I’m not sure this doesn’t misunderstand Wiener’s use of ‘control’ – and something about AI – but interesting – and interesting that so many articles about the origins of cybernetics continue to come out!
Wiener’s Cybernetics looked in close scientific detail at the process of control via feedback. (Cybernetics, from the ancient Greek for helmsman, is the etymological basis of our word governor, which is what James Watt called his pathbreaking feedback control device that transformed the use of steam engines.) Because he was immersed in problems of control, Wiener saw the world as a set of complex, interlocking feedback loops, in which sensors, signals, and actuators such as engines interact via an intricate exchange of signals and information. The engineering applications of Cybernetics were tremendously influential and effective, giving rise to rockets, robots, automated assembly lines, and a host of precision-engineering techniques—in other words, to the basis of contemporary industrial society.
Wiener had greater ambitions for cybernetic concepts, however, and in The Human Use of Human Beings, he spells out his thoughts on its application to topics as diverse as the thought experiment Maxwell’s demon, human language, the brain, insect metabolism, the legal system, the role of technological innovation in government, and religion. These broader applications of cybernetics were an almost unequivocal failure. Vigorously hyped from the late 1940s to the early 1960s—to a degree similar to the hype of computer and communication technology that led to the dot-com crash of 2000–2001—cybernetics delivered satellites and telephone switching systems but generated few if any useful developments in social organization and society at large.
Nearly 70 years later, however, The Human Use of Human Beings has more to teach us humans than it did the first time around. Perhaps the most remarkable feature of the book is that it introduces a large number of topics concerning human/machine interactions that are still of considerable relevance. Dark in tone, the book makes several predictions about disasters to come in the second half of the 20th century, many of which are almost identical to predictions made today about the second half of the 21st.
But Wiener failed to foresee crucial technological developments. Like pretty much all technologists of the 1950s, he failed to predict the computer revolution. Computers, he thought, would eventually fall in price from hundreds of thousands of (1950s) dollars to tens of thousands; neither he nor his compeers anticipated the tremendous explosion of computer power that would follow the development of the transistor and the integrated circuit. Finally, because of his emphasis on control, Wiener could not foresee a technological world in which innovation and self-organization bubble up from the bottom rather than being imposed from the top.
What Wiener Got Right
Wiener’s most famous mathematical works focused on problems of signal analysis and the effects of noise. During World War II, he developed techniques for aiming antiaircraft fire by making models that could predict the future trajectory of an airplane by extrapolating from its past behavior. In Cybernetics and in The Human Use of Human Beings, Wiener notes that this past behavior includes quirks and habits of the human pilot; thus, a mechanized device can predict the behavior of humans. Like Alan Turing, whose Turing test suggested that computing machines could give responses to questions that were indistinguishable from human responses, Wiener was fascinated by the notion of capturing human behavior by mathematical description. In the 1940s, he applied his knowledge of control and feedback loops to neuromuscular feedback in living systems, and was responsible for bringing Warren McCulloch and Walter Pitts to MIT, where they did their pioneering work on artificial neural networks.
Wiener’s central insight was that the world should be understood in terms of information. Complex systems, such as organisms, brains, and human societies, consist of interlocking feedback loops in which signals exchanged between subsystems result in complex but stable behavior. When feedback loops break down, the system goes unstable. He constructed a compelling picture of how complex biological systems function, a picture that is by and large universally accepted today.
Wiener’s vision of information as the central quantity in governing the behavior of complex systems was remarkable at the time. Nowadays, when cars and refrigerators are jammed with microprocessors and much of human society revolves around computers and cellphones connected by the internet, it seems prosaic to emphasize the centrality of information, computation, and communication. In Wiener’s time, however, the first digital computers had only just come into existence, and the internet was not even a twinkle in the technologist’s eye.
Wiener’s powerful conception of not just engineered complex systems but all complex systems as revolving around cycles of signals and computation led to tremendous contributions to the development of complex human-made systems. The methods he and others developed for the control of missiles, for example, were later put to work in building the Saturn V moon rocket, one of the crowning engineering achievements of the 20th century. In particular, Wiener’s applications of cybernetic concepts to the brain and to computerized perception are the direct precursors of today’s neural network–based deep-learning circuits, and of artificial intelligence itself. But current developments in these fields have diverged from his vision, and their future development may well affect the human uses both of human beings and of machines.
What Wiener Got Wrong
It is exactly in the extension of the cybernetic idea to human beings that Wiener’s conceptions missed their target. Setting aside his ruminations on language, law, and human society for the moment, look at a humbler but potentially useful innovation that he thought was imminent in 1950. Wiener notes that prosthetic limbs would be much more effective if their wearers could communicate directly with their prosthetics by their own neural signals, receiving information about pressure and position from the limb and directing its subsequent motion. This turned out to be a much harder problem than Wiener envisaged: Seventy years down the road, prosthetic limbs that incorporate neural feedback are still in the very early stages. Wiener’s concept was an excellent one—it’s just that the problem of interfacing neural signals with mechanical-electrical devices is hard.
More significantly, Wiener (along with pretty much everyone else in 1950) greatly underappreciated the potential of digital computation. As noted, Wiener’s mathematical contributions were to the analysis of signals and noise, and his analytic methods apply to continuously varying, or analog, signals. Although he participated in the wartime development of digital computation, he never foresaw the exponential explosion of computing power brought on by the introduction and progressive miniaturization of semiconductor circuits. This is hardly Wiener’s fault: The transistor hadn’t been invented yet, and the vacuum-tube technology of the digital computers he was familiar with was clunky, unreliable, and unscalable to ever larger devices. In an appendix to the 1948 edition of Cybernetics, he anticipates chess-playing computers and predicts that they’ll be able to look two or three moves ahead. He might have been surprised to learn that within half a century a computer would beat the human world champion at chess.
Technological Overstimulation and the Existential Risks of the Singularity
When Wiener wrote his books, a significant example of technological overestimation was about to occur. The 1950s saw the first efforts at developing artificial intelligence by researchers such as Herbert Simon, John McCarthy, and Marvin Minsky, who began to program computers to perform simple tasks and to construct rudimentary robots. The success of these initial efforts inspired Simon to declare that “machines will be capable, within 20 years, of doing any work a man can do.” Such predictions turned out to be spectacularly wrong. As they became more powerful, computers got better and better at playing chess because they could systematically generate and evaluate a vast selection of possible future moves. But the majority of predictions of A.I., e.g., robotic maids, turned out to be illusory. When Deep Blue beat Garry Kasparov at chess in 1997, the most powerful room-cleaning robot was a Roomba, which moved around vacuuming at random and squeaked when it got caught under the couch.
Technological prediction is particularly chancy, given that technologies progress by a series of refinements, halted by obstacles and overcome by innovation. Many obstacles and some innovations can be anticipated, but more cannot. In my own work with experimentalists on building quantum computers, I typically find that some of the technological steps I expect to be easy turn out to be impossible, whereas some of the tasks I imagine to be impossible turn out to be easy. You don’t know until you try.
In the 1950s, partly inspired by conversations with Wiener, John von Neumann introduced the notion of the “technological singularity.” Technologies tend to improve exponentially, doubling in power or sensitivity over some interval of time. (For example, since 1950, computer technologies have been doubling in power roughly every two years, an observation enshrined as Moore’s law.) Von Neumann extrapolated from the observed exponential rate of technological improvement to predict that “technological progress will become incomprehensively rapid and complicated,” outstripping human capabilities in the not-too-distant future. Indeed, if one extrapolates the growth of raw computing power—expressed in terms of bits and bit flips—into the future at its current rate, computers should match human brains sometime in the next two to four decades (depending on how one estimates the information-processing power of human brains).
The failure of the initial overly optimistic predictions of A.I. dampened talk about the technological singularity for a few decades, but since the 2005 publication of Ray Kurzweil’s The Singularity Is Near, the idea of technological advance leading to superintelligence is back in force. Some believers, Kurzweil included, regard this singularity as an opportunity: Humans can merge their brains with the superintelligence and thereby live forever. Others, such as Stephen Hawking and Elon Musk, worried that this superintelligence would prove to be malign and regarded it as the greatest existing threat to human civilization. Still others think such talk is overblown.
Wiener’s lifework and his failure to predict its consequences are intimately bound up in the idea of an impending technological singularity. His work on neuroscience and his initial support of McCulloch and Pitts adumbrated the startlingly effective deep-learning methods of the present day. Over the past decade, and particularly in the last five years, such deep-learning techniques have finally exhibited what Wiener liked to call Gestalt—for example, the ability to recognize that a circle is a circle even if when slanted sideways it looks like an ellipse. His work on control, combined with his work on neuromuscular feedback, was significant for the development of robotics and is the inspiration for neural-based human/machine interfaces. His lapses in technological prediction, however, suggest that we should take the notion of a technological singularity with a grain of salt. The general difficulties of technological prediction and the problems specific to the development of a superintelligence should warn us against overestimating both the power and the efficacy of information processing.
The Arguments for Singularity Skepticism
No exponential increase lasts forever. An atomic explosion grows exponentially, but only until it runs out of fuel. Similarly, the exponential advances in Moore’s law are starting to run into limits imposed by basic physics. The clock speed of computers maxed out at a few gigahertz a decade and a half ago, simply because the chips were starting to melt. The miniaturization of transistors is already running into quantum-mechanical problems due to tunneling and leakage currents. Eventually, the various exponential improvements in memory and processing driven by Moore’s law will grind to a halt. A few more decades, however, will probably be time enough for the raw information-processing power of computers to match that of brains—at least by the crude measures of number of bits and number of bit-flips per second.
Human brains are intricately constructed, the process of millions of years of natural selection. In Wiener’s time, our understanding of the architecture of the brain was rudimentary and simplistic. Since then, increasingly sensitive instrumentation and imaging techniques have shown our brains to be far more varied in structure and complex in function than Wiener could have imagined. I recently asked Tomaso Poggio, one of the pioneers of modern neuroscience, whether he was worried that computers, with their rapidly increasing processing power, would soon emulate the functioning of the human brain. “Not a chance,” he replied.
The recent advances in deep learning and neuromorphic computation are very good at reproducing a particular aspect of human intelligence focused on the operation of the brain’s cortex, where patterns are processed and recognized. These advances have enabled a computer to beat the world champion not just of chess but of Go, an impressive feat, but they’re far short of enabling a computerized robot to tidy a room. (In fact, robots with anything approaching human capability in a broad range of flexible movements are still far away—search “robots falling down.” Robots are good at making precision welds on assembly lines, but they still can’t tie their own shoes.)
Raw information-processing power does not mean sophisticated information-processing power. While computer power has advanced exponentially, the programs by which computers operate have often failed to advance at all. One of the primary responses of software companies to increased processing power is to add “useful” features, which often make the software harder to use. Microsoft Word reached its apex in 1995 and has been slowly sinking under the weight of added features ever since. Once Moore’s law starts slowing down, software developers will be confronted with hard choices between efficiency, speed, and functionality.
A major fear of the singulariteers is that as computers become more involved in designing their own software, they’ll rapidly bootstrap themselves into achieving superhuman computational ability. But the evidence of machine learning points in the opposite direction. As machines become more powerful and capable of learning, they learn more and more as human beings do—from multiple examples, often under the supervision of human and machine teachers. Education is as hard and slow for computers as it is for teenagers. Consequently, systems based on deep learning are becoming more rather than less human. The skills they bring to learning are not “better than” but “complementary to” human learning: Computer learning systems can identify patterns that humans cannot—and vice versa. The world’s best chess players are neither computers nor humans but humans working together with computers. Cyberspace is indeed inhabited by harmful programs, but these primarily take the form of malware—viruses notable for their malign mindlessness, not for their superintelligence.
Wiener noted that exponential technological progress is a relatively modern phenomenon and not all of it is good. He regarded atomic weapons and the development of missiles with nuclear warheads as a recipe for the suicide of the human species. He compared the headlong exploitation of the planet’s resources with the Mad Tea Party of Alice in Wonderland: Having laid waste to one local environment, we make progress simply by moving on to lay waste to the next. Wiener’s optimism about the development of computers and neuromechanical systems was tempered by his pessimism about their exploitation by authoritarian governments, such as the Soviet Union, and the tendency for democracies, such as the United States, to become more authoritarian themselves in confronting the threat of authoritarianism.
Unsurprised by global warming—the Mad Tea Party of our era—Wiener would applaud the exponential improvement in alternative-energy technologies and would apply his cybernetic expertise to developing the intricate set of feedback loops needed to incorporate such technologies into the coming smart electrical grid. Nonetheless, recognizing that the solution to the problem of climate change is at least as much political as it is technological, he would undoubtedly be pessimistic about our chances of solving this civilization-threatening problem in time. Wiener hated hucksters—political hucksters most of all—but he acknowledged that hucksters would always be with us.
From “Wrong, but More Relevant Than Ever” by Seth Lloyd. Adapted from Possible Minds: 25 Ways of Looking at AI edited by John Brockman, published by Penguin Press, an imprint of Penguin Publishing Group, a division of Penguin Random House LLC. Copyright © 2019 by John Brockman.
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Continues in source: Norbert Wiener’s Human Use of Human Beings is more relevant than ever.
PS I enjoyed the irony that I had to click this to read the article: