Making Sense of AI. Anthony Elliott
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 dominance in world politics meant that, during the Cold War, 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