Testing Machine Intelligence
Alan Turing was an English computer scientist who famously took part in decrypting the enigma machines that the Germans used to communicate during World War II. After the war, he set his sights on early computers. In particular, he was interested in how machines might be able to think.
In a 1951 paper, he proposed a test called the imitation game that was based on a Victorian parlor game. In the game, a man and woman sat in one room, and their interrogator sat in another (Figure 1.1).
The interrogator would ask the man and the woman a question. Then the team would pass back their answers in a written note. It was up to the interrogator to decide if each written answer came from the man or the woman. In an added twist, the man tries to fool the interrogator, whereas the woman tries to help.
Now, to a modern ear, this game sounds dreary and misogynistic. But to Turing, this was an excellent foundation to test a machine’s intelligence. He imagined an updated imitation game where the man was replaced by a machine (Figure 1.2).
Then the interrogator would ask both the woman and the machine a question and get back their answer in a written note. If the interrogator was just as likely to pick one or the other, then the machine must be seen as intelligent. This game was later known as the Turing test.
This test sparked a lot of curiosity in an “imaginable machine” even though it came out a few years before McCarthy even coined the term artificial intelligence. Even after nearly 70 years, this test still sounds intriguing. Imagine if you could ask a machine a question in your own natural language and get a response that is indistinguishable from that of another human?
That being said, most experts agree that the Turing test is not necessarily the best way to gauge intelligence. For one, it depends a lot on the interrogator. Some people might be easily fooled into thinking that they’re talking to another person. It also assumes that AI will be similar to human intelligence. You would assume that a machine would be able to have a decent conversation before it started performing an advanced task such as searching for new drugs or accurately predicting global weather patterns.
Yet the Turing test still inspires a lot of innovation. Companies still try to create intelligent chatbots, and there are still NLP competitions that attempt to pass the test. It seems like modern machines are only a few years away from passing the Turing test. Many modern NLP applications can accurately understand the majority of your requests. Now they just need to improve their ability to respond.
Yet even if a machine can pass the test, it still seems unlikely that that same machine would be seen as intelligent. Even if your smart phone can trick you into thinking you’re talking to a human, that doesn’t mean that it will offer meaningful conversation.