In the past few years, artificial intelligence and machine learning have frequently appeared on technical news and various websites. Both are often used as synonyms, but many experts believe that they have subtle and significant differences.
Of course, the experts themselves sometimes disagree about what the difference is.
In general, however, two things seem clear: First, the term artificial intelligence (AI) has a history earlier than machine learning (ML); second, most people think that machine learning is a subset of artificial intelligence.
One of the most clearly shown figures for this relationship comes from the official blog of Nvidia. It provides a good starting point to help understand the difference between artificial intelligence and machine learning.
Artificial Intelligence vs Machine Learning - First, what is artificial intelligence?
Computer scientists have many different definitions of artificial intelligence, but at its core, artificial intelligence includes machines that think like humans. Of course, it is difficult to determine if the machine is "thinking." So in fact, building artificial intelligence requires building a computer system that is good at dealing with the kind of work that humans are good at.
The idea of ​​creating a machine as smart as humans can be traced back to the ancient Greeks, when the myth of creating automata was circulated. In reality, however, the idea was not really popular until 1950.
That year, Alan Turing published a groundbreaking paper entitled "Computers and Intelligence" that raised questions about whether a machine would think. He proposed the famous Turing test, which actually claimed that if humans could not judge whether they were interacting with humans or interacting with machines, they could be said to be intelligent machines.
The term artificial intelligence was created in 1956 by John McCarthy, who organized an academic conference in Dartmouth to discuss this topic. After the meeting, participants suggested further research "this speculation that every aspect of learning or any other feature of intelligence can in principle be described very accurately so that the machine that simulates it can be developed. How to make machines use language, formal abstractions and concepts to solve the problems that are now left to humans to solve and improve themselves."
This proposal foreshadows many of the topics of great interest in the field of artificial intelligence today, including natural language processing, image recognition and classification, and machine learning.
In the years after the first meeting, artificial intelligence research flourished. However, in a few decades, it is clear that the technology that builds machines that truly believe in independent thinking will come out many years later.
But in the past decade, artificial intelligence has moved from the field of science fiction to the field of scientific facts. The news media has long reported that IBM's Watson artificial intelligence technology won the quiz TV show "Dangerous Edge" and Google's artificial intelligence technology beat the human champion in the Go game, which brings artificial intelligence back to the public eye.
Today, all of the largest technology companies are working on artificial intelligence projects, and most of us are exposed to artificial intelligence software every day, such as using smartphones, social media, Internet search engines or e-commerce sites. One of the most common types of artificial intelligence we are exposed to is machine learning.
Artificial Intelligence vs Machine Learning - Ok, then what is machine learning?
The phrase "machine learning" can also be traced back to the middle of the last century. In 1959, Arthur Samuel defined machine learning as "the ability to learn without explicit programming." To this end, he developed a computer-checking program that learned from his own mistakes. One of the early programs that continually improve performance.
Like artificial intelligence research, machine learning is not popular for a long time, but when the concept of data mining became popular in the 1990s, it became popular again. Data mining uses algorithms to find patterns in a certain set of information. Machine learning does the same thing, but goes a step further—it changes the behavior of the program based on what you have learned.
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