near future. An interesting feature of many of these formulations is that they tend to flatten AI into a monolithic entity. Today, AI can be a virtual personal assistant, a self-driving car, a robot, a smart lift or a drone. But it is not obvious that many of these formulations can easily cope with these gradations or differentiations of machine intelligence. A smart elevator using AI to manage the flow of demand in an office building based on data collected from daily usage, for example, is essentially goal-orientated and single in technological objective. It is an example of weak or narrow AI, where machine intelligence can only do what it is programmed to do, based on a very limited range of contexts and parameters. Examples of narrow AI range from Google Search to facial recognition software to Apple’s Siri, and these are all quite basic kinds of automated machine intelligence. They have been programmed to perform a single task well yet cannot switch to perform other types of tasks – or, at least, not without considerable further labour performed by engineers and computer scientists. On the other hand, there are more sophisticated forms of AI. Deep AI, or what is termed artificial general intelligence, is an advanced form of self-learning machine intelligence seeking to replicate human intelligence. Unlike narrow AI technologies, deep AI combines insights from different fields of activity, performs multiple tasks of intelligence and displays considerable flexibility and dexterity. Deep AI entails the harnessing of massive computational processing power – for instance, the Summit supercomputer, which, in performing 200 million billion calculations per second, is among the fastest computers in the world – to machine learning algorithms. Arguably one of the best operational examples of deep AI is IBM’s Watson, a system which combines supercomputing with deep learning algorithms: such algorithms are designed to optimize their performance against specified data-processing criteria (such as speech or facial recognition, or medical diagnosis) through self-adjusting the thresholds of what is relevant or irrelevant in the data under analysis. Another AI variant is that of superintelligence, which doesn’t exist yet, but is forecast by many specialists to involve a fully fledged machine intelligence which outstrips human intelligence in every domain, including both cognitive reasoning and social skills. Superintelligence has long been the preserve of Hollywood science fiction, and the personalized AI system of Samantha in the film Her is a signal example. (We will turn to consider technological advances related to superintelligence in more detail in Chapter 8.)
One of the problems of current debate is that there is a lot of hype, a lot of misconceptions and too many overblown claims about AI. One way of reading AI against the grain is to avoid the specialist definitions circulating in the field and talk about resistances, disorders and the historical past instead. It is always useful to get a sense of how a specialist discourse is approached by those outside of its representative institutions, and similarly it helps to look at the prehistory of an emergent technology. This line between the ‘official’ and the ‘unofficial’ version of AI is not always easy to cross, but I want to focus briefly on considering aspects of the prehistory of AI – in order to better grasp the constitution of the whole discourse of AI. That is to say, I want to focus on the function of ideas within and around AI – including the aspirations, objectives and dreams of technologists – in order to better situate today’s technological realities as well as its manifold distortions. In other words, my aim here is to return AI to its own displaced history.
An objection to the glossy image presented by various tech companies that AI has only recently arrived, and arrived fully formed, is that machine intelligence and mechanical automatons are, in fact, historical through and through. Those advocating the technological hype of our times may not wish to be embroiled in trawling through the histories and counter-histories of various technologies, but expanding the historical boundaries of the discourse of AI by bringing back into consideration those developments banished to the background and left out of the official narrative is essential to combating the idea that AI is a straightforward, linear story which runs roughly from the 1956 Dartmouth Conference to the present day. The developments that unite an otherwise disparate and apparently unconnected series of topics in the emergence of AI require us to go back to the eighth century bc, where automatons and robots crop up in Greek myths such as that of Talos of Crete.4 Or you have to go back to the ancient world of Mesopotamia, where Muslim polymath Ismail Ibn al-Razzaz al-Jazari invented automatic gates and automated doors driven by hydropower, whilst simultaneously penning his programmatic text, The Book of Knowledge of Ingenious Mechanical Devices.5 An alternative historical starting point might be the ancient philosophy of Aristotle, who wrote of artificial slaves in his foundational Politics.6
Fast forward to the early modern period in Europe, where the landscape of automatons is still largely about dreaming but also where conflicts between human and machine intelligence become amenable to, and await, resolution. Early modern European thought in cooperation with scientific reason found its way towards such conflict resolution under the twin banners of calculation and mechanics. The French philosopher, mathematician and scientist René Descartes compared the bodies of animals to complex machines. In the political thought of Thomas Hobbes, a mechanical theory of cognition stood for the human territory over which reason extended. In the practice of French mathematician and inventor Blaise Pascal, arithmetical calculations stood for the feasibility and ultimate triumph of the theory of probability – as this prodigious physicist and Catholic theologian worked obsessively to build mechanical prototypes and calculating machines. Fast forward again some centuries and we find writers and artists alike viewing a society leaning solely on human attributes or natural impulses with considerable suspicion. Throughout the modern era, from Mary Shelley’s Frankenstein to Karel Čapek’s Rossum’s Universal Robots, reality was to be shaped, thought about and interpreted with reference to automatons, cyborgs and androids. At the dawn of the twentieth century, the dream of automated machines was brought finally and firmly inside the territory where empirical testing is done, most notably with a tide-predicting mechanical computer – commonly known as Old Brass Brains – developed by E. G. Fischer and Rolin Harris.7 The world had, at long last, shifted away from the ‘natural order of things’ towards something altogether more magical: the ‘artificial order of mechanical brains’.
For most people today, AI is equated with Google, Amazon or Uber, not ancient philosophy or mechanical brains. However, there remain earlier, historical prefigurations of AI which still resonate with our current images and cultural conversations about automated intelligent machines. One such pivot point comes from the UK in the early 1950s, when the English polymath Alan Turing – sometimes labelled the grandfather of AI – raised the key question ‘can machines think?’8 Turing, who had been involved as a mathematician in important enemy code breaking during World War II, raised the prospect that automated machines represent a continuation of thinking by other means. Thinking in the hands of Turing becomes a kind of conversation, a question-and-answer session between human and machine. Turing’s theory of machines thinking was based on a British cocktail party game, known as ‘the imitation game’, in which a person was sent into another room of the house and guests had to try to guess their assumed identity. In Turing’s reworking of this game, a judge would sit on one side of a wall and, on the other side of the wall, there would be a human and a computer. In this game, the judge would chat to mysterious interlocutors on the other side of the screen, and the aim was to try to trick the judge into thinking that the answers coming from the computational agent were, in fact, coming from the flesh-and-blood agent. This experiment became known as the Turing Test.
There has been, then, a wide and widening gamut of automated technological advances, symptomatic of the shift from thinking machines that may equal the intelligence of humans to thinking machines that may exceed the intelligence of humans, but all of which have been and remain highly contested. Whether automated intelligent machines are likely to surpass human intelligence not only in practical applications but in a more general sense figures prominently among the major issues of our times and our lives in these times. Notwithstanding the notoriously overoptimistic claims of various AI researchers and futurists, there has been an overwhelming sense of crisis confronted by scientists, philosophers and theorists of technology alike, in greater or smaller measure, that the feverish ambition to establish whether AI could ever really
