It is not our intention in this book to give a detailed life history and background to Alan Turing in all its complexity with the many issues that it raised. This is done very well elsewhere, for example in Andrew Hodges’ excellent biography of Turing (Hodges, 1992). What this book is about is Turing's imitation game, pure and simple.

However the game, which came into existence in the late 1940s and was fine tuned in the early 1950s, continues to spark a plethora of misunderstandings, arguments and much controversy particularly with regard to its philosophical context. As a result we feel that it is well worthwhile taking a look at some aspects of Turing's life so that we can get to grips with the game a little better.

In order for us to comprehend the particular phenomenon that is the imitation game, what we try to do in this chapter is to understand more about the actual person and what he was like. We do this by drawing on some of his lectures, on some of the biographies about him and through the comments of people that knew him. Hopefully with this background we will be able to get to grips with what the imitation game is really all about.

Bletchley Park

During the Second World War, Turing worked in Bletchley Park, the UK's main decryption centre, which subsequently became famous as the place the code for the German Enigma machine was cracked. Some of the UK's leading mathematicians were brought together in one place and Turing was one of them.

Turing had realized early on that a large part of deciphering amounted to performing a whole series of manipulations and calculations in an automated fashion and that this could be achieved much better by a machine than by a human, partly because the machine could keep working on the problem without sleep and partly because it didn't make mistakes. Along with Gordon Welchman (see Davies, 1999), who like Turing had previously been at Cambridge University, he redesigned a Polish electromechanical cipher-breaking machine and, in 1940, they called their device the Bombe. In fact it consisted largely of rows and rows of uniselectors which were the standard telecommunications technology at the time for telephone routing.

As we already mentioned, to realise Turing's tests is, in the opinion of Hayes and Ford (1995), harmful to the science of AI. We contest this position and feel it is a dereliction of the duty of science whose remit should not be to avoid difficult goals or to appease the sceptical. Science should pursue innovation and advance technology for the benefit of humanity.

If realising Turing's two tests of imitation, deception and intelligence can help us ascertain what does and does not fool people, thus improving deception detection, then this cannot be contrary to the goals of good science. Especially as many researchers (including Block, Pinker, and Shieber) have pointed out and others (Colby et al., Heiser et al., Weizenbaum) have demonstrated through experiments that some intelligent humans are gullible.

The current climate of increasing cybercrime sees opportunists turning to innovative means of defrauding people – stealing their identity, swindling funds – including using text-based chatting across the Internet. So now is a very good time to engineer virtuous artificial conversationalists to counter the attack from malware such as CyberLover. In this chapter we look at some of the common arguments over the Turing test and early Turing test implementations, considering the key questions of duration, knowledge, memory, cultural bias. We begin by asking what if anything is actually being measured.

What is being measured?

Is it intelligence or a type of human intelligence being measured in a Turing test? Turing (1950) believed a sustained level of answering any questions was sufficient to assess a machine's performance in thinking at a satisfactory level. But what then is thinking? To Moor (2004) it is information processing in ways which involve recognition, imagination, evaluation and decision. For Baum (2004) semantics is the concern of thought equivalent to capturing and exploiting the compact structure of the world. Demchenko and Veselov (2008) ask if the proven ability to think shortens the distance between machines and humankind.

For a machine to succeed at providing sustained satisfactory responses in an imitation game these comments imply that a machine would necessarily be able to process information with the sophistication of a normal, living adult human being; that is, the machine must be a consummate actor.

At this stage in the book we take a break from looking at Alan Turing himself and the imitation game and consider the wider field of artificial intelligence (AI). Whilst the game itself has proved to be arguably one of the most iconic and controversial aspects of AI, it is useful, we feel, to assess just how the game fits into the field and perhaps to give some sort of understanding as to why it is so important. We also take a look at such things as natural language processing but we avoid heavy mathematics. Anyone who is already well versed in AI may well wish to move straight to Chapter 4.

Alan Turing is frequently referred to as the father of artificial intelligence. He was around at the dawn of the computer age and was himself directly involved in early computer systems such as the Bombe, which he designed, and the Colossus, on which his work was used. The field of AI itself however was, some claim, first so named after Turing's death, around 1956 (Russell and Norvig, 2012) although in general it could be said to have come into existence as the first computers appeared in the 1940s and 1950s.

In AI's formative years attention was focussed mostly on getting computers to do things that, if done by a human, would be regarded as intelligent acts. Essentially it was very human-centered. When Turing proposed his imitation game in 1950, it was perfectly timed to be grabbed hungrily by the young and burgeoning, soon to become, AI community, particularly those interested in the philosophical aspects of the new field. As was shown in the previous chapter even main stream radio broadcasting was not scared to encompass the topic.

The game and AI

Turing wanted to come up with a realisable concept of intelligence in machines. Rather than give a long list of definitions, many of which would be controversial, or to construct a series of mathematical statements, most of which would be impracticable, he put the human at the centre and used a form of science involving actual experimentation to confirm the hypothesis.

Turing's imitation game, also commonly known as the Turing test, is undoubtedly a key component in any study of artificial intelligence or computer science. But it is much more than this as it also provides insight into how humans communicate, our unconscious biases and prejudices, and even our gullibility. The imitation game helps us to understand why we make assumptions, which often turn out to be incorrect, about someone (or something) with whom we are communicating and perhaps it helps to shed light on why we sometimes make seemingly irrational conclusions about them.

In the chapters ahead we'll look at the game in much more detail; however in essence it involves a direct conversational comparison between a human and a machine. Basically, the goal of the machine is to make you believe that it is in fact the human taking part, whereas the human involved is merely being themselves. Both the human and the machine are hidden, so cannot be seen or heard. The conversation is purely textual with slang, idiom, spelling mistakes, poor grammar and factual errors all being part of the mix.

If you put yourself in the role of an interrogator in the parallel test then it is your job to converse with both a hidden human and a hidden machine at the same time and, after a five-minute period as stipulated by Alan Turing himself, to decide which hidden entity is which. If you make the right identification then that's a point against the machine whereas if you make a mistake or you simply don't know which is which then that's a point for the machine. If a machine is successful in fooling enough average interrogators (one interpretation of Turing's 1950 work is 30%), then it can be said to have passed the Turing test.

Actually restricting the topic to a specific subject area makes it somewhat easier for the machine, because it can direct the interrogation to its knowledgebase. However, Turing advocated that the machine be investigated for its intellectual capacity. Thus we should not restrict the topic of conversation at all, which we believe is an appropriate challenge for machines of today, and which is much more interesting for the interrogator and is in the spirit of the game as (we feel) Turing intended.

We presented in this book Alan Turing the man before taking the reader on a journey through the prescient beliefs of this mathematical genius, WWII code-breaker and all-round polymath. From his earliest works, and notions about thinking machines, to implementations of his thought experiment about a machine's ability to answer any questions put to it by a human interrogator, you can see how Turing's ideas are as relevant today as when he originally described them.

Consider the World Economic Forum (WEF) annual gathering of world leaders in Davos, in 2016. The Fourth Industrial Revolution was one of the eight themes of the conference with technology at the forefront of discussions. These included ‘The Transformation of Tomorrow’, ‘What if robots go to war?’ and the ‘State of artificial intelligence’. Nearly 70 years before this, in his 1948 report, Intelligent Machinery, Turing first proposed his test.

As we have seen, machines can now converse improving on the Eliza program's technique to transform an input message into an output question, thereby getting human interlocutors to talk about themselves (see Chapter 9). Of course there remain sophistications in human language that need mastering in machine talk; for example, creating metaphors and analogies to explain unexpected or unusual occurrences through similar or common experiences. This will take time to develop, especially to understand more fully how humans do it.

Do we feel then that the three practical Turing test experiments, especially the 2014 event, realised the kind of thinking machine Turing might have envisaged in 1948? Of course not, … yet. A stronger test would last for longer than five minutes and a machine would need to convince more than half a panel of interrogator–judges that it was human, as Turing felt in 1952, two years after his prediction in the Mind paper.

The three experiments, 2008, 2012 and in 2014 were a start in seriously examining Turing's ideas for a thinking machine. An unpredicted backlash followed the announcement of machine Eugene Goostman's performance in the 2014 Turing test experiment.

Just as Turing had predicted more than half a century before, academics like Stevan Harnad were unimpressed. Harnad tweeted his doubt that the test had actually been passed. Criticism also came from the psychologist and linguist Gary Markus.

Turing's imitation experiment can be regarded as a:

1. (a) A game for judges: to avoid being persuaded by a machine that it is human;

2. (b) A game for the machines: to persuade the judges that they/it are the human;

3. (c) A game for hidden humans: be human with all the constraints about not revealing personal identity;

4. (d) A game for the observer, to study and compare the results.

It is worth pointing out here that turning Turing's idea into a practical

means of examining machine thinking is fraught with problems, for example:

1. (a) matching the machines with ‘like’ humans for simultaneous comparison or in a viva voce experiment;

2. (b) lack of resources for conducting psychological tests; personality and typing speed of the Judges and hidden humans.

In the case of Eugene Goostman, a machine which simulates an English speaking young teen from Odessa, Ukraine, it is not difficult to pair the machine with a human teenager, the pair being interrogated by a teenage judge. However, what is difficult is recruiting the exact match: a teenager from the Ukraine who speaks English.

At the other end of the machine conversation spectrum, Elbot is a machine with a robot personality; it would be futile to recruit a human to act like a robot, because the Turing test is concerned with the hidden entities providing satisfactory and sustained answers to any questions.

When it comes to testing the personality of human interrogator judges and the hidden humans or checking for characteristics such as typing speed, it would add an interesting dimension to the experiment.

A Turing test is a scientific experiment in that a set of conditions, the duration of tests, the number/nature of participants – human or machine, can be put in place for observation and measurement, and be repeatable. Adding new features, therefore, would also mean new challenges.

In Chapter 8 we presented the Turing test experiment at Bletchley Park in 2012. That was concerned mainly with finding which of Turing's two scenarios for implementing his imitation game – the one-to-one viva voce or the simultaneous comparison – was harder for the machine when trying to convince the human judges that it was a human.