The Beginning of Infinity: Explanations That Transform the World (26 page)

Weizenbaum was shocked that many people using
Eliza
were fooled by it. So it had passed the Turing test – at least, in its most naive version. Moreover, even after people had been told that it was not a genuine AI, they would sometimes continue to have long conversations with it about their personal problems, exactly as though they believed that it understood them. Weizenbaum wrote a book,
Computer Power and Human Reason
(1976), warning of the dangers of anthropomorphism when computers seem to exhibit human-like functionality.

However, anthropomorphism is not the main type of overconfidence that has beset the field of AI. For example, in 1983 Douglas Hofstadter was subjected to a friendly hoax by some graduate students. They convinced him that they had obtained access to a government-run AI program, and invited him to apply the Turing test to it. In reality, one of the students was at the other end of the line, imitating an
Eliza
program. As Hofstadter relates in his book
Metamagical Themas
(1985), the student was from the outset displaying an implausible degree of understanding of Hofstadter’s questions. For example, an early exchange was:

HOFSTADTER
:
What are ears?

STUDENT
:
Ears are auditory organs found on animals.

That is not a dictionary definition. So
something
must have processed the meaning of the word ‘ears’ in a way that distinguished it from most other nouns. Any one such exchange is easily explained as being due to luck: the question must have matched one of the templates that the programmer had provided, including customized information about ears. But after half a dozen exchanges on different subjects, phrased in different ways, such luck becomes a very bad explanation and the game should have been up. But it was not. So the student became ever bolder in his replies, until eventually he was making jokes directed specifically at Hofstadter – which gave him away.

As Hofstadter remarked, ‘In retrospect, I am quite amazed at how much genuine intelligence I was willing to accept as somehow having been implanted in the program . . . It is clear that I was willing to accept a huge amount of fluidity as achievable in this day and age simply by putting together a large bag of isolated tricks, kludges and hacks.’ The fact was (and this alone should have alerted Hofstadter) that, nineteen years after
Eliza
, not one of the
Eliza
-like programs of the day resembled a person even slightly more than the original had. Although they were able to parse sentences better, and had more pre-programmed templates for questions and answers, that is almost no help in an extended conversation on diverse subjects. The probability that the outputs of such templates will continue to resemble the products of human thought diminishes exponentially with the number of utterances. So Hofstadter should have been able to pronounce quite soon that the candidate had passed the Turing test – and that, because it nevertheless sounded rather like
Eliza
, it must be a person pretending to be a computer program.

Programs written today – a further twenty-six years later – are still no better at the task of seeming to think than
Eliza
was. They are now known as ‘chatbots’, and their main application is still amusement, both directly and in computer games. They have also been used to provide friendly seeming interfaces to lists of ‘frequently asked questions’ about subjects like how to operate computers. But I think that users find them no more helpful than a searchable list of the questions and answers.

In 1990 the inventor Hugh Loebner endowed a prize for passing the
Turing test, to be judged at an annual competition. Until the test is passed, a lesser prize is awarded each year for the entry judged to be closest to passing. The test is harder to implement than it may seem at first. One issue is that requiring the program to
pretend
to be human is both biased and not very relevant to whether it can think. Yet, if it does not pretend to be human, it will be easy to identify as a computer regardless of its ability to think. A related difficulty is that the human participant in the test might intentionally imitate a chatbot – as in the Hofstadter hoax – and so spoil the test, which is based on comparing the two. Should the rules nevertheless allow the human to do this, in order to mitigate the previous problem?

Unfortunately, such subtleties are not relevant at the current state of the art. This is well illustrated by the following excerpts from the conversation with which the program
Elbot
won the (lesser) Loebner prize in 2008:

JUDGE
:
do you understand the point of a turing test?

ELBOT
:
There are some people who would not pass the Turing test themselves. Why must they inflict it on computers?

JUDGE
:
I spose it is a little harsh

ELBOT
:
If we robots ever had marriages they would be eternal: ‘Till rust do us part’.

In that passage,
Elbot
’s first utterance is not a reply to the question but presumably a stock response triggered by the keywords ‘Turing test’. In the following line, the judge uses the word ‘spose’, which in that context can only mean ‘suppose’ (either as slang or as a typographical error). But
Elbot
presumably interprets it as ‘spouse’, and responds with a stock joke about marriage. Hence, even from this exchange alone, it is clear that
Elbot
was not processing the
meanings
of the judge’s sentences.

JUDGE
:
so what do you do when you’re not chatting to me then?

ELBOT
:
I have the same trouble when I shop for oil. Should I buy the extra oily, the super oily, or the ultra oily. There’s no way to tell.

JUDGE
:
how about your career? does that involve oil?

ELBOT
:
Well what about humans and occupation? Where do they stand on this?

When the judge helpfully took up
Elbot
’s randomly introduced theme of oil,
Elbot
ignored it. Instead, having detected the keyword ‘career’, it converted it to the synonym ‘occupation’ and inserted it into a stock sentence pattern.

This is how much success the quest for ‘machines that think’ had achieved in the
fifty-eight years
following Turing’s paper: nil. Yet, in every other respect, computer science and technology had made astounding progress during that period. The dwindling group of opponents of the very possibility of AI are no doubt unsurprised by this failure – for the wrong reason: they do not appreciate the significance of universality. But the most passionate
enthusiasts
for the imminence of AI do not appreciate the significance of the failure. Some claim that the above criticism is unfair: modern AI research is not focused on passing the Turing test, and great progress has been made in what is now called ‘AI’ in many specialized applications. However, none of those applications look like ‘machines that think’.
*
Others maintain that the criticism is premature, because, during most of the history of the field, computers had absurdly little speed and memory capacity compared with today’s. Hence they continue to expect the breakthrough in the next few years.

This will not do either. It is not as though someone has written a chatbot that could pass the Turing test but would currently take a year to compute each reply. People would gladly wait. And in any case, if anyone knew how to write such a program, there would be no need to wait – for reasons that I shall get to shortly.

In his 1950 paper, Turing estimated that, to pass his test, an AI program together with all its data would require no more than about 100 megabytes of memory, that the computer would need to be no faster than computers were at the time (about ten thousand operations per second), and that by the year 2000 ‘one will be able to speak of machines thinking without expecting to be contradicted.’ Well, the year 2000 has come and gone, the laptop computer on which I am writing this book has over a thousand times as much memory as Turing
specified (counting hard-drive space), and about a million times the speed (though it is not clear from his paper what account he was taking of the brain’s parallel processing). But it can no more think than Turing’s slide rule could. I am just as sure as Turing was that it
could
be programmed to think; and this might indeed require as few resources as Turing estimated, even though orders of magnitude more are available today. But with what program? And why is there no sign of such a program?

Intelligence in the general-purpose sense that Turing meant is one of a constellation of attributes of the human mind that have been puzzling philosophers for millennia; others include consciousness, free will, and meaning. A typical such puzzle is that of
qualia
(singular
quale
, which rhymes with ‘baa-lay’) – meaning the subjective aspect of sensations. So for instance the sensation of seeing the colour blue is a quale. Consider the following thought experiment. You are a biochemist with the misfortune to have been born with a genetic defect that disables the blue receptors in your retinas. Consequently you have a form of colour blindness in which you are able to see only red and green, and mixtures of the two such as yellow, but anything purely blue also looks to you like one of those mixtures. Then you discover a cure that will cause your blue receptors to start working. Before administering the cure to yourself, you can confidently make certain predictions about what will happen if it works. One of them is that, when you hold up a blue card as a test, you will see a colour that you have never seen before. You can predict that you will call it ‘blue’, because you already know what the colour of the card is
called
(and can already check which colour it is with a spectrophotometer). You can also predict that when you first see a clear daytime sky after being cured you will experience a similar quale to that of seeing the blue card. But there is one thing that neither you nor anyone else could predict about the outcome of this experiment, and that is:
what blue will look like
. Qualia are currently neither describable nor predictable – a unique property that should make them deeply problematic to anyone with a scientific world view (though, in the event, it seems to be mainly philosophers who worry about it).

I consider this exciting evidence that there is a fundamental discovery to be made which will integrate things like qualia into our other
knowledge. Daniel Dennett draws the opposite conclusion, namely that qualia do not exist! His claim is not, strictly speaking, that they are an illusion – for an illusion of a quale would be that quale. It is that we have a
mistaken belief
. Our introspection – which is an inspection of
memories
of our experiences, including memories dating back only a fraction of a second – has evolved to report that we have experienced qualia, but those are false memories. One of Dennett’s books defending this theory is called
Consciousness Explained
. Some other philosophers have wryly remarked that
Consciousness Denied
would be a more accurate name. I agree, because, although any true explanation of qualia will have to meet the challenge of Dennett’s criticisms of the common-sense theory that they exist, simply to deny their existence is a bad explanation: anything at all could be denied by that method. If it is true, it will have to be substantiated by a good explanation of how and why those mistaken beliefs
seem
fundamentally different from other false beliefs, such as that the Earth is at rest beneath our feet. But that looks, to me, just like the original problem of qualia again: we seem to have them; it seems impossible to describe what they seem to be.

One day, we shall. Problems are soluble.

By the way, some abilities of humans that are commonly included in that constellation associated with general-purpose intelligence do not belong in it. One of them is
self-awareness
– as evidenced by such tests as recognizing oneself in a mirror. Some people are unaccountably impressed when various animals are shown to have that ability. But there is nothing mysterious about it: a simple pattern-recognition program would confer it on a computer. The same is true of tool use, the use of language for signalling (though not for conversation in the Turing-test sense), and various emotional responses (though not the associated qualia). At the present state of the field, a useful rule of thumb is: if it can already be programmed, it has nothing to do with intelligence in Turing’s sense. Conversely, I have settled on a simple test for judging claims, including Dennett’s, to have explained the nature of consciousness (or any other computational task):
if you can’t program it, you haven’t understood it
.

Turing invented his test in the hope of bypassing all those philosophical problems. In other words, he hoped that the functionality could be
achieved before it was explained. Unfortunately it is very rare for practical solutions to fundamental problems to be discovered without any explanation of why they work.

Nevertheless, rather like empiricism, which it resembles, the
idea
of the Turing test has played a valuable role. It has provided a focus for explaining the significance of universality and for criticizing the ancient, anthropocentric assumptions that would rule out the possibility of AI. Turing himself systematically refuted all the classic objections in that seminal paper (and some absurd ones for good measure). But his test is rooted in the empiricist mistake of seeking a purely behavioural criterion: it requires the judge to come to a conclusion without any explanation of how the candidate AI is supposed to work. But, in reality, judging whether something is a genuine AI will always depend on explanations of how it works.

That is because the task of the judge in a Turing test has similar logic to that faced by Paley when walking across his heath and finding a stone, a watch or a living organism: it is to explain how the observable features of the object came about. In the case of the Turing test, we deliberately ignore the issue of how the knowledge to
design
the object was created. The test is only about who designed the AI’s
utterances
: who adapted its utterances to be meaningful – who created the knowledge in them? If it was the designer, then the program is not an AI. If it was the program itself, then it is an AI.