Scenarios in Effective Communication to Citizens and Corporations
The term Utopia, coined by Thomas More in 1516, contains an inherent semantic ambiguity: it could be read as eu topos (good place) or ou topos (no place). The authors of this volume analyze this polysemous notion and its fascination for scholars across the centuries, who have developed a variety of visions and ways to explain the «realization» of utopian discourses. The experts in the fields of sociology, political science, economics, computer science, literature and linguistics offer extensive studies about how utopian scenarios are realized in different cultural contexts.
Technology, Artificial Intelligence and Keynes' Utopia: A Realized Prediction?
In a globalization scenario, the current economic crisis has highlighted the weaknesses of the global economic system, strongly interdependent and increasingly dependent on market fluctuations and the geopolitical balance. The difficulty of getting out of the current situation is evidenced by the numerous efforts to find systemic solutions that would be able to cope with other future manifestations of a crisis of such a magnitude. An interesting aspect is represented by the emergence of themes that form the backdrop to the economic crisis but, at the same time, they represent the most obvious manifestations. Among these economic themes, unemployment is the one that most affects the public and scholars, representing, in a system in which the consumer is the pivot of the economy and international finance, the Achilles heel of economic recovery.
But what else could be expected, given the present scenario? Are there other contributing factors that have accelerated the process of crisis? Above all, does the technological acceleration that we have been witness to in the last decades represent an opportunity or an obstacle in the search for a new system of economic balance?
Taking a cue from Keynes’ predictions in his famous speech Economic Possibilities for our Grandchildren (Madrid, June 1930), this work analyzes the link between technological progress and employment that is the responsible for rampant unemployment, critically tackling the major themes of the economy weighed against the progress of information technology and, in particular, of Artificial Intelligence.
2. John Maynard Keynes: Economic Possibilities for Our Grandchildren
In 1930 at a conference in Madrid, John Maynard Keynes delivered a speech entitled Economic Possibilities for our Grandchildren, in which he addressed the issue of unemployment in a far-sighted and completely innovative way, linking it to technological advances in the present and the future, among other things. In his speech, Keynes foresaw runaway unemployment that would have occurred mainly because of technological progress, which, over time, would take over the work ← 73 | 74 → that used to be performed by human workers, thereby reducing the number of necessary working hours. This in turn would free up time for individuals to carry out different activities pursued not to search for remuneration but for personal growth or entertainment. Keynes’ idea, which was then seen as a utopian idea, is, in fact, partially realized today. Germany, for instance, has solved the problem of unemployment in part by reducing working hours (the example of Volkswagen is significant here), realizing, in practice, the thought and the indications of Keynes. Unemployment is, today, in Italy up to 13.2%, with a peak among young people (15–24 years) of 40%. Other countries, however, are not better off.
In this speech, Keynes emphasizes that progress in general, and in technology in particular, is essential to the human aspiration to be free from wage slavery, opening a path to pursue more cultural and non-profit work. Yet Plato and Aristotle questioned the production of objects and practical activities by placing them in the background compared to the production of ideas:
Meanwhile, they dreamed future worlds, gods and heroes using robots. “If every instrument – fabled Aristotle – could, once ordered, work by himself, if quills could knit alone, if the bow sounded alone on the zither, entrepreneurs could do without the workers and the bosses of the slaves”. (“The revenge part of Keynes”, Domenico De Masi)
However, over time, a growing gap has been observed between the classes who can afford to reduce their work to a pleasant occupation and those for whom it was and is still a necessity for survival (referring to the birth of the proletariat).
Nevertheless, progress led to the surrender of the lower classes, who were able to live in degrading conditions and do exhausting work. New inventions, along with technological and industrial progress before then, gradually improved working conditions, providing a glimpse into the possibility of Keynes’ envisioned utopia of total freedom from fatigue and physical work. Information technology, in recent decades, has accelerated this process, making the motto “less and less work thanks to the machines” an ideological banner that has fostered the development of technologies’ and systems’ optimization of time and resources. Every expert in these areas develops their own experiences, keeping in mind that information technology can help save time and labor. In contrast, however, this impulse was not met with a real reduction in the demands of work. In contrast, the less time it takes to carry out certain tasks has actually led to a greater burden for each worker. In this way, the amount of product divided by the time and manpower needed to produce it, although incremental for each product produced, generates, for a certain period of time, uncontrolled growth of the demands placed on each worker. Today this situation primarily concerns workers who have to deal, directly or indirectly, with the technology and with the use of technological means, while the ← 74 | 75 → other categories experience progress not as liberation from work, but as a threat to employment. Along these lines, as clearly shown by De Masi, even organizational innovation has contributed to the reduction of labor required to produce goods or services. In fact, Taylor, the father of scientific management, provides several examples of the effect of organizational innovation on employment.
All of this may seem positive at first glance, the utopic desire for liberation from the need to work, but companies have not actually redistributed the workload to reduce working hours. Instead, they tend to reduce staff, up to a collapse in the entire system, with the boomerang effect of unemployment. In fact, growing unemployment (and therefore decreasing revenue per capita) consequently decreases the consumption of goods and services, with obvious repercussions for the entire economic system, which is already precarious because of delicate geopolitical balances, as previously mentioned. Therefore, reducing working hours is a principle, not a practice. In fact, according to the believe that the recognition of merit is closely related to the extension of permanence in the work place, new employees tend to prolong their working hours.
As De Masi states: “homo faber prevails systematically homo cogitans and especially the homo ludens, multiply, rather than reduce, the causes of unhappiness taken as ‘natural’ and even as a providential opportunity atoning of living beings”.
We are therefore still far from Keynes’ ideas about technological unemployment and its consequences. Nevertheless, the economist had anticipated the limits of his own theory, today practiced not with the desired effects, which states that unemployment should be combated by reducing taxes and increasing investment. While the technological acceleration we are seeing today was not yet predictable in 1930, Keynes realized that technology would indeed have a key role in the economy and that it would be a significant contributory cause of unemployment.
The first words uttered by Keynes in his speech at the opening of the Madrid conference were: “Right now we are suffering from a severe attack of economic pessimism. I think this is a very wrong interpretation of what is happening”. These are words that ring true with the mood of contemporary times.
Keynes begins with a historical excursus to describe all the efforts made by man to free himself from the crushing weight of hard work before proceeding to explain how technological unemployment is a necessary and transient evil to reach liberation from work.
In his speech, Keynes distinguishes between absolute needs (exhaustible) and related needs (inexhaustible), predicting that we will soon be able to meet the absolute needs to devote all our energy to non-economic purposes, thus reaching the utopian ideal that humanity has always pursued. He speculates, in fact, that ← 75 | 76 → there will be a first phase in which the work will decrease dramatically, which in turn will raise the need for the redistribution of employment. This phase is then followed by a second phase of a cultural nature, in which man will have the leisure time to occupy his free time with other interests. This will lead finally to the third phase in which a change in the moral code will serve as the culmination of the two transitional earlier periods.
As Keynes concludes:
I see us free, therefore, to return to some of the most sure and certain principles of religion and traditional virtue – that avarice is a vice, that the exaction of usury is a misdemeanour, and the love of money is detestable, that those walk most truly in the paths of virtue and sane wisdom who take least thought for the morrow. We shall once more value ends above means and prefer the good to the useful. We shall honour those who can teach us how to pluck the hour and the day virtuously and well, the delightful people who are capable of taking direct enjoyment in things, the lilies of the field who toil not, neither do they spin. […]
of course, it will all happen gradually, not as a catastrophe. Indeed, it has already begun. The course of affairs will simply be that there will be ever larger and larger classes and groups of people from whom problems of economic necessity have been practically removed. The critical difference will be realised when this condition has become so general that the nature of one’s duty to one’s neighbour is changed. For it will remain reasonable to be economically purposive for others after it has ceased to be reasonable for oneself.
The pace at which we can reach our destination of economic bliss will be governed by four things – our power to control population, our determination to avoid wars and civil dissensions, our willingness to entrust to science the direction of those matters which are properly the concern of science, and the rate of accumulation as fixed by the margin between our production and our consumption; of which the last will easily look after itself, given the first three. […]
3. The Responsibilities of Artificial Intelligence
A phenomenon much discussed today is the emergence of so-called unnecessary work (Graeber 2013). Just open the pages of financial newspapers to read the growing concern surrounding this phenomenon, which is causing a deep malaise among those most affected. Technological unemployment, in fact, extends not only to industrial and production activities, but is also related to new intellectual activities, which are facilitated by easy-to-use technological tools and therefore cause the need for a smaller labor force.
Few people are willing to admit that their professional tasks could actually be carried out in significantly less time. In fact, many see the creation and maintenance of fictitious jobs at companies willing to create surplus labor market for ← 76 | 77 → fear of falling into a kind of social depression that could cripple the current social order, based on the combination of production-consumption.
Indeed, psychology, and especially cognitive psychology does not underestimate this issue, noting that depression is a social phenomenon linked to modern times and periods of unemployment. When there is no concept of paid activity, such as in prehistoric times, are people depressed?.
In this scenario, since progress today is primarily technological, what responsibility can be attributed to this technology and, in particular, to Artificial Intelligence (AI), that is heading increasingly in the direction of creating machines and intelligent systems that might become capable of replacing humans, even in intellectual tasks?
When Keynes delivered his vision of this utopia, it generated a heated debate within the disciplines of automation, the forerunner of today’s AI. Scholars wondered whether it was ethical to study automation, and what systems and technologies could replace man in his daily activities and work. This discussion continues today, especially in light of the effects of technological progress on employment.
Taken from a historical perspective, it is obvious that technological innovation and human work evolved and continued to evolve in a symbiotic way, increasingly defining a framework in which human intervention is less and less necessary. Electronics, automation, information technology and the culmination reached with Artificial Intelligence, have meant that the pace of change taking place has become more and more accelerated, causing confusion and distress in all that part of humanity that, still immersed in the daily needs, has not embraced the utopian vision of Keynes.
ICT in general has already provided support to daily activities and work, has streamlined workloads and, in many cases, transformed or even made obsolete some professions or trades. Publishing is the sector that is suffering the most from this process (Greco et al. 2013). AI is leading to changes having an even greater impact, which often is not obvious but that now pervades every aspect of our lives. Indeed, cybernetics, bionics, robotics, neural networks (and, more generally, machine learning algorithms) have brought about significant changes in productive activities and services. The use of artificial intelligence systems has been extended from applications in scientific computing to more general domains. Algorithms and intelligent machines are used in various fields. The economic, business, medical, industrial and legal sectors are among the most obvious, but in general, any area in which predictions need to be made (i.e. by applying a sort of human intuition) using data without sufficient prior knowledge are affected by the change. One example of this is fraud detection machines and algorithms ← 77 | 78 → that can identify a fake check, fraud, or a scam, and lock the transaction. In the medical field, machine-learning methods are used for diagnosing and prognosing diseases, or for making efficient imaging machines that are able to detect pathologies. Automation, which is the field from which AI (cybernetics) has been playing an important role since World War II is a reality in industry today. The transport system uses AI algorithms (e.g. the state railways since the ‘80s) to regulate traffic. Even in the legal field, there are various applications of AI algorithms, such as the system SEL adopted by the Appeal Court of Rome, Italy, to automate the civil process, providing advice to judges to determine quickly the formal and substantial correctness of the preliminary stages of a trial. Scotland Yard uses an artificial intelligence system that can detect obvious similarities between different types of crime. Furthermore, experts in the financial markets use systems based on neural networks for making financial predictions. In the defense field, voice, writing and image recognition systems have been a reality for many years. In telecommunications, AI algorithms are used for signal cleanup (Vaseghi 2008). Weather forecasts are made using AI tools (Gardner and Dorling 1998). Search engines on the web, the systems of commerce and e-commerce are, too, based on AI algorithms (Lawrence 2000). Even in sports (Lapham and Bartlett 1995), or in the human resource selection process (Mehrabad and Brojeny 2007), these systems are used to improve process efficiency in, respectively, training and recruitment.
Today, our daily activities are based largely on Artificial Intelligence algorithms and the use of intelligent machines, though these instances may not be as obvious as the other applications mentioned previously. The wide field of AI includes, in fact, not only systems and algorithms, but also cybernetics and robotics, also more and more present in the daily life, not only in industrial automation, but also in applications such as exoskeletons (Yang et al. 2008) or robotic assistance to humans (Jacobsen et al. 2004). For instance, robot appliances, but also nurse cyborgs are already being used in some Japanese hospitals. In this socio-economic climate, Karel Čapek, who coined the term ‘robot’, foresaw in his play RUR (1920) (Čapek 2004) the possibilities inherent in the construction of artificial beings cheaper than the human being, but performing the same tasks. We have come a long way from Shakey (Nilsson 1984), the first mobile robot on wheels, which was created at the Stanford Research Institute in Menlo Park, California, during 1967–69. Think of the progress made with Asimo (Chestnutt et al. 2005).
In recent times, the excess labor force in industry was moved from manufacturing to services and, later, when it was revealed to be exuberant also in services, it was hijacked in the ICT sector (which employs 40 percent of the active population ← 78 | 79 → in the advanced countries). What will happen after? Will there be other emerging sectors or, rather, the utopia of Keynes? Do the times require a slowdown?
If answer to this question seems to be an obvious ‘yes’ because humans actually have difficulty in adapting so quickly to the new social order, then why is AI accelerating to create machines that can replace humans?
One might say that all AI experts are proponents of a Keynesian utopia, in fact, now there are neural networks that replicate the mechanisms of the human brain almost completely and very closely. We should start from the past to fully understand how strong man’s urge was to create machines that can replace us in everyday life. This calls to mind Pascaline (1642) created by Blaise Pascal, a computing machine created for calculations; Babbage's wheel calculators; the creation of ENIAC1 (1946) by Von Neumann. In contemporary AI, machines and algorithms (ANN, Artificial Neural Network, SOM, Self Organized Maps, machine learning, automatic learning) tend to replicate and replace man, imitating his cognitive processes. These efforts aimed to create thinking machines, as created by Turing in 1950. When thinking of AI, who does not think of HAL, the on-board computer in the film 2001: A Space Odyssey by Stanley Kubrick?
The great challenge of AI is to expand the capacity of artificial systems by combining the creativity, judgment, and intuition of human intelligence, with the speed, accuracy, and attention to detail of artificial systems. Without going into the philosophical or ethical questions, it is clear that being able to create a system that brings together human and artificial abilities can result in a product usable in any context, with some guarantee of success.
But will AI actually be able to create systems and machines that could replace humans?
In the field of AI, there is an eternal debate that pits those who support the so-called strong position against supporters of the weak position. In the philosophy of AI, the strong position (strong AI, a term coined by John Searle) argues that forms of AI can be made to reason and solve problems, and demonstrate self-awareness:
According to strong AI, the computer is not merely a tool in the study of the mind; rather, the appropriately programmed computer really is a mind. (John Searle)
On the other side of the debate, weak AI supports the use of programs to study or solve specific problems, excluding the possibility of achievement of self-awareness ← 79 | 80 → for machines, which can never be defined as intelligent because they cannot actually think. About the IBM supercomputer Deep Blue2,Drew McDermott wrote: “Saying Deep Blue doesn’t really think about chess is like saying an airplane doesn’t really fly because it doesn’t flap its wings”, arguing that Deep Blue possesses limited intelligence restricted by the size of its intellect. On the contrary, many refute this claim, arguing that Deep Blue only follows a program encoded in it.
Proponents of the weak AI argue that machines can never truly become intelligent, and therefore can never replace humans, while the supporters of strong AI believe that attaining true self-consciousness for machines will be possible in the future.
Von Herder asserts that there is no space for AI. He wonders, in fact,
“what does it mean to think? Speak inwardly, that is expressing itself marks acquired. Speaking means think aloud, in the flow of these thoughts, much can be for us only supposed and opined; but if I think really an object, it never happens without a sign. In thinking, the soul continually creates a unit of its manifold” (Von Herder 2002).
Conversely, Dennett argues:
Well, then how can the brain extract meaning from certain things? At what point can we talk about consciousness? These are the questions to which the cognitive sciences are trying to give an answer, trying to reduce the internal representation and those who experience the above representation of the machines. A computer can do it. The great insight of Turing was this: reduce the semantic machine to a syntactic machine. Our brains are nothing more than syntactic machines, which, however, extract meaning from the surrounding world, or work as semantic machines. We are in the presence of a paradox, but not a mystery, as many would have us believed. I do not believe in mysteries, they are only problems that we do not know how to approach. If we think we have found a mystery, we probably just misunderstood the problem. What is certain is that consciousness is less mysterious than you think: it develops from what the brain does – or how syntactic machine – and not by what it is made from. (Dennett 2006: 42).
The action of the mind unfolds, according to Searle, through intentionality, a basic property of the mind, a mental process that connects the inner world to the outside world.
The ability of speech acts to represent objects and states of things in the world – says Searle – is an extension of the most biologically fundamental ability of the mind to relate ← 80 | 81 → the body with the world, by means of mental states such as belief and desire, and, in particular, through action and perception. (Lyons 1995)
But does the strong position of AI tend to create thinking, creative machines? There are already many attempts to do so. Among others, Lamus3 was presented in July 2012, a super computer that composes classical music, designed and built at the University of Malaga by a research group in computational intelligence (coordinated by Eng. Francisco J. Vico), assisted by the pianist Gustavo Diaz-Jeres. The objective was to test the Turing test, and, in fact, the first concert on a computer (with the title Can machines be creative?) was streamed on July 2, 2012 and dedicated to Turing.
Even earlier, in 2010 in Udine, the poet and mathematician Hans Magnus Enzensberger created a machine that writes poems automatically, in complete independence4.
There are also mini-robots developed at the University of Lausanne, which, equipped with a brain based on artificial neural networks (imagine those of an insect), use a mechanism of ‘electronic reproduction’, evolving according to the laws of natural selection: the robots have the best chance to reproduce, and then to combine the ‘digital genome’ (or the weights of the neural network of the artificial brain) with that of another sample (randomly). Within a few generations, the robots have demonstrated an increase in intelligence, the ability to find ‘food’ independently (locating a charger) and avoiding ‘poison’ (a location similar to that of the ‘food’ but causes deactivation of the robot). Robots have also learned how to alert the presence of food to one another using light emitters they are provided with, thus developing a kind of language.
Furthermore, in 2012 at the Massachusetts Institute of Technology laboratories, researchers developed a prototype robot that can change its shape. The project was called Milli-Motein5 and its robots have extraordinary potential. Consider, ← 81 | 82 → for example, potential applications in domestic daily life, such as a lamp that can be transformed into a cup.
Finally, the SyNAPSE system (Systems of neuromorphic Adaptive Plastic Scalable Electronics) announced by IBM in 2009 emulates brain capacity linked to feelings, perceptions, actions, interactions and cognition. IBM’s aim is to build a chip for cognitive calculating. In cooperation with a team of researchers from the Lawrence Berkeley National Lab and Stanford University, the company has built a particularly innovative simulator of calculation mechanisms, memory and communication. It is also innovative for the biological details inspired by the neuro-physiology and neuroanatomy6.
AI efforts are, however, currently oriented towards the creation of computational models that can simulate intelligent systems (or be used by intelligent systems) to understand, but even more so to reproduce the workings of the human brain. Intelligent systems that derive from or benefit from this are the most diverse: think of agents, robots, systems in the environment, entertainment and learning applications that are already an everyday reality, tools that replace humans in their activities.
All this might be frightening, obviously, for all the reasons already discussed. But then why do humans want to create intelligent machines? Why do AI scientists pursue these goals?
It is not easy to answer these questions, and the Keynes’ utopia seems the most plausible explanation: the reasons are to be found in the human aspiration to gain more free time and the brain-spiritual dimension.
Surely AI, above all other ICT disciplines, has great responsibilities in today’s employment crisis and, moreover, the process has not yet been arrested: every day machines and systems are becoming not only more powerful, but also more creative. If we believe, in fact, that AI will always be in the service of intelligence and of human motivation, then it is sufficient to develop fast, flexible, powerful and mobile systems and adapt over time to the connected changes. If, on the contrary, the intention is to make the most of the opportunities to develop these machines, then artificial minds need to show human characteristics, such as creativity, mindful of the inherent risks. Therefore, technology and AI will wonder if the machines will have to feel part of a connected reality or simply be the means of connection, thus favoring, or slowing down, the process of realization of the Keynes’ utopia, for which they are largely responsible. ← 82 | 83 →
With regard to Keynes and his utopia, what aspects are still timely, and which have forcefully returned to the forefront? It appears that we are right in the first phase predicted by the economist. In fact, even Japanese efficiency had to surrender, due to the increase in unemployment caused by the progress of technology (think of Fujitsu which, despite the success of the company, has laid off thousands of people and announced a hiring freeze). Staff reductions, in fact, no longer concern only businesses that are in crisis. Instead, it extends to all the companies that have chosen or, rather, had to choose a high-tech model, opting for what is now called “jobless growth” or “development without employment” (Caballero and Hammour 1998). But will this model really result in greater levels of creative, richer intellectual activity, a life pervaded by aesthetics, freed from economic needs, the objective of which will be the subjective self?
Keynes’ speech seems prophetic in light of what we are experiencing. It is hoped that his utopia will soon come to fruition, raising us from the overwhelming and inevitable anxieties of the middle period in which we are living. We can’t say if the technology and, in particular, the AI can be of help in the solution to the current situation, or act as accelerators for the final structure advocated by Keynes. However, surely the involvement of technological disciplines makes them responsible for the pressure of events, if not for the entire process.
Obviously, AI is not yet able to totally replace human beings. Instead, systems and machines created using AI technology are becoming a valuable source of support to the human decision maker in a growing number of situations, or as a replacement for dangerous or heavy kinds of work. It goes without saying that progress in general has always led to a transformation and, given the acceleration of technological progress, even in this case technology has led to the disappearance of certain professions and trades. However, the responsibility of technological progress and AI, which is state-of-the-art technology, must be shared with social and economic practices, which thus far have not had the effects suggested by Keynes (e.g. reduction of working hours) but try to maximize profits at the expense of liberation from work as put forth in the Keynesian utopia.
Meanwhile, waiting to see if the Keynesian utopia will be realized, humans will have to live with fears about the misuse of technology, which have found expression in artistic masterpieces such as Brave New World (Aldous Huxley 1932), Modern Times (Charlie Chaplin 1936) and 1984 (George Orwell 1949), who were able to masterfully interpret fears of technological domination present in the collective imagination. ← 83 | 84 →
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1The ENIAC was absorbing a large amount of electricity, so much so that its first power-up caused a blackout in the western district of Philadelphia. Like all the first samples of computers, it was very cumbersome, occupying a space of nine by thirty meters (180 square meters) and weighed about 30 tons.
2Deep Blue is the IBM RS/6000 computer equipped with 512 processors working in parallel and programmed to play chess. On May 11, 1997, it beat the strongest human chess player in the world, Gary Kasparov. No one argued that mankind had finally built a thinking machine, but the IBM computer had been shown to have an intelligent behavior greater than the challenger.
4The machine was manufactured by Solar SpA, Udine. Enzensberger “wanted to experience concretely the theory that has fascinated many writers, beginning with von Chamisso, poet at the turn of the eighteenth and nineteenth centuries, but especially the work of Raymond Quenau and of surrealists like Breton. The first, with its ‘factory of potential literature’ has shown that you can write a million of billion sonnets starting from the verses of one poem”.
5Milli-Motein is a robot similar to proteins, which naturally change their shapes. See “I robot di Losanna” (episode of SuperQuark): <http://www.rai.tv/dl/RaiTV/programmi/media/ContentItem-446dbf19-e344-401b-b7fe-67698c4daad5.html?p=0>.
6Currently IBM has stated that the system shows the characteristic of a cerebral cortex of a cat (about 4,5% of human brain).