In the history of conceptualizing intelligence in machines, errors have played a key role. In mid 20th century cybernetics and computer science, leading figures insisted that the possibility of independent reasoning and creativity in machines hinged on a system’s ability to deviate or break from its original design and programming. A similar idea was espoused in 1980s critique of artificial intelligence (AI) but, this time, deviance, or error as a warrant of intelligence was placed with humans. The way humans err, it was claimed, testify to a type of mental performance permanently out of reach of machines.

Using these conflicting discourses as framing, this talk turns to the historical period between them and a series of experiments aimed at creating paranoid computers (essentially software with delusional traits). In focus here are the studies carried out by medical doctor, psychologist, and AI pioneer Kenneth Colby working at the Stanford Artificial Intelligence Lab (SAIL) in the 1960s and 70s. I argue that Colby’s undertakings capture essential aspects of both traditions mentioned, raising questions about what constitutes an error in a machine made to simulate human behavior. In highlighting the entanglements between error and intelligence in the history of AI, the talk notes their relevance for our current sociotechnical landscape. This includes not only the employment of chatbots to act as therapists for human subjects but also conceiving of AI systems themselves as objects worthy of analysis by “AI psychologists” – specialists in interpreting the otherwise inscrutable, alleged, “inner lives” of models.

Technical media of recording and playback have, since the 19th century at least, been employed in attempts to contact the spirits of the dead. In these histories of technological spiritualism, humans themselves have often played the role of “media”. In this talk, I compare the mid 20th century phenomenon of Electronic Voice Phenomena (EVP) where tape recorders allegedly picked up messages from “the other side” with so called deadbots: machine learning systems trained to simulate deceased people. In particular, the talk will note the significance of error and labor in these practices and how they distribute the effort of interpretation between user and machine.

The article argues that as of spring 2025, we should understand the developments in United States policy toward higher education as erasure as violence. This is different from traditional fears of bureaucracy. While we are used to thinking of governmental administration as burying citizens in inefficient processes and absurd procedures, the dismantling of administrative infrastructure is worse. This is the first stage of erasure as violence and concerns mainly the wrecking ball directed by DOGE and similar initiatives to tear down infrastructure upholding governance. The second stage concerns the individual level of research and teaching. Here, we have witnessed a deliberate campaign to make people in academia – students, teachers, researchers – insecure and afraid. The result is a second stage of erasure as violence in the form of self-censorship. Even as most actors in academia are not threatened directly by cuts in funding, discontinued courses, or demolished research initiatives, many feel the pressure to adjust as an act of survival. We have only begun to see the consequences of this forced behavior.

In our current generative AI paradigm, so-called hallucinations are typically seen as a kind of nuisance that will eventually be swept away as the technology improves. There are several reasons to question this assumption. One of them is that the very phenomenon is the result of deliberate business decisions by corporations invested in delivering diverse sentence structures through deep learning and generative pretrained transformers (GPTs). This article urges a fresh view on “hallucinations” by arguing that, rather than being errors in any conventional sense, “hallucinations” are evidence of a probabilistic system incapable of dealing with questions of knowledge. These systems are epistemologically indifferent. Yet, by presenting as errors to users of generative AI, “hallucinations” can function as practical reminders of and indexes to the limits of this kind of machine learning. Viewed this way, “hallucinations” remind us that every time you get something reasonable-seeming from a system such as OpenAI’s ChatGPT, you might as well have been given something quite outrageous; from the machine’s perspective it’s all the same.

Deadbots—AI systems designed to simulate the dead—clarify how generative AI reshapes temporal sense‑making. Operating in the “digital limit situation” of loss and finitude, they neither preserve memories nor store archives; they sever material traces and outsource the work of remembrance to automated interaction, ultimately fostering forgetting. The talk frames deadbots as a convergence of two traditions. From cybernetics, they inherit the “empty archive,” where feedback replaces retention and provenance is erased during model training. From technological spiritualism, they draw on practices that use technical mediation to confer authenticity, echoing nineteenth‑century séance boards and mid‑twentieth‑century Electronic Voice Phenomena. In both cases, technology gains authority through its apparent objectivity and opacity, inviting speculation about contact with the absent. Yet deadbots diverge from their spiritualist lineage by eliminating the interpretive labor once required to sustain such connections. The user’s role is reduced to passive consumption of a corporate service, while the system’s probabilistic token prediction turns remembrance into chance encounters. Consequently, deadbots function as engines of presentism—the endpoint of an “automation of memory” that dissolves the past into ever‑renewed simulations.

How were the problems and promises of technology addressed in the heightened public awareness of environmental issues during the 1970s? ARARAT provides insight here. It was an exhibition arranged at Moderna Museet in Stockholm in 1976. Its goal was to inform visitors about the interconnections between humans, society, and the environment and, on the basis of such knowledge, empower people to seek less wasteful ways of life.To this end, the exhibition invitedvisitors to become actively involved in experiments and get acquainted with everyday technologies that were less expensive to make, easier to repair, and more transparent in terms of production and life cycle. Previous research on ARARAT has focused on its relevance for current practices in art and architecture. The presentarticle, by contrast, aims to situate ARARAT in contexts that are both more general and more specific than previous work has been able to show.The study argues, first, that ARARAT can be understood as pioneering the field of practical knowledge (“praktisk kunskap”) before it was more formally established around 1980 in Sweden; second, that the ARARAT undertaking amounted to a new kind of popular education(“folkbildning”) with its combination of science, politics, and hands-on experimentation; and, third, that the ARARAT project was a demonstration of the exhibition format as an environing mediumwhereby it actively took part in changing the environment by empowering the population in an era with significant collective challenges.

With generative artificial intelligence being applied in everyday operations, e.g. as an alternative to web search, the question of models as sites of knowledge must be considered. As a source of information, an AI model is neither an ordered archive nor a database but a statistical engine. In this lecture, I discuss the relevance of error in the production of knowledge in cybernetic and AI systems and how it relates to specific uses of the past. While error-correction is crucial to the operation of language models, false outcomes e.g. hallucinations, can hardly be considered errors or mistakes in a epistemological sense because the concepts of truth and falsity are beyond the model architecture. This situation can be compared to an earlier cybernetic principle of negative feedback as a method to regulate and control a system. Such an approach has been suggested as being the opposite of a traditional archive effectively producing a very instrumental use of the past: input to steer toward a more desired outcome. Nevertheless, there have been approaches in social and human sciences which drew on cybernetic ideas for how to make use of accumulated knowledge. Comparing cybernetic principles with current AI regimes – especially their conceptions of errors – this lecture asks: what uses of history (broadly conceived) are made possible by these paradigms respectively?

For more than half a century, the use of artificial intelligence in legal domains has been a topic of interest for scholars and organizations. In this talk, I look at some major trends in these undertakings, asking: How did they conceive of the role of technology in the area of legal work? From the standpoint of how these projects sought to implement AI, how did they imagine the function of the law in society? Then, turning to our current situation and looking forward, I discuss some potential trade-offs which lie before us. If previous attempts to make legal matters computable ran up against the limits of propositional logic, the bluntness of formalization et cetera – what can we expect from deep learning? More specifically, is there a conflict between prediction as a goal in AI and explainability as a requirement in the practice of law? Or – might the efficiency of predictive statistics encourage a new self-image of the legal domain: from one concerned with precision, transparency, and accountability to one which traffics in data-driven forecasting, valorized by the old dream of prevention?

Our notion of what it means to communicate – where does it come from? This chapter visits a seminal work facing this question head on. In Speaking into the Air (1999), composed on the doorstep to the new millennium, historian and philosopher of media John Durham Peters suggests that our understanding of communication rests on an unfortunate view of mediated interpersonal exchange as something inherently broken. The presumed defective state of our interactions across distances, he argues, is based on the misguided idea that a harmonious and flawless union of souls is both possible and desired. To demonstrate his contention, Peters locates an original separation between the dialogues of Socrates – forcing participation, pushing towards agreement – and the dissemination of Jesus as told in the Gospels – spreading the seeds or words for those who have ears to hear. He then carries this ancient bifurcation on an idiosyncratic route between pillars of the Western canon of intellectual history, among them, Augustine, Aquinas, Bacon, Locke, Hegel, Marx, Kierkegaard, and Haraway. As Peters interrogates their positions on angels, money, love, law, ethics, labour et cetera, he takes these topics to be, ultimately, problems of communication whereby he builds on scholars like McLuhan and Kittler while also laying a foundation for continued extensions of concepts of media. Peter’s work productively shows the deep affinities between spiritual practices and technical endeavours in establishing contact with absent entities as in the cases of telegraphy and telepathy. It reminds its readers that techniques such as phonography, telephony, and photography were received with existential unease and wild speculation regarding the proper and probable locations of mind and matter. All carried fantasies of disembodied paths to the dead and the distant. Critiquing the idea of communication as an enduring shortcoming, Peters’ proposal is that we seek a less ambitious notion of what it means to connect. One that gives up on forced perfection and the desperate technological fixes employed to attain it and, instead, stands in awe of the fact that we are able to reach one another at all: humans and, perhaps, animals, computers, and aliens too. In making this case, Peters draws on the American pragmatist tradition and finds with William James and Ralph Waldo Emerson an attitude towards an exchange with others based on “making-do”; communication as a form of work that is ongoing, embodied, trusting, and always attentive to the profound otherness of fellow creatures and environments.

Artikeln argumenterar att Donald Trumps valseger 2024 utgör en strategisk triumf för Silicon Valleys “tech‑brorsor” – Elon Musk, Peter Thiel, Marc Andreessen m.fl. – vars långsiktiga mål är att ersätta politik med teknik. Författaren väver ihop observationer från Berkeley, historien om Ted Kaczynski och den moderna AI‑ekonomins extrema energihunger för att visa hur ett teknikcentrerat tänkesätt allt mer dominerar USA:s maktstruktur. Han beskriver hur riskkapitalet backar kandidater som J.D. Vance, hur AI‑jättar planerar egna kärnkraftverk (t.o.m. återöppningen av Three Mile Island) och hur Andreessens “tekno‑optimistiska manifest” speglar Kaczynskis idé om en livsavgörande fiende – nu riktad mot institutioner, reglering och demokrati. 2024 framställs som året då Silicon Valley “vann valet”, med löftet att teknik ensam ska lösa samhällsproblemen – men först efter att politiken avskaffats.