A Brief History

The first research into an artificial brain emerged between the 1940s and 1950s. Alan Turing, one of the mathematicians involved in the early development of A.I., published an article in 1950 on the possibility of creating machines endowed with real intelligence. The test that bears his name consists of having a machine engage in a conversation without being distinguishable from a conversation with a human being; if the test is passed, the machine can be described as “intelligent.” In 1956, artificial intelligence became an academic discipline during the Dartmouth Conference.

In the years following this conference, the field experienced rapid growth. Programs developed at the time were considered “extraordinary,” and many researchers believed that a fully intelligent A.I. would be created within the next 20 years. At the same time, companies and government agencies invested heavily in the field.

In the 1970s, A.I. research slowed significantly due to criticism and budgetary issues, as researchers faced unexpected difficulties.

The 21st century marked a turning point in artificial intelligence research. The first “intelligent” personal assistants appeared in 2007 with Apple’s Siri, followed by Google Now in 2012. Today, numerous A.I. systems exist in all fields (conversational A.I., finance, healthcare, manufacturing, automotive industries, etc.), and nearly half of the articles found on the web have been written by artificial intelligence.

What Is A.I.?

A.I. is often associated with an enhanced search engine, yet it does more than simply regurgitate stored data. It rather imitates the functioning of the human brain. In this sense, it can solve complex equations beyond its own capabilities thanks to access to thousands of resources. It can also recognize shapes, images, and sounds, automatically translate texts, and reproduce dialogues between two people. There are symbolic A.I. systems based on rules defined by humans, as well as machine learning (more widely used because it is more efficient in many cases), which includes deep learning. When people talk to A.I., they usually refer to this last approach, which involves learning from large amounts of information in order to make predictions.