Artificial Intelligence is an extremely broad term, used to describe many aspects of machines that can mimic cognitive functions of the human mind. There are at least several definitions for this concept. It is not surprising, therefore, that many inaccuracies have arisen around this phenomenon. Sometimes the term “Artificial Intelligence” is used interchangeably with others, such as machine learning. Although these technologies are related, there are several differences that you should know to use these terms correctly.

Artificial Intelligence – Two Approaches

Artificial intelligence can be understood in two ways. For some technologies, we are talking about a narrow AI. A good example of it are chess programs. These programs are written for a specific purpose – winning at chess. They have no other application, so they are called narrow AI. This type of artificial intelligence is already widely used in car navigation, search engines, or video games. It is, however, rather a set of certain algorithms and rules than real, human-like intelligence. Such a program must first be designed by its creator for a specific purpose so that it can function properly.

For genuine artificial intelligence, scientists use terms like strong or full AI. Such machine would be capable of performing any intellectual task that a human can do. That technology, however, exists only theoretically. Big companies and scientists are working on making that discovery, and machine learning can be the way to do that.

Machine Learning – A Solution?

As defined by Stanford University, “machine learning is the science of getting computers to act without being explicitly programmed.” This technology has already found many uses. Machine learning allowed the creation of self-driving cars, revolutionized speech recognition, web search effectiveness, and even improved understanding of the human genome.

In this case, we are also dealing with two approaches. Supervised learning requires training – in other words, a human has first to put in the system data that is already somehow categorized. Based on this input, the computer can further analyze and classify data. Researchers are also working on the development of unsupervised learning, that does not require training. This technology requires a tremendous amount of data and powerful computing power so that the program is able to draw conclusions from the input itself.

AI vs. Machine Learning?

AI covers many different issues of intelligent machines. Machine learning, on the other hand, focuses on specific aspects such as statistics, data mining and predictive analysis. This leads to the conclusion that machine learning should perhaps be treated as a separate branch of computer science. The main goal of AI research is to develop elements such as perception, planning, learning, problem-solving, communication and integrate them into a coherent system. At the same time, machine learning concentrates only on some aspects of that issue. ML systems can work without some AI features.

Narrow AI and supervised machine learning are similar concepts. In both cases, the system needs to be designed for a specific purpose. Unsupervised learning opens completely new opportunities. That is why this invention raises so much interest. Creating such a machine is the next step on the path to full artificial intelligence.

Although machine learning and artificial intelligence are closely related from the very beginning, each of these terms has unique meaning. You have to keep that in mind, even if sometimes they are used interchangeably. And since this branch of science is constantly evolving, we can expect that soon new terms will come up and add even more to the confusion.

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