Art, AI and apocalypse: Michael Takeo Magruder addresses our fears and questions

The digital artist talks about the possibilities and challenges of artificial intelligence.

Jonathan Evens

Jonathan is Team Rector for Wickford and Runwell. He is co-author of The Secret Chord, and writes on the arts.

A darkened art gallery displays images and screens on three walls.

Takeo.org.

In the current fractured debate about the future development of Artificial Intelligence (AI) systems, artists are among those informing our understanding of the issues through their creative use of technologies. British-American visual artist Michael Takeo Magruder is one such, with his current exhibition Un/familiar Terrain{s} infusing leading-edge AI systems with traditional artistic practices to reimagine the world anew. In so doing, this exhibition pushes visitors to question the organic nature of their own memories and the unsettling notions of automatic processing, misattribution, and reconstruction.

The exhibition uses personal footage of specific places of renowned natural beauty that has been captured on first generation AI-enabled smartphones. Every single frame of the source material has then been revised, reworked, and rebuilt into digital prints and algorithmic videos which recast these captured moments as uncanny encounters. In this exhibition at Washington DC’s Henry Luce III Center for the Arts & Religion, the invisible work of the AI allows people to experience more than there ever was, expanding both time and space.

Magruder has been using Information Age technologies and systems to examine our networked, media-rich world for over 25 years. A residency in the Department of Theology and Religious Studies at King’s College London resulted in De/coding the Apocalypse, an exhibition exploring contemporary creative visions inspired by and based on the Book of Revelation. Imaginary Cities explored the British Library’s digital collection of historic urban maps to create provocative fictional cityscapes for the Information Age.

A digital painting of a green glade of woodlands

Reconstructed Landscape{s} – Hidcote Bartrim : multi-channel 4K video with stereo soundscape : 2023.

A digital painting of a turbulent stream.

Reconstructed Landscape{s} – Great Falls [I0-1B] : 2024. Digital monoprint on archive fine art paper.

JE: You are a visual artist who works with emerging media including real-time data, digital archives, VR environments, mobile devices, and AI processes. What is it about the possibilities and challenges of emerging media that captures your artistic imagination?

MTM: As a first-generation digital native, computer technologies – and the evolving range of potentials they offer – have deeply informed my life and art. Computational media not only opens different avenues for artistic expression but provides a novel means to recontextualise traditional artforms and histories of practice; its ephemeral nature is a particular draw. However, this also creates new challenges, especially in areas concerning preservation and access. I sometimes wonder if my art will still exist for future generations to experience in full, or if it will simply fade alongside the technologies that I’ve used in its production.

JE: To what extent does Un/familiar Terrain{s} build on past exhibitions like Imaginary Landscapes and Imaginary Cities, and to what extent does it break new ground for you?

MTM: Un/familiar Terrain{s} certainly arises from and expands on the artistic concepts of those past projects. The main difference is that each artwork in Un/familiar Terrain{s} is generated from a small sample of personal data (a scenic moment that I’ve captured intentionally), not digital materials gleaned from large public archives and online collections.

JE: Do you find that working with images of the natural world (as is the case with this exhibition) as opposed to images of human-made environments (as you did with 'Imaginary Cities') leads to different approaches or inspiration on your part?

MTM: My projects that explore constructed environments often reference principles of Modernist architecture and design whereas my pieces in Un/familiar Terrain{s} explicitly seek to dialogue with the long history of Western landscape art. The AI systems that I have used in their creation are leading edge but conversely, their conceptual references extend back to long before the onset of what we consider ‘modern’ art.

JE: I've heard many artists criticise digital art in terms of degrading the principal tools and techniques of artists throughout history and those arguments would be made even more vigorously in relation to AI. In this exhibition you're enabling a conversation about the painterly effects you can create as a digital artist and those that can be achieved through AI, yet without leading us to one side or other of that argument. Is your vision essentially one of wanting to see the possibilities in whatever tools, techniques or technologies we have to hand?

MTM: Absolutely. For me that’s one of the fundamental purposes of art. AI is unquestionably the most disruptive (and potentially problematic) technology affecting creative communities at present, but it’s just the most recent historical example. I imagine similar criticisms arose during the proliferation of devices like the printing press and the first photographic cameras. Such inventions clearly did not ‘degrade’ art, but they indisputably shifted its trajectory.

JE: While your work is not expressly religious, you have engaged with theological themes and institutions as with Un/familiar Terrain{s}, which is on show at Wesley Theological Seminary in Washington DC. What do you think it is about your work and the ways you use and explore emerging media that enables such a dialogue to take place?

MTM: I feel that many of the social and ethical questions raised by the emergence of transformative digital technologies are quite similar (and sometimes identical) to ones that have been traditionally posed by theologians. With that in mind, although the fields are quite different in many ways, at present there are some strange and compelling intersections.

JE: From your experience, what can theological or religious institutions learn from a more engaged involvement with emerging media, particularly AI?

MTM: Like artists, perhaps theologians can use emerging (and disruptive) media to not only expand possibilities for their work, but more importantly, to refocus their efforts towards areas that these technologies cannot presently (and will likely never) address.

JE: Apocalyptic scenarios are often invoked in response to developments such as AI, the refugee crisis, populist political movements or the climate emergency. In De/coding the Apocalypse, you worked with emerging media to explore contemporary creative visions inspired by and based on the Book of Revelation. From that experience, what advice would you give to emerging artists wanting to engage with or invoke apocalyptic imagery? How might emerging artists live in the shadow of apocalypse or what have you noticed about our contemporary fear of modern apocalypses?

MTM: Throughout history, visions of apocalypse have been consistently rooted in humanity’s prevailing fears. In the Digital Age these sit alongside our growing concerns about technologies that afford increasingly greater potential to create or destroy. Of course, artists should continue to reveal the deeply problematic (and potentially apocalyptic) aspects of new technologies, but they should also highlight their positive aspects to encourage the creation of “a new heaven and a new earth” that can be a better place for all.

Un/familiar Terrain{s}, 30 May – 18 September 2024, The Dadian Gallery, Henry Luce III Center for the Arts & Religion.

Visit the Un/familiar Terrain{s} digital gallery

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21 June 2024

4 min read

It's our mistakes that make us human

What we learn distinguishes us from tech.

Silvianne Aspray

Silvianne Aspray is a theologian and postdoctoral fellow at the University of Cambridge.

A man staring at a laptop grimmaces and holds his hands to his head.

Francisco De Legarreta C. on Unsplash.

The distinction between technology and human beings has become blurry: AI seems to be able to listen, answer our questions, even respond to our feelings. It becomes increasingly easy to confuse machines with humans. In this situation, it is increasingly important to ask: What makes us human, in distinction from machines? There are many answers to this question, but for now I would like to focus on just one aspect of what I think is distinctively human: As human beings, we live and learn in time.

To be human means to be intrinsically temporal. We live in time and are oriented towards a future good. We are learning animals, and our learning is bound up with the taking of time. When we learn to know or to do something, we necessarily make mistakes, and we take practice. But keeping in view something we desire – a future good – we keep going.

Let’s take the example of language. We acquire language in community over time. Toddlers make all sorts of hilarious mistakes when they first try to talk, and it takes them a long time even to get single words right, let alone to try and form sentences. But they keep trying, and they eventually learn. The same goes with love: Knowing how to love our family or our neighbours near and far is not something we are good at instantly. It is not the sort of learning where you absorb a piece of information and then you ‘get’ it. No, we learn it over time, we imitate others, we practice and even when we have learned, in the abstract, what it is to be loving, we keep getting it wrong.

This, too, is part of what it means to be human: to make mistakes. Not the sort of mistakes machines make, when they classify some information wrongly, for instance, but the very human mistake of falling short of your own ideal. Of striving towards something you desire – happiness, in the broadest of terms – and yet falling short, in your actions, of that very goal. But there’s another very human thing right here: Human beings can also change. They – we – can have a change of heart, be transformed, and at some point in time, actually start to do the right thing – even against all the odds. Statistics of past behaviours, do not always correctly predict future outcomes. Part of being human means that we can be transformed.

Transformation sometimes comes suddenly, when an overwhelming, awe-inspiring experience changes somebody’s life as by a bolt of lightning. Much more commonly, though, such transformation takes time. Through taking up small practices, we can form new habits, gradually acquire virtue, and do the right thing more often than not. This is so human: We are anything but perfect. As Christians would say: We have a tendency to entangle ourselves in the mess of sin and guilt. But we also bear the image of the Holy One who made us, and by the grace and favour of that One, we are not forever stuck in the mess. We are redeemed: are given the strength to keep trying, despite the mistakes we make, and given the grace to acquire virtue and become better people over time. All of this to say that being human means to live in time, and to learn in time.

So, this is a real difference between human beings and machines: Human beings can, and do strive toward a future good.

Now compare this to the most complex of machines. We say that AI is able to “learn”. But what does it mean to learn, for AI? Machine learning is usually categorized into supervised learning, unsupervised and self-supervised learning. Supervised learning means that a model is trained for a specific task based on correctly labelled data. For instance, if a model is to predict whether a mammogram image contains a cancerous tumour, it is given many example images which are correctly classed as ‘contains cancer’ or ‘does not contain cancer’. That way, it is “taught” to recognise cancer in unlabelled mammograms. Unsupervised learning is different. Here, the system looks for patterns in the dataset it is given. It clusters and groups data without relying on predefined labels. Self-supervised learning uses both methods: Here, the system uses parts of the data itself as a kind of label – such as, for instance, predicting the upper half of an image from its lower half, or the next word in a given text. This is the predominant paradigm for how contemporary large-scale AI models “learn”.

In each case, AI’s learning is necessarily based on data sets. Learning happens with reference to pre-given data, and in that sense with reference to the past. It may look like such models can consider the future, and have future goals, but only insofar as they have picked up patterns in past data, which they use to predict future patterns – as if the future was nothing but a repetition of the past.

So this is a real difference between human beings and machines: Human beings can, and do strive toward a future good. Machines, by contrast, are always oriented towards the past of the data that was fed to them. Human beings are intrinsically temporal beings, whereas machines are defined by temporality only in a very limited sense: it takes time to upload data, and for the data to be processed, for instance. Time, for machines, is nothing but an extension of the past, whereas for human beings, it is an invitation to and the possibility for being transformed for the sake of a future good. We, human beings, are intrinsically temporal, living in time towards a future good – which machines do not.

In the face of new technologies we need a sharpened sense for the strange and awe-inspiring species that is the human race, and cultivate a new sense of wonder about humanity itself.