Original article by JF Puget here.
Here is a question I was asked to discuss at a conference last month: what is Artifical Intelligence (AI)? Instead of trying to answer it, which could take days, I decided to focus on how AI has been defined over the years. Nowadays, most people probably equate AI with deep learning. This has not always been the case as we shall see.
Most people say that AI was first defined as a research field in a 1956 workshop at Dartmouth College. Reality is that is has been defined 6 years earlier by Alan Turing in 1950. Let me cite Wikipedia here:
The Turing test, developed by Alan Turing in 1950, is a test of a machine’s ability to exhibit intelligent behaviorequivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation is a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel such as a computer keyboard and screen so the result would not depend on the machine’s ability to render words as speech. If the evaluator cannot reliably tell the machine from the human, the machine is said to have passed the test. The test does not check the ability to give correct answers to questions, only how closely answers resemble those a human would give.
The test was introduced by Turing in his paper, “Computing Machinery and Intelligence“, while working at the University of Manchester(Turing, 1950; p. 460). It opens with the words: “I propose to consider the question, ‘Can machines think?'” Because “thinking” is difficult to define, Turing chooses to “replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.” Turing’s new question is: “Are there imaginable digital computers which would do well in the imitation game?” This question, Turing believed, is one that can actually be answered. In the remainder of the paper, he argued against all the major objections to the proposition that “machines can think”.
So, the first definition of AI was about thinking machines. Turing decided to test thinking via a chat.
The definition of AI rapidly evolved to include the ability to perform complex reasoning and planing tasks. Early success in the 50s led prominent researchers to make imprudent predictions about how AI would become a reality in the 60s. The lack of realization of these predictions led to funding cut known as the AI winter in the 70s.
In the early 80s, building on some success for medical diagnosis, AI came back with expert systems. These systems were trying to capture the expertise of humans in various domains, and were implemented as rule based systems. This was the days were AI was focusing on the ability to perform tasks at best human expertise level. Success like IBM Deep Blue beating the chess world champion, Gary Kasparov, in 1997 was the acme of this line of AI research.
Let’s contrast this with today’s AI. The focus is on perception: can we have systems that recognize what is in a picture, what is in a video, what is said in a sound track? Rapid progress is underway for these tasks thanks to the use of deep learning. Is it AI still? Are we automating human thinking? Reality is we are working on automating tasks that most humans can do without any thinking effort. Yet we see lots of bragging about AI being a reality when all we have is some ability to mimic human perception. I really find it ironic that our definition of intelligence is that of mere perception rather than thinking.
Granted, not all AI work today is about perception. Work on natural language processing (e.g. translation) is a bit closer to reasoning than mere perception tasks described above. Success like IBM Watson at Jeopardy, or Google AlphaGO at Go are two examples of the traditional AI aiming at replicate tasks performed by human experts. The good news (to me at least) is that the progress is so rapid on perception that it will move from a research field to an engineering field in the coming years. We will then see a re-positioning of researchers on other AI related topics such as reasoning and planning. We’ll be closer to Turing’s initial view of AI.