September 2020 David Chu
These days, both Artificial Intelligence (AI) and Machine Learning (ML) are extremely popular concepts. They are frequently mentioned in all types of media and in various contexts: from reviews of the best AI apps to scientific projects employing ML algorithms.
The presence of these concepts became so overwhelming that, at some point, people started to mix them up and use them interchangeably. Though they seem similar and often quite complex for a layperson, AI and ML are different in some key aspects, including their scope, history, and application.
It is understandable why people keep mixing up Machine Learning and Artificial Intelligence. First, they are both highly advanced technologies. Second, they are hot topics that make headlines. And third, they relate to computers operating on their own, like living beings. And people are accustomed to associating intelligent behavior with learning processes based on their experience. And to a certain extent, machine learning resembles the process of education in schools.
To define and compare the differences, it is necessary to examine both Artificial Intelligence and Machine Learning as separate concepts. This will give a better understanding and prevent confusion.
Artificial Intelligence is a much older and broader idea than Machine Learning. Ancient myths popularized the idea of mechanical human-like creatures possessing rudiment intelligence and performing the bidding of their masters. Luckily for mankind, the real-life implementation of AI has evolved far past those automatons and golems.
Computer logic is the foundation of the AI in its modern form. Initially, arithmetic machines – the predecessors of computers – were built as imitations of a human brain that has memory and is only capable of performing calculations. For many years, AI was based on a rigid set of rules that predetermined the functions and behavior of a machine. However, as Artificial Intelligence developed and became capable of performing increasingly complex calculations, it still could not think “outside the box.” It had to stay within preset rules, no matter how elaborate they were getting over the years.
Eventually, the evolution of Artificial Intelligence split into two directions. One of them is more traditional and focused. It continues the conventional idea that AI has to function within a certain scope defined by its application. For instance, an AI chatbot can maintain a conversation only on a preset topic it is dedicated to. A software program for automated bitcoin trading cannot switch to other activities and function as a customer support bot, for example.
The other direction is more general in its nature. According to this concept, AI is perceived as a “jack of all trades” that can develop the required skills to handle any task. Thus, its potential is limitless, which results in breathtaking possibilities for the future of humanity. This approach to Artificial Intelligence spurred the appearance of Machine Learning.
According to one popular belief, ML is just one of many subordinate areas of Artificial Intelligence. However, some researchers view Machine Learning as a current trend, a state of affairs in the field of AI. One thing is certain: the idea of Artificial Intelligence at some point during its evolution gave birth to the idea of Machine Learning, but now ML has become the driving force for AI.
The philosophy behind Machine Learning is rather simple: do not teach the machines what they should do but rather teach them to learn from the information they come across. However, before the digital era, this was more of a wild dream rather than a realistic idea. All knowledge accumulated by people was stored in the analog form, and it was very difficult to use it for teaching a computer. Only after the Internet became an integral part of our lives, machines got enough material for learning.
The digitalization of data that has affected all aspects of life has fueled the development of Machine Learning. Industries and sciences were subjected to the digital transformation that made massive amounts of information easily accessible. Moreover, the new format of data could be effectively used by computer systems.
The last factor of the rise of Machine Learning was the appearance of new programming instruments: languages, libraries, frameworks, etc. At last, coders got the proper tools to program computers to think like humans. This was the prerequisite for the successful learning process based on accessing and analyzing data.
This being said, the concept of Machine Learning is more efficient but also somewhat dangerous. The programmers give a machine the ability to learn, and then it uses its algorithms to process the information it has access to, develop patterns, make decisions, etc. However, machines do not have moral features and do not distinguish between right and wrong. That is why all ML processes must be closely monitored and supervised.
As you can see, the difference between AI and ML is quite evident after a close examination of these two concepts. Artificial Intelligence is the broader idea and the “parent” of Machine Learning. ML is an area of AI that opens virtually endless possibilities for machines by giving them an ability to absorb knowledge by themselves. As opposed to the traditional approach to AI with strict rules and preset decisions, ML presents a certain form of independence, if not free will.