AFTER DIGITALISATION
RJ 2022

THE LIMITS OF ARTIFICIAL INTELLIGENCE

Hans Ruin

Translated by Clare Barnes

Riksbankens Jubileumsfond
in collaboration with Makadam förlag

MAKADAM FÖRLAG

GÖTEBORG & STOCKHOLM

WWW.MAKADAMBOK.SE

After Digitalisation is the 2022 Yearbook for Riksbankens

Jubileumsfond

Editor: Jenny Björkman

Editorial board: Nicklas Berild Lundblad, Ingrid Elam, Lisa Irenius, Sven Anders Johansson

THE LIMITS OF ARTIFICIAL INTELLIGENCE

© Hans Ruin 2022

Graphic design: Johan Laserna

eBook: Bulls Graphics, Halmstad 2022

ISBN this volume 978-91-7655-406-7

ISBN for the box set of six essays: 978-91-7061-404-0

ISSN 2000-1029

This work is licensed under Creative Commons — Attribution 2.5 Sweden.

To view a summary of the licence, go to http://creativecommons.org/licenses/by/2.5/se/ or contact Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

Contents

Foreword: After Digitalisation

Hermeneutic perspectives on AI

The road to AI

Phenomenological criticism and AI

A new AI and new fantasies

On the need for a critical humanist discussion about AI

For a freer view of technology

Notes and list of illustrations

Presentation of Riksbankens Jubileumsfond

Foreword:
After Digitalisation

“After digitalisation”? The theme could give the impression that digitalisation is complete: first we lived analogue lives, then digitalisation arrived, and now it is finished. But the “after” of the heading does not mean that digitalisation is a stage we have passed through, nor that the relationship between the digital and its analogue counterpart is as simple as we imagine. Nevertheless, perhaps we have arrived at a situation or state in which we can gain perspective on digitalisation, and examine its effects and conditions in a critical and nuanced manner.

The essays in this collection are wide ranging in their subjects, but they all relate to the question of what the digital is and how it affects our lives. Nina Wormbs’ essay, on how what is often presented as an ethereal sphere of ones and zeroes, beyond the material world, examines the extent of its hunger for resources and its impact, with the vast amounts of e-waste it produces. Malin Åkerström’s text about meetings as a phenomenon demonstrates how digital technology encourages both a new meeting etiquette and new opportunities for escape. In turn, Orsi Husz reveals the continuity behind the transition from a paper-based regime of identity to digital surveillance capitalism, and Jesper Olsson describes how binary code infiltrates and reorganises not just reading, as in the case of audiobooks, but culture in general, while demonstrating how the new is always more intricately intertwined with the old than it may appear. In area after area, the consequences of digitalisation appear to be double or, rather, dialectical. The digitalisation that promised simplicity seems, in reality, to lead to the generation of increasingly complex systems, writes Nicklas Berild Lundblad. If technology reveals the world, simultaneously it also appears to hide it. In this essay, philosopher Hans Ruin writes about the limits of artificial intelligence, and about the enormous expectations long placed on machines. These hopes for AI, artificial intelligence, are paradoxical, because they link together a desire for control with the fear of submission to technology.

If we are to understand the increasingly intense interaction between human and machine, technology must be historically and philosophically located, says Ruin. His basis is philosopher Martin Heidegger’s idea that in the era of technology the world is controllable, and reality thus becomes a resource. When mathematics assumes philosophy’s role in explaining how everything is linked, everything becomes measurable. Mathematical thinking leads mathematicians, such as Alan Turing, to the “thinking machine” and makes others, such as John von Neumann, warn of the singularity, when computers will destroy the human race.

But, states Ruin, the AI prophets need to take a reality check – because what is intelligence, actually?

The Editors

Hermeneutic perspectives on AI

A recurring idea in the literature about what could be called post-digitality or after-digitality, is that humankind has reached a point where we need to release ourselves from the computerised expert systems that encompass and increasingly control our daily lives. This idea reflects a central trope of twentieth-century philosophy of technology: the rift between a striving for control and the fear of losing control. Alongside nuclear power, computer science, cybernetics and artificial intelligence (AI) all manifest humanity’s deeply rooted and long-practiced striving to ensure we control our surroundings. These innovations awaken our dreams and our anxieties: how the technological artefacts we use to help us guarantee power and survival, will result in the opposite, namely powerlessness, subordination and death. Artificial intelligence is currently the main focus of this anxiety.

My purpose here is to use a philosophical and cultural-scientific perspective to reflect this situation, perhaps helping us to intellectually navigate this new landscape. Because conceptions about AI are encapsulated in myths – like that of a fast-approaching “singularity”, when computers will take over the world – it is reasonable to recapitulate its history. The aim is to show how we can understand and discuss the consequences of modern technology more freely and more constructively, using a philosophical and cultural perspective. Beyond both the digital and analogue intelligences, there remains a need for a historical humanistic and hermeneutic intelligence, if we are to understand how our technologies are part of a larger philosophical and cultural context.

The road to AI

In June 1938, philosopher Martin Heidegger had been invited to give a lecture in Freiburg, as one of a series on modernity’s “world picture”.¹ In his article, he covers – for the first time – what was to become a recurring theme in his later works, namely the philosophical and metaphysical meaning of technology in modern humans’ self-understanding. He writes: “machine technology still remains the most visible outgrowth of the essence of modern technology”. The growth of technological knowledge and the control of nature is not just of enormous practical importance, it also increasingly governs our ways of seeing and thinking. In the age of technology, the world is transformed into an image of itself. Heidegger thus flipped the title of the event to which he had been invited; instead of talking about the contemporary world picture, he focused on how the world in the age of technology becomes something staged as a technological representation, and thus something that can be monitored and controlled. Our time, he writes, is a time “when the world becomes picture”.

In a lecture series on Kant and the thing-in-itself from around the same time, Heidegger interprets modernity using the growth of mathematics and the mathematisation of nature, as developed by Galileo and Descartes, among others. Hereafter, mathematics functions as a fundamental choice for thinking and experiencing the world, while mathematics is maintained by a thinking intellect at its heart. In this way, the modern view of human subjectivity as the foundation and centre of knowledge converges with the mathematisation of nature at an early stage, in an overarching stiving to make the world comprehensible and controllable from the horizon of cognisance.

Two years before Heidegger gave his lecture in Freiburg, the then just twenty-four-year-old British mathematician Alan Turing published what was also to become a legendary essay, with the title “On Computable Numbers with an Application to the Entscheidungsproblem”. The essay targeted his older German colleague David Hilbert’s attempts to develop a method that, once and for all, would determine whether a particular mathematical theorem was true or false. A few years previously, the renowned Austrian logician Kurt Gödel had exposed the limitations of Hilbert’s programme. Turing’s article made it even more obvious that hopes for such a definitive method were in vain. Turing’s conclusion was also negative; namely, there can be no such procedure or algorithm. To prove this negative result, he invented a universal machine. This was not an actual technological device, rather a thought model, or a theoretical machine. He first called it just “a-machine”; later, it would come to be called simply the Turing Machine and be considered the theoretical foundation of computer science.

Turing’s goal at this time was not to construct a physical machine, but rather a general calculation procedure for all imaginable (and mathematisable) problems. During the following decade, however, the practical consequences of these abstract thought models would have an undreamed-of practical application, thanks to the close cooperation between mathematicians and engineers as part of a military operation. Turing was recruited to the British intelligence services and sent to Bletchley Park, the British cryptanalysis centre close to London. The intention was that he would contribute to the project by building a physical machine that would crack the German military’s Enigma code.

In this secret battle between German and British mathematical talent, which has recently been depicted and mythologised in books and films, the outcome of the war was decided in hidden laboratories by the first generation of computer engineers. As this was taking place, their colleagues across the Atlantic were working against the clock, in equally secretive laboratories, to develop military applications for theoretical nuclear physics.

Thus, the full potential of mathematical knowledge, which up until then had been cultivated in quiet, secluded environments, became apparent during World War Two. Now it stepped forward as the language, the art - the techne - through which the world could be controlled and governed; it was foreseen that mathematical thought would soon be converted into technology and take over the baton from philosophy in the form of “thinking machines”.

Only a decade later, the Hungarian-American mathematician John von Neumann, in 1948, declared that computers would soon exceed human intelligence, an event he called the “singularity”. In 1950, Alan Turing published another classic essay, “Computing Machinery and Intelligence”, in which he also argued that machines would soon be thinking like humans. To determine its occurrence, he devised an imitation game – later known as “the Turing test” – in which an evaluator communicates with a machine without knowing whether it is a human or an artefact. When it is no longer possible to determine which is which using external criteria, the machine can justifiably be said to be intelligent.

This behaviourist perspective leads to the conclusion that intelligence is performance (albeit within a restricted domain). In 1956, at a legendary conference at Dartmouth College, New Hampshire, US, von Neumann’s student, John McCarthy, coined the phrase “artificial intelligence” for this new technology. The road thus appeared to be staked out for a project of cosmic proportions; humanity was moving towards recreating the ability that, through a long tradition of philosophical and theological speculation, had been regarded as our most valuable and unique characteristic; yes – even that which links us to God – namely, our intelligence.

The vision of this potential soon actualised another question: what will this mean for the future of humankind? This concern quickly found its way into literary and artistic creations. There were, of course, precursors. In literature, Mary Shelley’s Frankenstein (1818) had captured the dawning technological era’s concern about the possibility of artificial life, through the relatively newly discovered electricity. The Victorian author Samuel Butler’s book Erewhon (1872) later portrayed a new race of machines which, in the spirit of Darwinism, gain consciousness and take over the world. And, finally, the Czech author Karol Čapek minted the word ‘robot’ (from a Slavic word for a forced labourer) in his play R.U.R. (1920), as a name for a mechanical being that rebels against its maker.

The outstanding technological breakthroughs that occurred, one after the other, from the early 1940s onwards, however, fed the growth of a new wave of dystopian literature. In this context, one particularly fascinating personage is John Good, one of Turing’s colleagues at Bletchley Park. In 1965, he published an article in the New Scientist, where he imagined an “ultraintelligent machine” which, in turn, could generate new machines. Every new generation of machines would increase their capacity, rapidly leading to an “intelligence explosion”, the phenomenon that von Neumann had already called the “singularity”. Consequently, it was John Good that Stanley Kubrick and Arthur C. Clarke approached for scientific advice about their work on the script for 2001: A Space Odyssey (1968). The supercomputer in the film – and the subsequent book – Hal9000, was to thereafter haunt the collective unconscious as the paradigmatic image of an artificial brain which, at a decisive moment, refuses to obey its user and turns on him instead.

In the same spirit, Hannes Alfvén’s pseudonymously published dystopian work The Great Computer: A Vision (1966, 1968), describes a society at an unspecified point in the future, when supercomputers have taken over most vital functions and made humankind increasingly superfluous.² In the end, this civilisation collapses from the weight of its complexity. This occurs when there are no living humans left to understand how the machine is constructed, and everything must start over from the beginning.

Phenomenological criticism and AI

At this time, there were rising stakes for perspectives on what the technologisation and mathematisation of nature entailed for humanity’s relationship to the world. In 1954, Heidegger published a much-cited article, “The Question Concerning Technology”, in which he developed his ideas from the previous lecture, namely that technology is not essentially about the development of artefacts for one purpose or another, but is instead an entire thought system through which humankind seeks to control nature and our existence. In extension, this not only affects our way of experiencing the world, but also ourselves.³

Heidegger establishes a concept for describing the form of thought that arises: Ge-stell, the enframing, a means through which everything is established and ordered in a manageable structure, including thought and language. His belief was that as long as we hang onto the traditional image of technology as a tool or a means of achieving an aim, something up to us to control, we are not grasping technology’s deeper effects on society. From Heidegger’s perspective, technology is never simply a tool; it is also a means of relating to the world that affects how the world appears to us. Through technology, the world tends to appear as something to utilise, regulate and control. He often returned to these issues in his conversations with the Swiss psychoanalyst Medhard Boss in the 1950s, and explicitly links them to Harvard mathematician Norbert Wiener and his “cybernetic” science about control systems for nature and society. Ultimately, from this perspective, language also becomes a calculable artefact.⁴

Heidegger never refers to the new concept of artificial intelligence, and Wiener was only marginally involved in the project conducted in this field by his more technically oriented colleagues at Harvard and MIT. However, due to his critical reflections on cybernetics, Heidegger can be said to have indirectly participated in the early discussions about artificial intelligence. Either way, his ideas would later be the inspiration for the first more comprehensive philosophical confrontation with AI and its claims, namely Hubert Dreyfus’ book What Computers Can’t Do, from 1972.⁵ Dreyfus, who was a phenomenologically schooled philosopher, had studied at MIT in the 1960s and been personally acquainted with the utopian pioneers of AI who gathered around cognition scholar Marvin Minsky. Using Heidegger’s and Maurice Merleau-Ponty’s philosophical analyses of the mind and the body as a basis, Dreyfus stated that the entire research programme responsible for AI, as it was conducted at the time, rested on an overly restricted view of intelligence; this corresponded to the abstract management of symbols with no connection to the human body, the immediate life-world, or history.

However, it was not Dreyfus, but his colleague at Berkeley, John Searle, who would receive particular attention for his philosophical criticism of AI. In an article from 1980, he described a now famous thought experiment that he named “The Chinese Room”. This thought experiment directly targeted Turing’s previously established behaviourist criterion for intelligence.⁶

Searle’s point was that even if a machine succeeds in imitating intelligent behaviour, such as providing relevant answers to questions asked in Chinese, its ability still rests on a thoughtless shuffling of symbols that lack meaning for the machine itself. Therefore, said Searle, there is no reason to ascribe human qualities to the machine other than in a metaphorical sense. A computer may be able to make chess moves, or provide correct diagnoses or reasonable answers in a conversation, but ultimately there is no one there who is aware that they are involved in these activities. Searle’s criticism targeted the behaviourist conditions for ascribing intelligence to a machine. His point was that there is a way of being for yourself, a self-awareness, that is an important aspect of what it is to actually understand something.

Many of the practically oriented engineers who were occupied with machine intelligence found these theoretical discussions about the essence of intelligence of little interest. When faced with the complicated and demanding task of creating a machine that could replace a human ability, such as playing chess, translating a text, making a diagnosis or driving a vehicle, the essential factor was not whether the machine would be regarded as “intelligent”, but whether it could perform and produce intelligent results.

Nevertheless, the philosophical criticism was provocative for some advocates of AI research. It was somehow important for these engineers to not only be regarded as practitioners with a technological task to solve, but also as theoreticians on the way to understanding human intelligence, perhaps even solving the riddle of the nature of consciousness. Sometimes there was also a financial aspect. The perception that AI was about to reveal the essence of the soul, or of meaning, or to give rise to a new silicon-based post-human lifeform, led to media attention and thus increased funding. The fact that inventor and futurist Ray Kurzweil, formerly responsible for Google’s research unit, spends several pages of his famous book from 2004 about the “singularity” in trying to refute Searle’s then twenty-five-year-old article is testimony to what was at stake.⁷

The desire to defend AI against philosophical criticism is also linked to how science as a whole developed in the last decades of the twentieth century. As it became apparent that the early predictions of new technology’s rapid success were not materialising, researchers began experimenting with new models for increasing the performance of computers, especially through the use of neural – self-learning – networks, which appeared closely related to the structure of the brain.

However, even with drastically improved processors, there was no great breakthrough. A shockwave went through the chess world when Deep Blue, IBM’s supercomputer, defeated Garry Kasparov in 1997. Some people interpreted this as if AI was, despite everything, on the way to fulfilling its early promise. However, not even the people who had created the programme could claim that it was intelligent in any sense other than being able to produce effective chess moves. The software lacked strategy, could not draw conclusions from previous experience of the game and was useless for anything other than playing chess. In that sense, not even this performance was an argument for AI now having succeeded in creating true machine intelligence.

In retrospect, the 1980s and 90s appear to have seen a falling-off in AI research; in fact, the entire field began to get a slightly dubious reputation. This is depicted in an illuminating manner in book that the New Scientist published a few years ago, with the title Machines that Think: Everything you Need to Know about the Coming Age of Artificial Intelligence.⁸

A new AI and new fantasies

Today, the situation is different. AI is once again being talked about by everyone. It has its new evangelists and its doomsday prophets. Aesthetic representations of threatening or tender robots are once again populating films and literature: the Westworld series, Kazuo Ishiguro’s Klara and the Sun and Ian McEwan’s Machines like Me, to give just a few examples. Even von Neumann’s and Good’s fantasies about the “singularity” and “intelligence explosions” are being relaunched by researchers, philosophers and science journalists, who are paid huge sums for their lectures. Above all, the commercial applications and interest are again at the forefront.

The explanation for this change has an interesting philosophical dimension. As long as AI research believed in recreating human intelligence, it was stuck in a rut that did not lead to the promised results. The real breakthrough came when engineers abandoned the idea of trying to model human intelligence and its functions, and instead focused their efforts on creating machines that could “learn” on their own, using algorithms that statistically process enormous and continually growing data volumes.

The situation can be illustrated through contemporary translation software. Translation between different languages was promised at an early stage of research into AI but, despite great efforts being made to construct software that would imitate the skills of an experienced human translator, they never achieved an acceptable level of performance. Instead, successes were first achieved in the first and particularly the second decades of the twenty-first century, through the creation of a new type of recursive software, which uses an ever-growing text database to generate statistically calculated probabilities that a particular expression or statement in the target language is equivalent to the source language.

The same models are now used for facial recognition and in self-driving vehicles, as well as in securities trading. These software programmes demonstrate the great intelligence of their creators, but no one can credibly claim that the machines understand the language they translate, realise what it entails to drive a car or that they are in any reasonable sense autonomously intelligent.

And yet we are still fed dramatic depictions of how intelligent robots are ready to take over the world in a fast-approaching future. It is not only cultural entrepreneurs, authors and scriptwriters who disseminate these narratives; they also come from prominent philosophers and physicists. Among the best-known are two Swedes, philosopher Nick Bostrom (Niklas Boström) and physicist Max Tegmark. Their bestselling books about AI and an imminent superintelligence argue that the self-learning machines now on the market give rise to the idea that humanity will soon be facing an uncontrollable form of “intelligence explosion”.⁹

For an entrepreneur such as Ray Kurzweil, this is something auspicious, while Bostrom and Tegmark both tend to see something menacing, something which requires countering, both practically and in terms of research. However, what unites all three is that, based on their fantastic scenarios, they have succeeded in collecting large sums of money and, in Bostrom’s case, established the well-funded Future of Humanity Institute at Oxford, which uses ethics to calculate the desirable consequences of dreamed and actual events.

Naturally, even if we have reason to be vigilant and critically discuss the effects of the new technologies now being implemented in our working lives and societal functions, there is an underlying problem with the dizzying mages of the future that the loudest critics propagate. This criticism builds upon the same inaccurate model of what intelligence is, which led AI research in the wrong direction from the very start. Referencing the brain’s biological and chemical complexity, its intricate network of neurons and synapses, these influential AI debaters maintain that it is just a question of time until computers achieve a similar complexity of calculation power. Nevertheless, aside from the argument of pure complexity, there is no reason to assume that the extremely specialised software now being developed for clearly defined tasks will, through some form of random evolutionary logic, suddenly give rise to a super-being with its own internal motivations and associated organisational and political strategies and objectives.

Indeed, given the current level of technology, this appears to be a very improbable scenario. This is one of several “esoteric possibilities”, to quote the abovementioned book from the New Scientist, whose authors consistently state that contemporary AI prophets need a comprehensive “reality check”.¹⁰ Still, people flock to these fantasies, which are often driven by financial incentives and whose attraction power comes from the aesthetic and theological conceptions found deep in our collective consciousness.

On the need for a critical humanist discussion about AI

What is more problematic is that these threat scenarios risk obscuring the view on a discussion that is closer to hand, one about the value of using AI to achieve cost-efficiency and an apparently value-neutral increase in control and governance. The threat scenarios present a fragmented image of the expectations of technology, characterised by both a longing for absolute control and a phantasmagorical, terrifying submission. In this bizarre combination of affects, it is as if culture is generating both the dream and the nightmare of its own desires. Therefore, we need not less, but more philosophical, humanist and historically founded reflection that explains these phenomena in their cultural and historical contexts. Only then will it be possible to conduct a discussion at the requisite level.

Unfortunately, this split also, to some extent, characterises the situation within the humanities. Traditionally, humanists have been associated with scepticism about technology, whilst, like most others, they have reaped its benefits, particularly in information technology, with everything from printing presses to typewriters, computers and web archives. The humanities “technological turn” in the 1970s has made it popular to reject older kinds of interpretive and hermeneutical humanists as naïve reactionaries. For many, a philosopher such as Heidegger has come to appear as particularly so, not just because of his right-wing politics in the 1930s, but also because of his recurring criticism of the technological society. The influential (and too soon deceased) German media theorist Friedrich Kittler likes to highlight Heidegger as representative of humanists from a bygone age, who could not see how the traditional humanities were predestined to be assimilated into information and media theory.11 According to Kittler, the idea that humans and machines could be kept separated was erroneous, because they were already bound to each other.

A related argument had previously been presented by Jacques Derrida and, later, by French philosopher and media researcher Bernard Stiegler, who talked about how human consciousness, even from its inception, was characterised by a “technicity” through the use of a generally encompassing system of language and signs.¹² However, for Derrida, this kind of technification was always associated with a comprehensive striving to think critically about technology and humans’ relationship to it, as our era’s greatest challenge. In this sense, he remains closer to Heidegger than a media theorist like Kittler.¹³

Among many of those who now find inspiration in these arguments, those who encourage the humanities’ transformation to media theory and who perhaps visualise its future as that of “digital humanities”, there has often been a blindness that is reminiscent of those who imagine AI’s imminent conquest of the life-world. Instead of analysing and separating conceptual and historical levels, they allow themselves to be led by the affects that flow from the technological approach and its striving for control and governance. In addition, among the humanists who write about AI and other advanced information technology, there is often a lack of technological understanding that makes it difficult to analyse the systems they claim to talk about, which risks increasing the levels of mystification. References to algorithms become a conceptual black box that can be used to hold almost anything. By supplicating to the great Other in the form of information technology, they allow themselves to be voluntarily incorporated in the same dichotomous tension that characterises technological culture in its entirety.

It is only recently that a more critical humanist discussion about AI has begun to take shape under the interdisciplinary heading of Critical AI Studies. An anthology that gathers the discussion was published in 2021: The Cultural Life of Machine Learning: An Incursion into Critical AI Studies. It is edited by the British and Canadian sociologists and computer scientists Jonathan Roberge and Michael Castelle.¹⁴ In their preface, they open up for a more long-term social science and humanist perspective on this rapidly expanding technology, which is the subject of enormous investments and associated expectations and anxieties. They prefer to talk about machine learning (ML) rather than AI, precisely because of the misleading associations that have long been linked to the term. They describe what we are now undergoing as an “epistemic shock”, where the challenge is to formulate analyses that are aware of the field’s tendency towards self-mythologisation – such as in the above example from Bostrom and Kurzweil. Without getting lost in the details of the relevant technology, these analyses must also be grounded in a technical knowledge that makes them relevant to those involved in actually developing the systems.

Machine learning/AI has developed what Roberge and Castelle call “a cultural life of its own”. There are constant references to revolutions and tsunamis as leaps in development, and Roberge and Castelle believe that this is the explanation for the hype around ML and AI. Partly, this hype builds upon the already mentioned and off-target claim that it solves the great question of meaning, using an idea that is essentially symbol-based. At the same time, they say that the switch from classic AI to self-learning machines, ML, opens up new dialogues with phenomenology and hermeneutics.

Focusing on the interaction between human and machine within a larger theory about social interaction and communication allows this new technology to be situated within a paradigm of understanding, one that instead links the technology to critical theory and hermeneutics.

Rather than being seduced or repelled by these innovations and their imagined implications, the critically minded humanist researcher has the important task of trying to locate this increasing interaction between humans and machines in the correct historical and semantic context.

Naturally, the economic and military interests behind the current development belong to such an analysis. It is important to be attentive to the conceptions and prejudices that generally characterise the environments in which technology is developed, as well as on whose behalf this is done.

A critical discussion about AI/ML needs to take a clear step beyond the dichotomous model of utopia/dystopia. However, it also needs to be able to distance itself from the immediate demands that technology appears to place on ethical directives, which feed the type of institution that is dedicated to applied ethics. In these environments, work often focuses on using a complex reality to create refined examples, which can then be processed using models of decision-making theory for an optimal outcome within a clearly delimited domain. For example, this could involve how a self-driving car will react in a situation where it must choose between two bad options. Instead of developing a critical and reflective approach, here the philosophical models are generally used as an aid for the technology, as a type of authority for ethical certification. In the final chapter of the anthology, “Critical Perspectives on Governance Mechanisms for AI/ML Systems”, the American and Canadian media and information researchers Luke Stark, Daniel Green and Anna Laureen Hoffman argue that it is important to broaden the discussion and instead focus it on the societal and political consequences of the technology’s use, in “a more radical and inclusive democratic conversation around AI-governance”.¹⁵

The authors of this anthology have theoretical backgrounds in primarily critical theory and a hermeneutically inspired reflexive sociological tradition. They make links to an older discussion in their desire to develop an AI sociology in line with that of British sociologists Brian Bloomfield and Steve Woolgar who, back in the 1980s, formulated critical perspectives on contemporaneous claims for AI.¹⁶

Their approaches lead them back to the roots of sociology in hermeneutical theory, and thus to the kind of critical questions once formulated by Heidegger and Dreyfus, among others. It is interesting to see how the theoretical discussion about AI/ML has thus travelled in a loop and partially returned to where it once began. However, now it is not only facing fantastical imaginings, but also an actually existing and functioning technology that is rapidly becoming intertwined with every level of humanity’s life-world. Its pioneers’ dreams about what this technology would soon be capable of proved as inadequate as the models with whose help they tried to achieve them but, as technology found new ways, much of what was then imagined has become reality, such as the now increasingly functional translation programmes.

For a freer view of technology

Now, as we face functioning machine-learning technology that rests upon revised solutions, it is not only the older and mystically influenced conceptions that must be used to think about, analyse and control it. Instead, there is a need for a more hermeneutic, historical and critical social philosophy that can place actual functions and performance in their correct perspectives, based upon a familiarity with the technological solutions which can also formulate the relevant questions about understanding, meaning and dedication. Ultimately, technology must be understood and analysed from a holistic humanist perspective, one which can also weigh in question of justice, agency and overarching aims. This is particularly applicable in education, where the consequences of these digitalised expert systems’ entry into schools must be critically examined.

One fundamental task of an emancipatory philosophy is to think the actual reality, both the natural and the created, from the viewpoint of freedom; this involves training a free view of events that otherwise tend to assume the imaginary forms of fate and necessity. This is a pressing task in a time when many people are frightened of the genesis, introduction and use of systems they do not fully understand. Creating spaces in society where developments can be reflected upon is especially important when even intellectuals, who should be tasked with understanding and explaining what is occurring, lose themselves in mythologisation or in indulgent implementation that follows the money.

Initially, there was mention of how technological culture likes to generate its own myths about power, control and submission. The image of technology collapses between the road to increased control of the uncontrollable and the fear of lost control and submission. Given the influx of digital control systems at all levels of the life-world, some people’s return to analogue forms of living can be regarded as a sign of health. But, given all the exaggerated imaginings about the nature of artificial intelligence and how it may affect us, it is also important not to become lost in technological culture’s dichotomous hall of mirrors. Instead, we should maintain and develop our own intelligence, allowing us to view and interpret the contemporary technological reality in its historical and existential context.

Notes and list of illustrations

1. Later published as Martin Heidegger, “Die Zeit des Weltbildes”, in Holzwege, Frankfurt am Main: Klostermann, 1950.

2. Olof Johannesson [Hannes Alfvén], The Great Computer: A Vision (1966), trans. Naomi Walford, London: Victor Gollancz, 1968.

3. Martin Heidegger, “Die Frage nach der Technik”, in Vorträge und Aufsätze, Pfullingen: Neske, 1954.

4. Martin Heidegger, Zollikoner Seminare: Protokolle, Gespräche, Briefe, pub. Medard Boss, Frankfurt am Main: Klostermann, 1987. For a longer discussion of Heidegger’s technological philosophy and the concept of Gestell, see my “Gestell. Enframing as the essence of technology”, in Martin Heidegger: Key Concepts, ed. Bret W. Davis, Durham: Acumen Press, 2010, pp. 183–194.

5. Hubert Dreyfus, What Computers Can’t Do: A Critique of Artificial Reason, New York: Harper & Row, 1972. He followed it up fifteen years later with a revised version: What Computers Still Can’t Do: A Critique of Artificial Reason, Cambridge, MA: MIT Press, 1992.

6. John Searle, “Minds, Brains and Programs”, originally published in Behavioral and Brain Sciences no. 3, 1980, pp. 417–457.

7. Ray Kurzweil, The Singularity is Near: When Humans Transcend Biology, New York: Viking, 2005.

8. Machines that Think: Everything you Need to Know about the Coming Age of Artificial Intelligence, Boston: Nicholas Brealey Publishing, 2017.

9. Nick Bostrom, Superintelligence: Paths, Dangers, Strategies, New York: Oxford University Press, 2014 and Max Tegmark, Life 3.0: Being Human in the Age of Artificial Intelligence, London: Allen Lane, 2017.

10. Machines that Think.

11. Cf. Friedrich Kittler, “Farben und/oder Maschinen denken”, from 1996, published in English as “Thinking Colors and/or Machines”, Theory, Culture & Society vol. 23, 2006, pp. 39–50.

12. Bernard Stiegler, Technics and Time 1: The Fault of Epimetheus, trans. Richard Beardsworth & George Collins, Stanford: Stanford University Press, 1998. For Derrida’s analysis, see his Grammatology, trans. Gayatri Chakravorty Spivak, Baltimore: Johns Hopkins University Press, 1998, among others.

13. For a current book that takes the first comprehensive approach to Derrida as a philosopher of technology, see Björn Sjöstrand, Derrida and Technology: Life, Politics and Religion, Springer, 2021, first published in Swedish as Att tänka det tekniska: En studie i Derridas teknikfilosofi, Huddinge: Södertörn University, 2015. The book grew from a first attempt to establish phenomenological and hermeneutically based critical studies of technology in Sweden, which also resulted in the above-quoted anthology Fenomenologi, teknik och medialitet, a cooperation between researchers at Södertörn University and KTH.

14. The Cultural Life of Machine Learning: An Incursion into Critical AI Studies, eds. Jonathan Roberge & Michael Castelle, London: Palgrave, 2021. See also the recently published The Algorithmic Society: Technology, Power, and Knowledge, eds. Marc Schuilenburg & Rik Peeters, Abingdon, Oxon: Routledge, 2021 and Dal Yong Jin, Artificial Intelligence in Cultural Production: Critical Perspectives on Digital Platforms, London: Routledge, 2021. An interesting new contribution in Swedish that takes a humanist and critical approach to AI is the book edited by Rikard Roitto and Susanne Wigort Yngvesson, Teknoteologi: AI och människans villkor, Lund: Studentlitteratur, 2021, where the authors successfully use theology to approach the least fantastical side of new technology and how it actualises older conceptions of submission and control.

15. The Cultural Life of Machine Learning, p. 275.

16. Cf. Steve Woolgar, “Why Not a Sociology of Machines? The Case of Sociology and Artificial Intelligence”, Sociology vol. 19, 1985, pp. 557–572, and Brian Bloomfield, The Question of Artificial Intelligence, London: Routledge, 1987.

List of illustrations

Reconstruction of Alan Turing’s decryption machine that cracked the Enigma code. Wikimedia Commons.

Children practicing their self-awareness.

The match between Garry Kasparov and Deep Blue in 1997. AFP / Stan Honda.

Gates with facial recognition at Beijing’s western railway station in September 2019. Wikimedia Commons.

Handprint at Cuevas de las Manos, Argentina, that is around 9,500 years old. Wikimedia Commons.

Riksbankens Jubileumsfond: promotes, inspires and participates

Riksbankens Jubileumsfond (RJ) is an independent foundation with the goal of promoting Swedish research in the humanities and social sciences. The foundation was established through a resolution in the Swedish Riksdag in 1964, when a donation from Riksbanken (the Swedish Central Bank) was approved and the statutes adopted. RJ’s establishment was part of the tercentenary celebrations for the world’s oldest still operating central bank. These celebrations also included the Prize in Economic Sciences in Memory of Alfred Nobel and the bank building on Brunkebergstorg, Stockholm. The Riksdag and Riksbank aimed to use the foundation to benefit an urgent national issue: scientific research linked to Sweden.

For more than fifty years, the foundation has promoted research in the humanities and social sciences. In 2021, total funding for research and collaboration amounted to more than SEK 500 million. Hundreds of researchers in these disciplines have received grants for conducting research, building infrastructures and networks, establishing new contacts and participating in conferences and seminars, as well as in public debate.

RJ 2022

After Digitalisation

Editor: Jenny Björkman

Editorial board: Nicklas Berild Lundblad, Ingrid Elam, Lisa Irenius, Sven Anders Johansson