Countless articles discussing AI are published every day. The trend has increased in the last two years. There are several definitions of AI floating around the web, my favorite being the automation of intellectual tasks normally performed by humans.
Artificial intelligence
The history of AI
The term artificial intelligence was first coined by John McCarthy in 1956, when he held the first academic conference on the subject. The journey of the question of whether machines think or not started much earlier than that. In the early days of AI, machines were able to solve problems that were difficult for humans to solve.
For example, the Enigma machine was built at the end of World War II to be used in military communications. Alan Turing built an AI system that helped to crack the Enigma code. Cracking the Enigma code was a very challenging task for a human, and it could take weeks for an analyst to do. The AI machine was able to crack the code in hours.
Computers have a tough time solving problems that are intuitive to us, such as differentiating between dogs and cats, telling whether your friend is angry at you for arriving late at a party (emotions), differentiating between a truck and a car, taking notes during a seminar (speech recognition), or converting notes to another language for your friend who does not understand your language (for example, French to English). Most of these tasks are intuitive to us, but we were unable to program or hard code a computer to do these kinds of tasks. Most of the intelligence in early AI machines was hard coded, such as a computer program playing chess.
In the early years of AI, a lot of researchers believed that AI could be achieved by hard coding rules. This kind of AI is called symbolic AI and was useful in solving well-defined, logical problems, but it was almost incapable of solving complex problems such as image recognition, object detection, object segmentation, language translation, and natural-language-understanding tasks. Newer approaches to AI, such as machine learning and DL, were developed to solve these kinds of problems.
To better understand the relationships among AI, ML, and DL, let's visualize them as concentric circles with AI—the idea that came first (the largest), then machine learning—(which blossomed later), and finally DL—which is driving today’s AI explosion (fitting inside both):
How AI, machine learning, and DL fit together
