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Machine learning and AI development are among the hottest topics in the world of IT. More and more often we are reminded of these concepts not only at conferences or shows but also in our everyday lives. The demand for proficiency in these fields is growing as they offer almost unlimited perspectives. What is the difference between AI and machine learning? Let us explain.
Although the term AI dates back to the 1950s, the world had to wait a long time before the capabilities of this technology could actually be put to use. Now, after more than 50 years, things are much different. Today, programmers use equipment offering unparalleled computing power and have access to fast internet connections and gigantic sets of data. This opens up countless new possibilities.
The term artificial intelligence is composed of two words. Artificial means something produced by humans or in an otherwise non-natural manner. Dictionaries describe it as something created by humans to replace a natural equivalent. Intelligence means the ability to understand, learn and use acquired knowledge in new situations.
In a nutshell, AI is the field dedicated to teaching computers how to solve problems in the same way humans do – through intelligence. Whereas traditional software is coded in a way that very clearly and unambiguously shows how to solve particular problems, AI works differently. The use of this technology involves providing very little or even no concrete guidelines. The intelligence has to deal with the problem on its own. The aims of AI include:
American scholar Herbert Simon defined learning as systemic changes which are adaptive in the sense that they allow the system to repeatedly perform the same or similar tasks more efficiently.
Machine learning is a field that combines mathematics, robotics, statistics and information technology. Its purpose is to create complex algorithms capable of self-improvement by drawing on previously acquired experience. Machine learning is therefore the result of the development of artificial intelligence.
The aims of machine learning include:
Algorithms which form the basis of machine learning operate on a dynamic model which processes input data to make concrete decisions. Despite its complexity, the mechanism of machine learning has serious limitations. The entire operation of processing data is very much reliant on the person supervising the process. This person assists the machine by manually entering data, eliminating system locks and verifying process statuses. Computer autonomy is therefore still limited. One concept emerging as the solution to this problem is deep learning – but we will come back to it in a different post.
The invention of both machine and deep learning brought about a slew of new applications for artificial intelligence. Perspectives for its use are extremely broad and cover such fields as the automotive industry (autonomous vehicles), medicine, finance, e-commerce and many more. Artificial intelligence is rapidly becoming our present reality. Investing in it – both in learning how to create it and how to use it in business – is more than just a hot trend. First and foremost, it’s a profitable business and career undertaking which can bring immediate tangible benefits.
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