Turing Test in AI?

The Turing test is a central, long term goal for AI research – will we ever be able to build a computer that can sufficiently imitate a human to the point where a suspicious judge cannot tell the difference between human and machine? From its inception it has followed a path similar to much of the AI research. Initially it looked to be difficult but possible (once hardware technology reached a certain point), only to reveal itself to be far more complicated than initially thought with progress slowing to the point that some wonder if it will ever be reached.

Despite decades of research and great technological advances the Turing test still sets a goal that AI researchers strive toward while finding along the way how much further we are from realizing it.

Inception of the Turing Test

In 1950 Alan Turing was the Deputy Director of the computing laboratory at the University of Manchester. The paper which defined what would come to be known as the Turing test was published in a Philosophical journal called Mind. The paper itself was based on the idea of an ‘Imitation Game’. If a computer could imitate the sentient behavior of a human would that not imply that the computer itself was sentient? Even though the description itself is fairly simple, the implications of building a machine capable of passing the test are far reaching. It would have to process natural language, be able to learn from the conversation and remember what had been said, communicate ideas back to the human and understand common notions, displaying what we call common sense. Similar to how he used the Turing Machine to more clearly formalize what could or could not be computed, Alan Turing felt the need to propose the Turing Test so that there was a clear definition of whether or not the responses given by a human were part of the computable space. In the paper he wanted to replace the question, ‘Can machines think?’ (which can have many possible answers and come down to a difference of opinion) with a version of the ‘Imitation Game.’

The original game upon which Turing’s idea was based required a man, a woman and an interrogator. The goal was for the interrogator to identify which of the participants was a man and which was a woman. Since the interrogator would be able to identify the gender of the respondent by their voice (and maybe handwriting) the answers to the interrogator’s questions would be type written or repeated by an intermediary. For the Turing Test, one of those two participants would be replaced by a machine and the goal of the interrogator would not be to identify the gender of the participants, but which is human and which is a machine. As described above, the Turing Test has a few key components that in effect define what Turing means when he wonders if machines can think. First the interrogator knows that there is one human and one machine. The test doesn’t just require a computer to fool a human into thinking it is sentient; it asks the computer to fool a suspicious human. Second, physical nature isn’t important – the goal is to not be able to tell the difference between man and machine when comparing the output of the machine and the true human.

The communication medium is such that there are absolutely no hints beyond what can be expressed with written language. Also, the test doesn’t include anything specific – no complex problem solving or requests to create art. As described, it seems a machine would pass the Turing test if it were able make small talk with another human and understand the context of the conversation. For Turing, passing such a test was sufficient for him to believe that machines were capable of thinking.

Problems/Difficulties with the Turing Test

A large portion of Turing’s original paper deals with addressing counter arguments concerning how the test he proposes may not be valid. In the introduction to that section he states that he believes there will be computers with enough storage capacity to make them capable of passing the Turing test “in about fifty years”. The statement is interesting because it seems to imply that the AI software required to pass the Turing Test would be 7 rather straightforward and that the limiting factor would only be memory. Perhaps this limitation was at the front of his mind because he was routinely running into problems that he could have solved if only there were enough storage available. The same type of reasoning is similar to what happens today when we believe that Moore’s law will let us solve the hard problems.

Beyond the storage limitations, he also raises other objections, including those based in theology (the god granted immortal soul is necessary for sentience), mathematical arguments based on Godel’s work, the ability for humans to create original works and experience emotion, and others.

One of the more interesting contradictions to the test is what he terms ‘The Argument from Consciousness.’ The argument goes that just imitating a human would not be enough because it doesn’t invoke the full range of what it is that we consider to be human. Specifically, the Turing Test could be passed by a machine unable to do things such as write a poem or piece of music wrapped up as part of an emotional response. A machine passing the Turing test would not really have to experience or interpret art either. Turing argues that it is impossible to tell if the machine is feeling unless you are the machine, so there is no way to contradict the claim or to prove it. Using that method to dismiss the argument, he points out that the Turing test could include the machine convincing the interrogator that it is feeling something, even if there is truly no way to know that the emotions are actually being felt the way they would in a human. This would be similar to how humans communicate to convince each other of what they are feeling, though there is no guarantee that it is really true.

Another interesting counter argument against the test that Turing describes is ‘Lady Lovelace’s Objection.” She posited that because machines can only do what we tell them, they cannot originate anything, while it is clear that humans do originate new concepts and ideas all of the time. At the time this was written it may not have been possible to model the learning process, but much of the progress that has been made in teaching machines to learn and infer seems to have shown that this issue can be overcome. There have been specific implementations where voice or character recognition is reached by software training itself to recognize the variances in human writing or dialect. At least in these specific cases a machine can recognize something new so perhaps they will be able to in the general case as well.

Overall the potential problems with the Turing test appear to fall in one of two categories:

• Does imitating a human actually prove intelligence or is it just a hard problem
• Is intelligence possible without passing the Turing test

It seems fair to say that passing the Turing test is only a subset of the situation that humans have to contend with on a day to day basis. So it is possible that there are other key capabilities like experiencing emotions, having core beliefs or motivations, or problem solving that might be simulated in a computer but would not necessarily be the same as what humans do.

The Turing test avoids these questions by judging the computer (and human) only on the text they output as part of the casual conversation that takes place during the test. So even if a computer could pass the Turing test, is that enough to say machines are ‘intelligent’ or that they can ‘think’, or does that just say that they can now pass the Turing test, and there is much more to understand before we do consider them intelligent.

Beyond that, there are many humans that we’d consider sentient – young children for instance, that would probably do poorly in the Turing test because they haven’t accumulated enough knowledge and experience in communication. We wouldn’t apply the Turing test to them and say that they therefore are not capable of thought, which means that it might be possible for a computer to ‘think’ but still not pass the Turing test.

Alternatives to the Turing Test

Many people have proposed their own version of the Turing test to help contend with the perceived or possible shortcomings of the test as proposed by Alan Turing. Most of the alternatives either narrow the scope of the test to make it easier to pass (a more reachable goal), or shift the scope to an area where researchers might make better progress. One alternative, called the Feigenbaum Test, avoids the issues that make it difficult for a 8 computer to communicate in a causal manner as is done in the Turing test. The Feigenbaum test asks the computer to be able to pass as an expert in a particular field, essentially setting a mark for when technology like that in Expert systems has matured.

This test definition does a couple of things – while eliminating the casual, anything goes, nature of the Turing interrogator, the test now requires that the computer be able to solve problems that another expert would be able to solve. In some ways the test is harder, given the expert problem solving portion, while being easier in others where the casual conversation isn’t needed.

Another variation on the Turing test was proposed by Nicholas Negroponte, one of the co-founders of the MIT Media Lab. In his test he determines that the value in a ‘thinking’ computer is its ability to work together with a human instead of passing an interrogation by a human. Rather than have a human interrogate the machine to see if it is a human as well, the test would be whether or not the machine could help the human reach his or her goals in the same way another human would. This test was believed to be more difficult because the computer would need an even better ability to understand the vagueness or incorrectness of what is communicated to it by the human.