprecedented way. After this breakthrough, machines were able to access the amount of data that they normally could not access due to storage limitations.
With the increase of data that computers should process, it has become more efficient to encourage them to “think for themselves” rather than “make machines do something”. Machine learning can be applied in many business scenarios that depend on hundreds of factors, where human-powered solutions are impossible to process. For example, businesses can use machine learning to predict loan defaults, understand factors that lead to customer churn, detect possible fraudulent transactions, optimize insurance compensation processes and many other situations. Companies, which implement machine learning and other artificial intelligence technologies in business processes, gain a considerable competitive advantage.
If we look at the differences of these two concepts in general terms;
• Artificial intelligence is decision-making, on the other hand, machine learning enables the system to learn new things from data.
• While the purpose of artificial intelligence is to simulate natural intelligence to solve a complex problem, machine learning aims to learn from data related to a specific task to maximize the machine’s performance in that task.
• Artificial intelligence enables the development of a system that will imitate a human reaction to a situation. Machine Learning creates self-learning algorithms.
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