feeling where humanity is ‘living at the crossroads’. There have been, it should be noted, some very vocal and often devastating critiques of AI developed in this connection. The philosopher Hubert Dreyfus was an important early critic. In his book What Computers Can’t Do, Dreyfus argued that the equation mark put between machine and human intelligence in AI was fundamentally flawed. To the question of whether we might eventually regard computers as ‘more intelligent’ than humans, Dreyfus answered that the structure of the human mind (both its conscious and unconscious architectures) could not be reduced to the mathematical precepts which guide AI. Computers, as Dreyfus put it, altogether lack the human ability to understand context or grasp situated meaning. Essentially reliant on a simple set of mathematical rules, AI is unable, Dreyfus argued, to grasp the ‘systems of reference’ of which it is a part.
Another critique, arguably more damaging, of the limitations in equating human and machine intelligence was developed by the American philosopher John Searle. Searle was strongly influenced by the philosophical departures of Ludwig Wittgenstein, especially Wittgenstein’s demonstration that what gives ordinary language its precision is its use in context. When people meet and mingle, they use contextual settings to define the nature of what is said. This time-and-effort contextual activity of putting meaning together, practised and rehearsed daily by humans, is not something that AI can substitute for, however. To demonstrate this, Searle provided what he famously termed the ‘Chinese Room Argument’. As he explains:
Imagine a native English speaker who knows no Chinese locked in a room full of boxes of Chinese symbols (a data base) together with a book of instructions for manipulating symbols (the program). Imagine that people outside the room send in other Chinese symbols which, unknown to the person in the room, are questions in Chinese (the input). And imagine that by following the instructions in the program the man in the room is able to pass out Chinese symbols which are correct answers to the questions (the output). The program enables the person in the room to pass the Turing Test for understanding Chinese but he does not understand a word of Chinese.9
The upshot of Searle’s arguments is clear. Machine and human intelligence might mirror each other in chiasmic juxtaposition, but AI is not able to capture the human ability of constantly connecting words, phrases and talk within practical contexts of action. Meaning and reference are, in short, not reducible to a form of information processing. It was Wittgenstein that pointed out that a dog may know its name, but not in the same way that her master does. Searle demonstrates this is similarly true for computers. It is this human ability to understand context, situation and purpose within modalities of day-to-day experience that Searle, powerfully and provocatively, asserts in the face of comparisons between human and machine intelligence.
Frontiers of AI: Global Transformations, Everyday Life
Another way of reading AI against the grain – contesting the ‘official’ narrative of artificial intelligence – is to rethink its relation to economy, society and unequal relations of power. These are all key domains in which the discourse of AI can and must be situated. I have argued in the preceding section that what the idea of an intelligence rendered ‘artificial’ signifies is, among other things, the transformation and transcendence of human capabilities from natural, inborn and inherited determinations of the biological and biographical realms. AI consists in the project of transforming human knowledge into machine intelligence – and charging social actors with the task of integrating, incorporating and invoking such newly minted artificial automations into the living of everyday life. Such manufacturing of automated intelligent machines, however, works not only upon an internal register – the field of individual life, individualization and the development of human intelligence – but also outwards – across societies, economies and power politics. AI-powered software programs are today downloaded to multiple locations across the planet – at once stored, operationalized and modified. Contrasting the limitations of the human brain by cranial volume and metabolism with the extraterritorial reach of AI, Susan Schneider argues that automated machine intelligence ‘could extend its reach across the Internet and even set up a galaxy-wide “computronium” – a massive supercomputer that utilizes all the matter within a galaxy for its computations. In the long run, there is simply no contest. AI will be far more capable and durable than we are.’10
So, AI is also all about galaxy-wide movement and especially the automated global movement of software, symbols, simulations, ideas, information and intelligent agents. AI-powered information societies involve a relentless automation of economic, social and political life. This point is an important one to register, as many commentators invoke the spectre of globalization to capture the economic transformations of manufacturing, industry and enterprise as a consequence of AI technology and its deployment in offshore business models. Certainly, a great deal of academic and policy thinking has emphasized how the global digital economy has become ‘borderless’, with many frontiers now automated and regulated through the operations of intelligent machines. The rise of AI is intricately interwoven with globalization, it is often said. This is surely the case, though it is vital to see that globalization links together people, intelligent machines and automation in complex, contradictory and uneven ways. Understanding that AI is both condition and consequence of globalization has to be properly contextualized.
Many studies have cast globalization solely as an economic phenomenon. From this angle, globalization consists of the ever-increasing integration of economic activity and financial markets across borders. Some analyses have emphasized that globalization is the driver of economic neoliberalism, privatization, deregulation, speculative finance and the crystallization of multinational corporations operating across the borderless flows of the global economy.11 It is obvious that such an image of globalization is well geared to rendering AI as simply an upshot of the corporate activities of IBM, Amazon, Google, Microsoft and Alibaba. Other writers have argued that globalization is synonymous with Americanization. AI here is viewed as a set of effects brought about by powerful actors, academic research institutes and industry labs, administrative entities and political forces promoting the Americanization of the world. Much AI research, as we will examine throughout this book, has indeed been funded by the American government, especially the US Department of Defense. Consider, for example, the extensive role of the Defense Advanced Research Projects Agency (DARPA), which during the 1960s poured millions of dollars into the establishment of AI labs at MIT, Carnegie Mellon University and Stanford University along with commercial AI laboratories including SRI International. As I discuss in some detail in chapter 3, the influence of the US Department of Defense upon the digital revolution was hugely consequential and brought in its train a global extension of emergent markets for artificial intelligence.
And so we come back to the big issue of who exactly commissioned the major AI projects that were launched in the 1950s and 1960s. Who was paying for the key AI research breakthroughs? What forms of power were these early commissions advancing and reinforcing? Obviously there were many divergent interests, although the history of the funding cycles around AI clearly suggests that nation-states (especially the United States and, to a much more limited extent, the United Kingdom) along with the biggest multinational companies were the principal actors. Beyond nation-states and corporations, however, another dimension of AI concerns the world military order. Understanding the connections between the techno-industrialization of war, automated techniques of military organization and the flow of AI technologies is very important to grasping the globalizing of AI. I seek to highlight these issues in terms of an institutional account of what I shall call algorithmic modernity, developed with reference to the operations of advanced capitalism, lifestyle change, social inequalities and surveillance, throughout the book as a whole. For the moment, however, it is notable that many of the early successes, as well as some fairly dramatic failures, in AI can be traced to overlaps between military power and the development of automated intelligent machines.
Some argue, rightly in my view, that the rise of AI sprang directly from challenges that the West faced in relation to Soviet communism and the outcomes of the Cold War. Certainly, the general imperative of establishing military
