Yorick Wilks - Artificial Intelligence: Modern Magic or Dangerous Future?

Wittgenstein wrote that a philosophy book could be written consisting entirely of jokes. In that spirit, an AI book could perhaps be written now consisting entirely of snippets from the daily news making claims about breakthroughs and discoveries. But something would be missing: how can we tell which of them are true, which really correspond to programs or are just science fiction fantasies? In this book, my aim is to describe the essence of AI now, but also to give an account of where it came from over a long period, and speculate about where it may be headed.

This very week one reads of a Belorussian woman who had programmed her dead fiancs texts into a neural network with which she could now talk posthumously. The same idea appeared in the Black Mirror episode Be Right Back in 2013, and in an article I wrote in Prospect magazine in 2010 called Death and the Internet. The same meme comes back all the time and is appealing, but did the Belorussian programmer really do anything serious? No one knows at the moment, but the need to distinguish research, pouring from companies and laboratories, from speculation, fantasy and fiction has never been greater, and I try to sort them out in this book.

The name artificial intelligence was coined by American computer scientist John McCarthy, one of the handful of AI pioneers whose reputation still grows, for a 1956 workshop at Dartmouth College. But doubts about the phrase have grown since then and English code-breaker and computer scientist Donald Michies previous version, Machine Intelligence, which it ousted, is making a comeback, and will be revived when the important journal Nature Machine Intelligence begins publication in 2019. That will be a badge of scientific respectability for a sometimes dubious field, where the word artificial has come to have overtones of trickery. McCarthy said firmly that AI should be chiefly about getting computers to do things humans do easily and without thinking, such as seeing and talking, driving and manipulating objects, as well as planning our everyday lives. It should not, he said, be primarily about things that only a few people do well, such as playing chess or Go, or doing long division in their heads very fast, as calculators do. But Michie thought chess was a key capacity of the human mind and that it should be at the core of AI. And the public triumphs of AI such as beating Kasparov the then world champion at chess, and more recently by playing world championship Go, have been taken as huge advances by those keen to show the inexorable advance of AI. But I shall take McCarthys version as the working definition of AI for this book.

There can, then, be disputes about exactly what AI covers, as we shall see. I shall take a wide view in this book and try to give a quick and painless introduction to its history, achievements and aims immediate and ultimate. The history is important, because although AI now seems everywhere, at least according to newspapers and media, and is pressing upon every human skill, it has actually been around for a long time and has lapped up around us very slowly. Here is a dramatic example: the road sign below was at the end of the driveway of the Stanford AI laboratory when I was there in the early 1970s.

This vehicle was rarely seen, and it consisted of four bicycle wheels with a wooden tray on top holding a radio aerial, a camera and a computer box. It could be steered by radio but sometimes ran itself round the driveway steered by the onboard computer. It was, though, far more significant than its absurd appearance. It was the beginning of the US government funded Moon Lander, later the Mars Lander, project, which was set up because it was known that vehicles on either body would have to be autonomous. That is to say, they would have to drive themselves, because they would be too far away to be radio controlled from Earth; they might fall down a crevasse in the time it took for a radio signal to reach them. That primitive vehicle ran almost 50 years ago, but is the father of all the autonomous vehicles now being tested on our roads and doing millions of miles a year.

It is important to see how long AI has been gestating, slowly but surely, even though it has been a bumpy ride with major setbacks. For example, in 1972 and 1973 AI suffered two major setbacks: the first was a book called What Computers Cant Do, by the philosopher Hubert Dreyfus. He called AI a kind of alchemy (forgetting for a moment that alchemy an early form of chemistry which posited that metals could be transformed into each other has actually turned out to be true in modern times with the discovery of nuclear transmutation!). Dreyfuss central point was that humans grew up, learning as they did so, and only creatures that did that could really understand as we do; that is to say, be true AI. Dreyfuss criticisms were rejected at the time by AI researchers, but actually had an effect on their work and understanding of what they were doing; he helped rejuvenate interest in machine learning as central to the AI project.

The following year, Sir James Lighthill, a distinguished control engineer, was asked by the British government to examine the prospects for AI. He produced a damning report the effect of which was to shut down research support in the UK for AI for many years, though some work was continued under other names such as Intelligent Knowledge Based Systems. Lighthills arguments about what counted as AI were almost all misconceived, as became clear years later. He himself had worked on automated landing systems for aircraft, a great technical success, and which we could easily : the activity of simulating uniquely human activities and skills.

Lighthill considered that trying to model human psychology with computers was possible, but not AIs self-imposed task of just simulating human performances that required skill and knowledge. He was plainly wrong, of course the existence of car-building robots, automated cars and effective machine translation on the web, as well as many AI achievements we now take for granted, all show that. Although a philosopher and an engineer respectively, Dreyfus and Lighthill had something in common: both saw that the AI project meant that computers had to have knowledge of the world to function. But for them, knowledge could not simply be poured into a machine as if from a hopper. AI researchers also recognised this need, yet believed such knowledge