the US military sought to automate the translation of documents from Russian and other languages into English. This situation led to considerable state investment in machine translation research. During this initial period of increased defence funding in AI research, a cluster of economic, political and military changes occurred around the late 1950s and early 1960s that were of essential significance to the building of better intelligent machines and advanced AI systems. First, Soviet communism delivered a major shock to the American psyche with the launch of Sputnik, the first artificial earth satellite, in 1957. Beyond this dramatic shock, further reverberations were felt throughout the West in the same year when Russia launched Sputnik 2, a spacecraft that put Laika the dog into orbit. The idea of a space future successfully colonized by Soviet-bloc countries spurred the USA into dramatically increasing spending – military and otherwise – on science, technology and research. Second, new research funding in AI – from machine translation to speech-recognition projects – was launched in America by agencies including the CIA, the National Science Foundation and the Department of Defense. This increasingly defence-driven system of research innovation resulted in a much greater speed-up of advances in automation as well as other breakthroughs in machine intelligence.
Third, during this period of state-led AI research investment in the 1960s, various socio-technical and cultural shifts took place as regards the promise, power and prestige of automated machine intelligence. The establishment of the Advanced Research Projects Agency (ARPA) in 1962 represented, for example, a gigantic effort to ensure that America was first to land on the moon. Beyond the space race, however, this entity ushered into existence other world-transforming contributions too, most notably breakthroughs in advanced computing and automated system architectures led by J. C. R Licklider. A psychologist with a passion for mathematics and mechanical engineering, Licklider served at the Pentagon and sought to expand ARPA (and subsequently DARPA, with the D added in 1972) beyond its narrow military confines by supporting multiple AI research projects and associated breakthroughs in advanced computing. As a chief networker among networked researchers and technologists, Licklider authorized support for many projects, including the work of John McCarthy, as well as projects at Carnegie Mellon University, SRI International and the RAND Corporation. His major legacy was to develop a computer network linking these colleagues and research projects together, initially pursued through Project MAC – the development of multi-access computing. This, in turn, culminated in the establishment of ARPANET – a computational network which was, in effect, the forerunner of the Internet and the World Wide Web. But it was ideas as well as inventions for which Licklider deserves a prominent place in the history of artificial intelligence. The digital transformation envisaged by Licklider was captured most vividly in his 1960 paper, ‘Man-Computer Symbiosis’. This was a dramatic advance beyond Turing’s notion that machines might one day think. Licklider’s vision, by contrast, was all about intuitive interactive computing, the interface of human and machine. In his compelling intellectual history The Dream Machine, M. Mitchell Waldrop argues that Licklider
was unique in bringing to the field a deep appreciation for human beings: our capacity to perceive, to adapt, to make choices, and to devise completely new ways of tackling apparently intractable problems. As an experimental psychologist, he found these abilities every bit as subtle and as worthy of respect as a computer’s ability to execute an algorithm. And that was why to him, the real challenge would always lie in adapting computers to the humans who used them, thereby exploiting the strengths of each.12
In this speaking up for interactivity, technological interfaces, decentralization and connectivity, Licklider can in many ways be said to have shaped AI as we know it today.
Complex Systems, Intelligent Automation and Surveillance
One sometimes hears the opinion that the industry of AI – the tech giants from Silicon Valley to Shenzhen – is inhospitable to critique. AI as a global enterprise has been, over a long period, the sworn enemy to critical thought about what it may control, whilst altogether blocking off engagement with questions of how new technologies might be controlled by other economic powers and political forces. While hospitable to engagement from consumer society, AI industry leaders have been remarkably silent on questions of control, power and exploitation. In retrospect, we can say that AI – both within industry and beyond – has often been presented as a neutral object. Against such trends towards diffusion or neutralization, the critical question remains this: what might it mean to read power and control back into the discourse of AI? The notion that AI is associated with globalization is familiar enough. Science, technology and automated intelligent machines more generally play a fundamental role in the globalizing of AI. However, I seek throughout this book to reframe this issue in terms of an institutional account of AI, developed in terms of interdependent complex systems. The overall direction of AI is to create automated settings of action which are ordered in terms of complex systems at once robust and fragile. This is an important, although nuanced, point – and requires further elaboration. Many commentators emphasize the exponential dynamics of change in contemporary society as a result of AI, but this is often misleading because AI can also contribute to the stabilization of socio-technical systems for long stretches of time. Rather, the point is that AI facilitates persistent structures and durable systems on the one hand, and the break-up, breakdown or disappearance of complex systems on the other hand. Understanding how AI intersects with complex systems which are dynamic, processual and unpredictable is of key importance for grasping the ways in which automated intelligent machines also function as a field of force, a realm of conflict and coercion in which power and control are produced, reproduced and transformed.
Some central notions from complexity theory are developed in this book, especially in chapter 4. In seeking to demonstrate the power interests realized in and through artificial intelligence, it is necessary to characterize the complex systems of AI. Over the course of the twentieth century and into the twenty-first century, a number of interdependent complex systems served to create a major field of AI, spun off from economic, bureaucratic, industrial and military forces, and each typically providing major resources for the advancement of AI in the contemporary world. The interdependent complex systems, as I discuss at length in chapter 4, include:
1 the scale, scope and extensity of AI in terms of research and innovation, industry and enterprise, as well as technologies and consumer products;
2 the intricate interplay of ‘new’ and ‘old’ technologies, and of the role of established technologies persisting or transforming within many modes of more recent AI and automated intelligent machines;
3 the globalization of AI and the centrality of AI technologies and industries in high-tech digital cities;
4 the growing diffusion of AI in modern institutions and everyday life;
5 the trend towards complexity, at once technological and social;
6 the intrusion of AI technologies into lifestyle change, personal life and the self;
7 the transformation of power as a result of AI technologies of surveillance.
The complex systems in which AI is enmeshed in the contemporary world are at once economic, social, political, material and technological. These interconnected complex systems, as I seek to show, should not be reduced to separate ‘factors’ or ‘processes’. There are no automated intelligent machines without complex systems. As a result, AI is a field characterized by transformation, unpredictability, innovation and reversal. The interdependent complex systems of AI are continually adapting, evolving and self-organizing.
In the early decades of the twenty-first century, there have been two major debates about technology and the general conditions of society and world order. One concerns a possible ‘autonomization’ of society and possibly even of culture and politics. The other concerns broad, massive changes in technological
