By: Ben Potter

AI, Myth and Metaphor

What’s the ‘Intelligence’ in Artificial Intelligence?

Keywords: artificial intelligence; GPT-3; myth; metaphor; communication

Introduction: Welcome to the Future

Ok, full disclosure, I didn’t write this introduction, an AI called Generative Pre-Trained Transformer-3 (GPT-3) did.^[1] GPT-3 is at the crest of a new wave of ‘AI’ research which uses deep learning and natural language processing (NLP) to manipulate data about language with the aim of automating communication. There is an astonishing amount of hype and myth surrounding these new ‘AI’ with Google researcher Blake Lemoine calling Google’s language model LaMDA ‘sentient’ and GPT-3 engineer Ilya Sutskever calling them ‘slightly conscious’.^[2] This position is reflected in popular culture with films like Ex Machina (2014) and Her (2013) perpetuating ideas of machine consciousness. But something is amiss – we often equate machine intelligence with human intelligence, even though human understanding and computational prediction are radically different ways of perceiving. Why is this so? It is because of the imprecision and myth surrounding the metaphor of ‘intelligence’ as it has been used within the field of AI. As AI researcher Erik J. Larson argues, the myth is that we are on an inevitable path towards AI superintelligence capable of reaching and then surpassing human intelligence.^[3] Corporations have exploited this intelligence conflation and transformed it into a marketing tool, and we, as consumers, have bought into the myth. Despite the fact there has been real progress in recent years with AI’s capable of outperforming humans on narrowly defined tasks, dreams of artificial general intelligence (AGI) which resembles human intelligence are sorely misplaced.

What I will attempt to do in this article is unpack the myth to show how reality differs from the hype. Firstly, taking GPT-3 as a case study, I will look at machine learning algorithms’ deficient understanding of language. AI research is a broad field and taking GPT-3 and other Large Language Models (LLMs) as my case study helps focus on a particular branch of AI research which has dealt with what has often been considered the defining trait of human intelligence: language. Secondly, I will critique the metaphor of ‘intelligence’ as it has come to be used in AI, showing how its usage contributes to the myth. Finally, I propose that, to counter this myth, we rethink the term ‘artificial intelligence’, in view of what we know about how these systems operate.^[4]

Calculating communication without understanding

Developed by the company OpenAI, GPT-3 can perform a huge variety of text-based tasks. Known as a ‘transformer’, it works by identifying patterns that appear in human-written language, using a huge training corpus of textual data, scraped from the internet (input), and turning this into reassembled text (output). Effectively it is a massively scaled-up version of the predictive text function we have on our phones. But what separates GPT-3 from other NLP systems is that after training it can execute this great variety of tasks without further fine-tuning. All that is required is a prompt to manipulate the model towards a specific task.^[5] For example, to generate my introduction, I supplied the text: “Where will Artificial Intelligence be in 10 years?”. GPT-3 can then work out the chances of one word following another by calculating its probability within this defined context. Once it has picked out these patterns, it can reconstruct them to simulate human written text related to the prompt. The reason it can do this so fluently relates to its size – GPT-3 is one of the most powerful large language models (LLMs) ever created, trained on nearly 1 trillion words and contains 175 billion parameters.^[6] 

The fluency attained by GPT-3 and similar models has convinced some within the field of AI research of a breakthrough in the search for artificial general intelligence (AGI). Indeed, OpenAI even have as their mission statement that they seek to “ensure that artificial general intelligence benefits all of humanity”.^[7] AGI is the idea that machines can exhibit generalised human cognitive abilities such as reading, writing, understanding and even sentience. Attaining it would be a key milestone towards ideas of superintelligence and AI singularity popularised by futurists like Ray Kurzweil, whereby machines eclipse humans as the most intelligent beings on the planet.^[8] This is the holy grail of the myth of AI but a deeper look at exactly how GPT-3 functions and the problems it has with basic reasoning will show how far away we are from this reality.

The single most important fact in grasping how LLMs like GPT-3 work is that, unlike humans, they have no embodied and meaningful comprehension of the world and its relation to language. GPT-3 is an ‘autoregressive’ model, which means that it predicts future values based on past ones. In other words, it uses historical data of past words to predict the likely sequence of future words. There is no doubt that this method can create realistic and original discourse that can be difficult to distinguish from human text. It is nevertheless an entirely different method of composing text to the one used by humans. This is because GPT-3 and similar models have no understanding of the words they produce, nor do they have any feeling for their meaning. As AI researchers Gary Marcus and Ernest Davis show, you cannot trust GPT-3 to answer questions about real-world situations. For example, ask it how you might get your dining room table through a door into another room and it might suggest that you “cut the door in half and remove the top half”.^[9] And it is not only problems with basic reasoning but also with racist and offensive language.^[10] This is because GPT-3 simply approximates the probability of one word following another. Its disembodied and calculative logic leaves it semantically blindfolded, unable to distinguish the logical from the absurd. It has no consciousness, no ethics, no morals, and no understanding of normativity. In short, these AI are nowhere near ‘human-level’ intelligence, despite what those who perpetuate the myth might say. 

In highlighting the myth and its detachment from reality, I am not suggesting that GPT-3 and similar models are not valuable and impressive technologies but rather that these machines simulate human abilities without the understanding, empathy, and intelligence of humans. They are the property of the biggest corporations in the world who have a vested interest in making these machines profitable and may do so without thinking through the dangers to society. This is the key point to keep in mind when we think about how these technologies might be used as they break out into the mainstream. 

So, what could this technology be used for? As we have seen, GPT-3 can be used to create text. This means that it can be used to make weird surrealist fiction, or perhaps usher in the age of robo-journalism. It can be used to create computer code, to power social media bots, and to carry out automated conversational tasks in commercial settings. It could even be used to power conversational interfaces in the style of Siri or Alexa, thus redefining how we retrieve information on the internet. More recently, OpenAI released a 12 billion parameter version of GPT-3 called DALL-E, which has been trained on pictures, and which can generate hilarious original material (Figure 2).  However, the dark side of such technology would be its use in the creation of realistic deepfakes, hence why OpenAI currently blurs human face generations. What is certain is that the economic potential of GPT-3, and NLP AI more broadly, is massive, due to these systems’ versatility and their apparent ability to communicate with us. They nevertheless do so without the fundamental quality of human intelligence, despite what their advocates might suggest. 

Putting the ‘Intelligence’ in AI: the origins of the myth

The term ‘Artificial Intelligence’ was coined in 1955 by John McCarthy and consolidated a year later at the 1956 Dartmouth Conference which signposted the official founding of the field. ‘AI’ as it is colloquially known, is now an umbrella term for a diverse set of technologies, the meaning of which is imprecise. Indeed, I would argue that applying the term ‘intelligence’ to models such as GPT-3 or LaMDA is ideological and obfuscating – it contributes to the myth as a poorly thought through metaphor conflating human intelligence with the statistical reasoning displayed by machines. To explain how we got here, we need to look more closely at the concept of ‘intelligence’ and exactly what it came to represent for the early field of AI.

In 1950, before the nascent field of AI had coalesced, Alan Turing proposed the ‘Imitation Game’. More commonly known today as the Turing test, the game seeks to assess machine intelligence and involves an anonymous text conversation between a human interrogator and two interlocutors: one human and one machine. The task of the interrogator is to determine which interlocutor is the machine.^[11] Turing initially poses the question “Can machines think?” but sidesteps it because of difficulty in defining the terms ‘machine’ and ‘think’.^[12] Instead, he focused on one specific aspect of human intelligence: communication. In doing so, Turing turned a question about whether machines possess the broad and situational understanding that defines human thinking into a task where machines and their programmers are concerned solely with using a set of calculated decisions to simulate linguistic textual communication. In this move, he placed human perception of machines at the centre of AI research. What mattered was not whether machines thought like humans but rather how convincing the machines were at appearing human. Thus, Turing radically narrowed the meaning of ‘intelligence’ within the embryonic field of AI, to focus on simulating communication.^[13] 

It was 14 years before a machine capable of even playing Turing’s hypothetical game was invented, when Joseph Weizenbaum, an MIT computer scientist, created the first chatbot, ELIZA, in 1964. The program itself was relatively simple and worked through scripts. Each script, or program, corresponded to a human role. For example, the most famous ELIZA script, Doctor, simulated a therapist.^[14] It worked by breaking down text, input by a human interlocutor, into its data structure, and searching for patterns within it. If a keyword was spotted, the text was reassembled as a response to the interlocutor; if no keyword was spotted, a generic response was sent.^[15] 

What was important for Weizenbaum was not that his machine be intelligent but that it appear intelligent. He was not seeking true human-like intelligence; neither was he attempting to build a machine capable of understanding language. What he focused on instead was how humans interpreted the machine’s generated output, combining abstract mathematical reasoning with psychological deception.^[16] This is why scripts such as ‘doctor’ are important. They frame, and to some degree control, humans’ interpretation of computer-generated conversation. Weizenbaum’s insight has had far-reaching consequences in the field of AI. Indeed, the tendency of humans to anthropomorphise machines is known today as the ‘ELIZA effect’. Blake Lemoine’s claims of language model sentience with his assertion that “I know a person when I talk to it” is a classic example of this.^[17] Weizenbaum was wary of such effects and warned against exploiting them; however, his early and prescient concerns went unheeded. The mere appearance of intelligence was consolidated as a vital tool in the development of AI and the myth of computers with human-like intelligence took hold.

The control behind communication

Computer programmers researching AI since Weizenbaum have deployed a range of techniques to manipulate human interaction with machines in ways that make us more inclined to view these autonomous machines as possessing human-like traits. These range from writing idiosyncratic behaviours into code to creating personalities for ‘virtual assistants’ like Siri and Alexa. So, if these machines are not intelligent and are rather engaging in a kind of simulated communication, the question we should be asking is: what power and control lies behind the myth of apparent intelligence? 

Weizenbaum used the analogy of how some people believe what a fortune teller has to say about their future to describe how some people read more insight and understanding into his ELIZA program than is there. When thinking about future uses of today’s LLMs, it is not a huge stretch to extend that analogy. Imagine a Siri or Alexa-type assistant powered by GPT-3. They would be like a fortune teller who has scores of data about the person having their fortune told – so much data that they can predict the types of things that you might search for. You could easily start to think that this assistant knows you better than yourself. Moreover, the assistant might present information from the internet to you in idiosyncratic ways, simulating quirky traits which give it a personality. You might start to trust it, form a friendship with it, fall in love with it. All the while, the more you have been communicating with the machine, the more it has been learning about you. It has been drawing on encoded ideas from human psychology to maintain an illusion of spontaneity and randomness, while also consolidating control within your interactions.^[18] 

What is happening with the deployment of communicative AI is that the complex systems which administer and shape ever more of our lives are being placed behind another layer of ideological chicanery. We need to ask if we trust the likes of Google, Amazon, Apple, Meta and Microsoft to influence our lives in increasingly intimate ways with systems which they describe as ‘intelligent’ and which in reality are anything but. We need to ask why they want machines to appear humane, spontaneous and creative all whilst being strictly controlled.  As Erik J. Larson suggests, AI produced by Big Tech will inevitably follow the logic of profit and scalability and forsake potentially fruitful avenues of future research, which could go some way toward creating a machine with the capacity to understand language as humans do.^[19] Moreover, the race towards profitability – evidenced by OpenAI’s change from ‘not for profit’ to ‘capped profit’^[20] – will mean one thing: these systems will be rushed into public use before we have a chance to fully comprehend the effect they will have on our societies. These new forms of interaction will be no different underneath the hood. But they will feel friendlier and more trustworthy, and that is something we should be wary of. 

Beyond the myth: reimagining the ‘Intelligence’ metaphor

As recently noted by the European Parliamentary Research Service, “the term ‘AI’ relies upon a metaphor for the human quality of intelligence”.^[21] What I have tried to do in this article is show how the intelligence metaphor is inaccurate and thus contributes to the myth of AI. The metaphors that we use to describe things shape how we perceive and think about concepts and objects in the world.^[22] This is why reconsidering the metaphor ‘Artificial Intelligence’ is so important. The view of these systems as ‘intelligent’, as I have shown, dates back over 70 years. In this time, the idea of machine intelligence has captured public imagination and laid the ideological groundwork for the widespread and unthinking reception of various technologies which pose as intelligent. The technologies that fall under the AI umbrella are diverse. They carry out a huge array of tasks, many of which are essential to society’s function. Moreover, they often offer insights that cannot be achieved by humans alone. Therefore, the narrow and controlled application of machine learning algorithms to problem-solving scenarios is something to be admired and pursued. However, as I have highlighted, the economic potential of systems such as GPT-3 means that they will inevitably be rolled out across multiple sectors, coming to control and influence more aspects of our lives. One way of remaining alert to these developments is to reimagine the metaphors we use to describe the technologies which power them.

But what would be a more suitable names for these systems? Two descriptions that more accurately represent what LLMs do are ‘human task simulation’ and ‘artificial communication’.^[23] These terms reflect the fact that programmers behind LLMs have consciously or unconsciously abandoned the search for human-like ‘intelligence’ in favour of systems which can successfully simulate human communication and other tasks.^[24] These metaphors help us understand, and think critically about, the workings of the systems that they describe. For example, the word ‘simulated’ is associated not only with computing and imitations but also with deception. ‘Communication’ points to the specific aspect of human intelligence which is attempting to be simulated. It is a narrow and precise term, unlike the vaguer ‘intelligence’. One of the core features of language is that it helps us orientate ourselves in the world, come to mutual understandings with others, and create shared focal points for the meaningful issues within society.^[25]  If we are to retain perspective on these systems and avoid getting sucked into the AI myth, then avoiding the ‘intelligence’ metaphor and the appropriation of human cognitive abilities that this entails is not a bad place to start.

[end]

BIO

Ben Potter is a PhD researcher at the University of Sussex researching the social and political effects of artificial intelligence. Specifically, his interest is in how communicative AI technologies such as Siri, Alexa or GPT-3 are changing the structural mediations within what has been termed the ‘public sphere’, including how we communicate on and retrieve information form the internet. He is interested in the policy implications and ethical regulation of AI and writes on the philosophical and sociological effects of technology more broadly.

REFERENCES

^[1] To generate this introduction, I provided the GPT-3 powered interface ‘philosopherAI’ with the prompt: “where will Artificial Intelligence be in 10 years?”. For clarification, the GPT-3 generated text starts with “we will have much more data” and ends with “and it can magnify anything we do with it – good or bad”. I removed two paragraphs for concision but left the rest of the text unaltered. You can try out your own queries at https://www.philosopherai.com. 

^[2] Blake Lemoine, ‘Is LaMDA Sentient? – An Interview’ accessed on 18^th June 2022 at: https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917; And Ilya Sutskver’s twitter comments at: https://twitter.com/ilyasut/status/1491554478243258368?s=21&t=noC6T4yt85xNtfVYN8DsmQ.

^[3] Erik J. Larson, The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do (Cambridge: The Belknap Press, 2021), 1.

^[4] This article is drawing on research from my wider PhD project which is enquiring into the way artificial intelligence creates discourse out of data. I aim to publish a full-length research article on natural language processing artificial intelligence in 2023 which will include preliminary work conducted here on GPT-3.

^[5] Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al., ‘Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing’, p. 3. Visited 17^th June 2022, https://arxiv.org/pdf/2107.13586.pdf.

^[6] Parameters are the adjustable ‘weights’ which inform the value of a specific input into the end result. For the technical paper from OpenAI showing how GPT-3 was trained, see T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal et al., ‘Language models are few-shot learners’, OpenAI, p. 8. Visited 10^th June 2022, https://arxiv.org/pdf/2005.14165.pdf.

^[7] https://openai.com/about/.

^[8] Ray Kurzweil, The Singularity is Near: When Humans Transcend Biology (New York: Penguin, 2005).

^[9] Gary Marcus and Ernest Davis, ‘GPT-3, Bloviator: OpenAI’s language generator has no idea what it’s talking about’, MIT Technology Review, August 2020. Visited 17^th June 2022, https://www.technologyreview.com/2020/08/22/1007539/gpt3-openai-language-generator-artificial-intelligence-ai-opinion/.

^[10] Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell et al., ‘On the Dangers of Stochastic Parrots: Can Language Models be too Big?’ FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021. At https://dl.acm.org/doi/pdf/10.1145/3442188.3445922. For an example of a GPT-3 powered interface creating racist text see: https://twitter.com/abebab/status/1309137018404958215?lang=en.  This something that OpenAI are aware of, restricting the generation of certain content and adding a layer of human fine tuning to prevent GPT-3 creating racist discourse. To see how they achieve this, see Long Ouyang, Jeff Wu, Xu Jiang et al., ‘Training Language Models to Follow Instructions with Human Feedback’, OpenAI, 2022: https://arxiv.org/pdf/2203.02155.pdf. The process of adding a human layer of supervision within these systems is called ‘reinforcement learning from human feedback’ (RLHF) and makes GPT-3 somewhat safer and commercially viable but less autonomous.

^[11] Alan Turing, “Computing Machinery and Intelligence”. Mind, 59 (236), 1950: 443.

^[12] Turing, ‘Computing Machinery and Intelligence’, 433. To date, no AI has ever passed the Turing test. Those that have come closest have used deception to trick the human interlocutors.

^[13] Larson, The Myth of Artificial Intelligence, 10.

^[14] If you want to have a play at interacting with an ELIZA replica you can do so at: http://psych.fullerton.edu/mbirnbaum/psych101/eliza.htm.

^[15] Joseph Weizenbaum, “Contextual Understanding by Computers”, Communications of the ACM (Volume: 10 Number: 8, August 1967), 475.

^[16] Simone Natale, Deceitful Media: Artificial Intelligence and Social Life after the Turing Test (Oxford: Oxford University Press, 2021), 53.

^[17] Blake Lemoine, ‘Is LaMDA Sentient? – An Interview’.

^[18] Natale, 120; and Esposito, 10.

^[19] Erik J. Larson, The Myth of Artificial Intelligence, 272.

^[20] Alberto Romero, ‘How OpenAI Sold its Soul for $1 Billion’, accessed on 24^th Aug 2022 at https://onezero.medium.com/openai-sold-its-soul-for-1-billion-cf35ff9e8cd4.

^[21] European Parliamentary Research Service, ‘What if we chose new metaphors for artificial intelligence?’ accessed on 24 Aug 2022 at  https://www.europarl.europa.eu/RegData/etudes/ATAG/2021/690024/EPRS_ATA(2021)690024_EN.pdf.

^[22] George Lakoff and Mark Johnson, The Metaphors We Live By (Chicago: University of Chicago Press, 2003).

^[23] Elena Esposito, Artificial Communication: How Algorithms Produce Social Intelligence (Cambridge: The MIT Press, 2022), 5.

^[24] Ibid.

^[25] Morten H. Christiansen and Nick Charter, The Language Game: How Improvisation Created Language and Changed the World (London: Bantam Press, 2022), 15-23.

By: Sandy Di Yu

Eternal Recall

Keywords: digital immortality; discrete units, archives, subjective duration, eternity

Eternal Recall/You only live on 

A wise man once wrote, “one lives but once in the world”^[1]. Centuries later, a decade or so ago from today, a wiser man popularised the related aphorism “you only live once” by igniting it with a catchy anagram and letting it spread like wildfire on a maturing Internet 2.0.^[2]

I remain cynical of aphorisms both old and less old. How do they know that one lives only once? What makes them so sure that existence doesn’t simply go on and on and on and on and on? It’s not like they died and came back to let us know, like a cyclical continuation of life after death, like wilted flowers renewing their blooms in the spring.

I suppose that’s the whole point. We don’t know, just as surely as they don’t. The impossibility of knowing instils in us the fear of an unremarkable life lived mutely and without purpose. And so we say, “YOLO”, and in a stirring display of presentism, follow through with actions that claw at the possibility of ecstatic escape.

This same unknowing compounds with the unassailable knowledge of life’s ending. We only live once (probably), and it’s not even for that long. This is the condition that makes our limited time precious, and why finitude is a categorical component of Dasein. As the late philosopher and celebrated bank robber Bernard Stiegler once wrote, “Human beings exist only under the condition of the anticipation of death, which is a protention they hold in common, but is also their impossible protention.”^[3]

Our shared impossible protention is the commonality of death, the knowledge of the unknowable. It’s what Emmanuel Levinas calls there is, an alterity that might parallel other minds: “the other that is announced does not possess this existing as the subject possesses it; its hold over my existing is mysterious. It is not unknown but unknowable, refractory to all light. But this precisely indicates that the other is in no way another myself, participating with me in a common existence.”^[4] Boris Groys says something of similar nature, but in relation to the flow of time and the implications of museum objects: “…in analysing my own thinking process, I can never find any evidence of its finitude. To discover the limitations of my existence in space and time, I need the gaze of the Other. I read my death in the eyes of others.”^[5]

If the alterity of death and the alterity of the Other are analogous, then the death of the subject might be the gentle marriage of individual minds into an ocean of collective unconscious. It would make true Hito Steyerl’s proclamation about how the internet, the swathe of networked activity often characterised as a collective mind, approximates death by being undead.^[6] If death is a return to the great collective, then immortality is the contrived individuation of the self, continuing on without anticipation. If not death, then there is no destination to anticipate.

But what if the alterity of death is not to be anticipated? What if immortality, in all its grotesque implications, was within our reach? Imagine that you only live once, but you live forever. Without finitude, what would be of being?

What do you think of immortality? Most people I ask seem to shudder at the thought. A lifetime of this is more than enough, they say. But to have it go on forever? One might crumple under the mere thought of that immeasurable exhaustion. Then there are the outliers, those who revel in the idea of experiencing what’s to come with the next thousand or more rotations around the sun. It’s all harmless speculation. No one I know has taken up an offer of immortality and lived to tell the tale.

Not yet, anyway. 

With the acceleration of technological innovations, and with the shared commonality of death that extends throughout human history, we might just be on the cusp of some sort of life-prolonging breakthrough at any moment.

In the bid for an indefinite postponement of biological death is gerontologist Aubrey de Grey. In his interview with Douglas Lain, he claims that people dismiss his project because they don’t want to get their hopes up^[7] rather than there being issues that are overlooked in his proposals.

De Grey will have to forgive me if I don’t quite buy his whole “misunderstood genius” schtick. As the self-appointed spokesperson of everyday people, I’d like to clarify that the root issue with such programmes aiming at indefinite life extension is the replication and perpetuation of the systemic inequalities that would be exacerbated. Who do you think would have access to life-prolonging medical procedures? Surely not the struggling worker who can’t afford private dental, or the time-poor caretaker who must choose between heating and food. Who needs longer-living oligarchs and tech billionaires? They can all die mad, thanks.

Postponement of biological death aside, in the digital milieu, there are other ways to think about immortality. That’s not to say that loading one’s mind up to the cloud would produce any more of an egalitarian society, but conversations about systemic issues can be carried out along with speculative modes of reinvention. Digitality is a relatively nascent field still formalising its structure. The possibility of redirecting its evolution away from the reproduction of preexisting hierarchies emerges with its advent. 

Perhaps such tech optimism feels familiar, and caution to keep this in check may be warranted. Media theorist Wendy Hui Kyong Chun warns that the internet in its earlier stages was never truly the utopia purported by 90s technologists with a hard-on for William Gibson, but rather “the Wild West meets speed meets Yellow Peril meets capitalism on steroids.”^[8] In the years since, with the monopolisation of the internet tempered by the phenomenon of platforming, the situation has only grown direr. But if by some miraculous feat we’re able to redress such systemic plights in the digital, might the physical follow suit?

Suppose that digital networks can be built up without the issues latent in their physical counterpart. Can the digital then become a vessel for eternal life? Could a former military-funded project for allowing executable code to survive past Cold War-induced catastrophes let us exceed the deterioration of physical bodies, of earthly death?

Metaphysically, the issue becomes multi-pronged. Digital networks are temporal in their architecture. They necessitate change, an elemental aspect of time. Yet immortality assumes a certain unchangeability. This is exemplified by the preservation of artefacts in museums, as explained by Boris Groys. Taken out of the flow of time, such objects enjoy the status of eternal commemoration. They become immortal, as far as culture will allow, but they are dead, no longer a part of the present milieu. Immortality, then, becomes timelessness, without change and out of time.^[9]

This is further complicated by the genesis of data analytics, a core component of the current tides of the web, and its connection to eugenics. “Both big data and eugenics seek to tie the past to the future–correlation to prediction–through supposedly eternal, unchanging biological attributes.”^[10] With eugenics, phenomenological time is folded in on itself, as the temporality of the object is stretched into eternity. Such is also the basis of biological immortality, of “good” genes that thwart the decay of telomeres. It might not be such a coincidence that the logic of eugenics rears its troubling head in the digital milieu. 

Another issue arises if we accept that immortality is indeed the extended and contrived individuation of the self. Would this be at odds with the digital? Does the digital presuppose a lack of individuation? 

Yes and no. Digitality as a pure concept implies differentiation, requiring discrete units in its very function.^[11] Such units might be interpreted as unmitigated individuation. However, digitality in practice, as the internet functions, is not so straightforward. The transfer and use of data are inherently leaky, spilling into one another as it makes borders obsolete.^[12] There’s little to say where one object stops and the other begin. 

The immortal subject as a digital entity thus produces contradictions. To reconcile this, it must be remembered that subjectivity is fundamentally temporal. Dasein is nothing if not a historical being, bound up in time. Therefore, time in the historical sense must be injected into the immortal digital subject in order to reclaim its untainted existence.

To do this, the historicity of the digital subjectivity may be captured in the digital archive. It might also provide a way to rethink the individuation of extended life, or what it means to be an individual subject confronted with eternity. Counter to the hierarchical systems of contemporary societal structuring, we might consider the archive as the commons, after author Ariella Aisha Azoulay.^[13] We heed the cautions from sceptics telling us that a true digital commons is a pipedream, impossible to substantiate in this reality, but we shimmy forward towards a digital archive that might activate a site, rich in historical nuance, that offers respite from the lonely inidividuation of the immortal subject.

We’re still building our archives, architecture and contents and all. As they continue to be engineered, it is still unclear what it would mean to be a pure subject living on as a digital being. But the historicity sculpted into the framework of the potential digital archive may be key and crucial to the possibility of eternity. Its digital beams and columns reverberate in the realm of not-yet-but-soon, echoing the refrain, “You only live once, but you’ll have always lived.”

[end]

BIO

Sandy Di Yu is a Canadian writer, researcher and artist currently based in the UK. She primarily works with painting, text and digital media, having obtained an MA in Contemporary Art Theory from Goldsmiths, the University of London in 2018 following her BFA in visual arts and philosophy at York University, Toronto. Sandy has taken part in several group exhibitions, and she has written extensively on visual culture, working with several arts organisations, independent zines and publications from the UK and beyond. Her current research focuses on the dissolution of time that coincides with the advent of digital networks. She is currently pursuing a PhD in Digital Media at the University of Sussex.

REFERENCES

^[1] Goethe, Johann Wolfgang von. Clavigo. 1774.

^[2] Drake ft. Lil Wayne, Tyga. The Motto. 2012.

^[3] Hui, Yuk, and Bernard Stiegler. On the Existence of Digital Objects. Minneapolis: University of Minnesota Press, 2016. 

^[4] Lévinas, Emmanuel. Time and the other. Translated by Richard A. Cohen. Pittsburgh, PA: Duquesne University Press, 1987, 77.

^[5] Groys, Boris. In the Flow. London: Verso, 2016, 27.

^[6] Steyerl, Hito. “Too Much World: Is the Internet Dead?” E-flux, no. 49 (November 2013). https://www.e-flux.com/journal/49/60004/too-much-world-is-the-internet-dead/.

^[7] Lain, Douglas, and De Grey, Aubrey D. N. J. Advancing Conversations: Aubrey De Grey. Zero Books, 2016. 

^[8] Chun, Wendy Hui Kyong. UPDATING TO REMAIN THE SAME: habitual new media. Cambridge, MA: MIT PRESS, 2017, 8.

^[9] Groys, Boris. In the Flow. London: Verso, 2016.

^[10] Kyong, Chun Wendy Hui, and Alex Barnett. Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition. Cambridge, MA: The MIT Press, 2021. 

^[11] Galloway, Alexander R. Uncomputable: Play and Politics in the Long Digital Age. New York: Verso, 2021. 

^[12] Chun, Wendy Hui Kyong. UPDATING TO REMAIN THE SAME: habitual new media. Cambridge, MA: MIT PRESS, 2017.

^[13] Azoulay, Ariella. Potential History: Unlearning Imperialism. London: Verso, 2019.

By: Karl Logge

Towards a fourfold digital weaver theory: notes on a past-future praxis

Keywords: Weird design, Weird digital, Weaver Theory, Speculative Fourfolding

Cyborg informatics, recrafting bodies / Artificial intelligence, C31 operations coding
Multiple databases, umbilical network / Breath engine, indestructible heart

…We are the first to program your future / We are the first of cyber- evolution
We are the first, we are the last… 

— ‘The 1st’, X-Dream, 2004

What we’re gonna do right here is go back, way back, back into time.
When the only people that existed were troglodytes…cave men…
cave women…Neanderthal…troglodytes. Let’s take the average
cave man at home, listening to his stereo.

— ‘Troglodyte (Cave Man)’, The Jimmy Castor Bunch, 1972

via GIPHY

open_access.direct: digital-fourfold
^Initiate: Opening up the digital weavescape^

On and Off.
Input / Output.
One, Zero.
Compute…

When did the digital begin? Does it begin with device or apparatus, the design of circuits or tapping at computer terminals? Is it numbers, the invention of zero, or the counting that counts? Is it the language of programming or the alphabetic grammata of writing^[1]? Is it computation, signals sent and received or the pulse of electricity that gives the digital its presence? Is the digital media or medium, the message or the manage? Could it be that the digital is simply another way of naming a technological Rubicon, where we suddenly tipped into a so-called modern way of life?

To add to this, what does it mean to be digital? Is it calculating, coding, gaming, snapping selfies or sunsets, swiping left and right, checking out or checking in? Is it the act of negotiating a life amongst technical objects, swimming within data flows, charging, uploading, clicking? Writing up, clocking on or switching through nodal points? Is it getting the invite to virtual block-parties with our virtual, blocky meta-selves? Is the digital act separate from the digital process and the things we use to enact these processes? Or is there something more fundamental to the idea of the digital? What if we go back, way back… back into time and ask ourselves if perhaps we have been digital beings for longer than we care to remember? Perhaps, somewhere along the line, we merely got lost, disoriented, having dropped the thread that could lead us out of the labyrinth?

Over and Under.
One Naked, One Dressed.
Weave…

Once naked, so the story goes, those avatars of the first human souls were free and easy in the garden where everyone knew everything on a first-name basis. Then it all went pear-shaped and the first binary entered the equation: good…evil. For better or worse, forbidden fruit leaves quite the aftertaste, a bitter mix of exile and shame. And so, as the story continues, it’s been fig leaves over naughty bits ever since. 

Still, you might say the tree of knowing stuff has its uses. At some point fig leaves swapped out for pelts, or deer gut, or twine or hemp or wool, silk, cotton and jute — each materialises another binary. Warp and weft begets cloth and dress, toga, tunic and trousers, and before you know it, the sails on ships open up new worlds taking us into this uncertain future.

Let’s say then, that for almost as long as we have been dressed (and probably well before) that we have long been working on a certain digital equation. This is based on passing lines, between other tightly stretched  — over and under, under and over, over and over again. Let’s call this DIGITAL-0.

Access_portal.unfold: To continue reading this essay you can navigate the digital fourfold by clicking through each quadrant below.

BIO

An installation and live-art artist, redirective designer, undisciplined academic and irresponsible researcher-writer, and student of the Master Weaver Chira Vigo, Karl Logge holds a PhD in Design from Charles Sturt University and creates projects that focus on teaching the ancient art of weaving to children and young people. This includes works presented together with Marta Romani as part of the New European Bauhaus Festival in Brussels, the Nature, Art and Habitat Residency in Val Taleggio, the Earth Rising Festival at IMMA and BOOM! Festival in Portugal.

REFERENCES

^[1] This is activated by Bernard Steigler’s work on digital ontologies and technologies of attention where he states: “Alphabetical vocalic writing, which appeared between the 8th and 7th Century B.C., allowed the constitution of a singular attentional process … ceaselessly reformed, deformed and transformed as the process of psychic and collective individuation ….If we want to analyse and understand the stakes of this transformation (insofar as this is possible), we must analyse what, as process of ‘grammatisation’, leads us from the appearance of the writing of grammata up to the digital apparatuses and the new attentional forms that they constitute. For these inaugurate a new process of psychic and collective individuation that emerges at the heart of what must be understood as a network society of planetary proportions.” Stiegler, Bernard. 2012. “Relational ecology and the digital pharmakon” in: Culture Machine Vol 13, 4-5.

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