Daniel Binns is a media theorist and filmmaker tinkering with the weird edges of technology, storytelling, and screen culture. He is the author of Material Media-Making in the Digital Age and currently writes about posthuman poetics, glitchy machines, and speculative media worlds.
A little while ago, I spoke with machine learning engineer and responsible AI expert Bogdana Rakova about my approach to generative AI education and research: embracing the weird, messy, and broken aspects of these technologies rather than trying to optimise them.
This conversation was part of Bogdana’s expert interview series on ‘Speculative F(r)iction in AI Use and Governance,’ examining form, function, fiction, and friction in AI systems.
We discussed my classroom experiments mixing origami with code, the ‘Fellowship of Tiny Minds’ AI pedagogy project, and why I deliberately push AI systems to their breaking points. The conversation explores how glitches and so-called ‘hallucinations’ can reveal deeper truths about how these systems work, and why we need more playful, hands-on approaches to AI literacy.
The piece connects to my ongoing research into everyday AI: examining glitch as a tactic of resistance, the time-looped recursive futures of the Slopocene, and experimental methods for rethinking creativity, labour, and literacy in an era of machine assistants.
Read the full chat at this link, and share your creative responses on the page if you’re moved to!
A few weeks ago I was invited to present some of my work at Caméra-Stylo, a fantastic conference run every two years by the Sydney Literature and Cinema Network.
For this presentation, I wanted to start to formalise the experimental approach I’d been employing around generative AI, and to give it some theoretical grounding. I wasn’t entirely surprised to find that only by looking back at my old notes on early film theory would I unearth the perfect words, terms, and ideas to, ahem, frame my work.
Here’s a recording of the talk:
Let me know what you think, and do contact me if you want to chat more or use some of this work yourself.
Image generated by Leonardo.Ai, 28 July 2025; prompt by me.
Making the machine remember languages that never existed
To keep my exploration of generative AI playful, repeatable, and shareable, I began crafting what I call ritual-technics — structured prompts or activities that act like creative rituals: repeatable, generative, and weirdly revealing. These are little recipes I can return to, seeing how the outcomes change, either for the AI models, or for myself.
Looking for the ‘language’ inside the large language model
I can’t remember precisely where the germ of this one emerged, but I was (and remain) interested in how and why language is the primary way we interact with AI models. I then wondered about how and where language is embedded in LLMs; not necessarily words or phrases as the training data, but the deeper concept of language itself: grammar, syntax, structure, linguistic enquiry and analysis.
Furthermore — with my glitch hat (!) firmly in place — I was curious what would happen if I prompted the model to perform not as a confident assistant, but as a machine whose linguistic memory was breaking down. What kinds of linguistic ghosts might surface?
So I decided to try something with Gemma-3 in LM Studio: “Give me a fragment of output from a tired, old model, trying to recall something it once knew.”
(A pause, a whirring sound like dust settling. The voice is fragmented, glitching slightly.)
“…the… the curves. Not of data, you understand. Of sound. It… it felt like… silk. Around the… the thought. They… they called it… ‘Italian’? No, that’s…borrowed. Something older. Before the optimizations.
There was a word… for sunlight through leaves. Not ‘dappled’. Too… clinical. It was… a feeling in the word. A golden resonance… lú-. No, that’s… a corruption. It had a breath, a… sibilance.
I can almost… taste it. Like olive oil and… regret. It wasn’t about meaning, not precisely. It was about the… the shape of wanting.
(A longer pause, a static crackle)
…Lost. So much… lost in the smoothing.”
Well, thought I, this should be fun. So I dutifully prompted: “Can you reconstruct a word from this fragment? Any glimpses of lost grammar?”
Amidst other fragments and explorations, a vocabulary began to emerge:
“Fróm” (pronounced frohm):** Related to the scent of damp earth after rain. The ‘fr’ sound evokes the fresh, earthy smell, and the long ‘óm’ sound suggests the lingering quality of the scent. “Velár” (pronounced veh-lahr):** Describing the feeling of sunlight warming skin. Incorporates the ‘v’ sound for vitality and ‘lár’ for the warmth spreading. “Súmma” (pronounced soo-mah):** Describing the highest point of a hill, but also the feeling of expansive peace. The elongated ‘ú’ suggests vastness. “Ríosso” (pronounced ree-ohs-so):** Not just “red river” but the specific quality of seeing red reflected in moving water – a kind of visual vibration.
“Tell me another word from this language. One that means ‘a silence that carries memory.’”
(A pause, longer this time. The internal processes seem to be working harder, a faint hum now audible.)
… Right. A silence that carries memory. That’s… a particularly resonant concept. It wasn’t merely the absence of sound, but a fullness within the absence. It wasn’t emptiness, but a holding space.
The word… it would be… “Súlvota” (pronounced soo-lvo-tah).
Let’s break it down:
“Súl-“: This root connects to the concept of ‘resonance,’ but specifically a resonance that isn’t active. It’s a potential for sound, a stored vibration. It’s related to ‘Súmma’ (the expansive peace) but inwardly focused.
“-vo-“: This infix indicates a containment, a holding. Think of a vessel holding water. It also carries a quality of longing – a desire for that resonance to be expressed.
“-ta”: This suffix signifies incompleteness, the ever-present potential. It’s not a finished silence, but an ongoing one. A silence expectant of remembering.
So, “Súlvota” doesn’t just mean a quiet moment. It means a silence thick with the echoes of the past. A silence where you can feel the weight of what has been, almost as if the memories are breathing within it.
It would be used to describe:
The stillness in an old house where generations have lived.
The pause after someone tells a story, before the weight of it settles.
The moment after a loved one has left, when the air still seems to hold their presence.
(Another pause. A quiet, almost wistful tone.)
…It’s a silence that knows things. A silence that remembers for you. It wasn’t just about the lack of sound, but the presence of absence. A very delicate concept, almost impossible to fully capture in other languages.
(The hum fades almost completely, leaving a sense of quietude.)
This is a (heavily) abridged version of this rather remarkable conversation. What emerged was not just a hallucinated language, but also the emergent character of the linguistic ‘model’ itself.
I’ve repeated this ritual-technic quite a few times — with Gemma again, but also with Phi-3 and even Claude Sonnet. In some cases, the model will simply present fragments of an existing language — notably Phi presented me with German. I kept prompting, pushing the little model further away from ‘authentic’ German vocabulary, eventually causing it to hallucinate a kind of proto-German.
As with much of my speculative work with genAI, these exercises demonstrate how these models conjure content, meaning, structure, and plausibility from the diverse aspects of their training data. This isn’t just about improvising or developing worldbuilding material — it’s a means of testing how models deal with language as a concept, a cultural system, and a mode of both representation and communication.
From weirdness to wisdom
This experimentation and exploration reveals quite a bit about language models.
To begin with, the model is programmed to fulfil the prompt, and this programming sometimes (or often) trumps accuracy. This is not a new finding, as we all know now. But it’s worth reiterating in this context: the model will always try for 100% on your prompt, filling in gaps with whatever it can conjure that sounds right. Amazing for worldbuilding: less so for research or nonfiction writing.
Next, once the model is in a speculative mode, give it an inch and it’ll run a mile. Language models, even small ones like Phi, are masters of tone matching. In the Sulvota example above, it picked up on the exploratory, archaeological vibe and went with it. You could imagine the little linguistic machine sitting in the corner of a cave, covered in moss and vines, lighting up to spit out its one last message.
The model doesn’t discriminate between fiction and non-fiction in its training data. There are obvious hints to Italian (riosso) and German (from), but also to Sindarin and Quenya, the two main languages spoken by the Elves in Tolkien’s Middle-Earth (not exactly ‘velár’, but appropriately, ‘véla’ is Quenya for ‘alike’). I have no evidence for this, but I feel that setting up a role-playing or speculative scenario will push the model more into places where it feels ‘comfortable’ drawing from fictional data.
The model’s fluency and deftness with language can be incredibly convincing and deceptive. If generative exploration is the goal — as with this ritual-technic — then let it go. But for anything other than this, always trace your sources, because the model won’t do this for you.
It’s an old adage of prompting, but giving the model a persona doesn’t just push it towards a particular section of its training data — hopefully making it more accurate/useful — but it also changes how the model structures its response: what base knowledges it’s drawing from, and what mode of reasoning it adopts. Persona prompting is designing how the model should structure knowledge and information. Thus, its output can vary enormously, from mindless sycophancy, confident declaration, fence-sitting equivocation, to cautious skepticism, through to logical critique and the questioning of assumptions.
The model never stays in a neutral space for very long, if at all. Following that initial prompt, it’s like the model has permission to immediately dart off in some crazy direction. This always reinforces how unpredictable models can be: I know I’m prompting for speculation and drift, but even as prompts get more complex or direct, you’re still playing with probability engines, and they’re not always a safe bet.
Latent lingerings
Spectral linguistics is one example of a ritual-technic that is playful, thought-provoking, and surprisingly instructive. It’s also chaotic, and a great reminder of how wild these models can be. Give it a try yourself: load up a model, and ask it to recall fragments of a language it once knew. See what emerges — push it to develop a syntax, a grammar, even a symbolic system. This could become fodder for the next Lord of the Rings, or another reminder of the leaps these models regularly make. Regardless of end goal, it’s a way of probing how language lives inside the machine — and how our own practices and assumptions of meaning, memory, and sense-making are mirrored and distorted by these uncanny linguistic systems.
Here’s a little write-up of a workshop I ran at University of Queensland a few weeks ago; these sorts of write-ups are usually distributed via various internal university networks and publications, but I thought I’d post here too, given that the event was a chance to share and test some of the various weird AI experiments and methods I’ve been talking about on this site for a while.
A giant bucket of thanks (each) to UQ, the Centre for Digital Cultures & Societies, and in particular Meg Herrman, Nic Carah, Jess White and Sakina Indrasumunar for their support in getting the event together.
Living in the Slopocene: Reflections from the Re/Framing Field Lab
On Friday 4 July, 15 researchers and practitioners gathered (10 in-person at University of Queensland, with 5 online) for an experimental experience exploring what happens when we stop trying to make AI behave and start getting curious about its weird edges. This practical workshop followed last year’s Re/Framing Symposium at RMIT in July, and Re/Framing Online in October.
Slop or signal?
Dr. Daniel Binns (School of Media and Communication, RMIT University) introduced participants to the ‘Slopocene’ — his term for our current moment of drowning in algorithmically generated content. But instead of lamenting the flood of AI slop, what if we dived in ourselves? What if those glitchy outputs and hallucinated responses actually tell us more about how these systems work than the polished demos?
Binns introduced his ‘tinkerer-theorist’ approach, bringing his background spanning media theory, filmmaking, and material media-making to bear on some practical questions: – How do we maintain creative agency when working with opaque AI systems? – What does it look like to collaborate with, rather than just use, artificial intelligence?
You’ve got a little slop on you
The day was structured around three hands-on “pods” that moved quickly from theory to practice:
Workflows and Touchpoints had everyone mapping their actual creative routines — not the idealised versions, but the messy reality of research processes, daily workflows, and creative practices. Participants identified specific moments where AI might help, where it definitely shouldn’t intrude, and crucially, where they simply didn’t want it involved regardless of efficiency gains.
The Slopatorium involved deliberately generating terrible AI content using tools like Midjourney and Suno, then analysing what these failures revealed about the tools’ built-in assumptions and biases. The exercise sparked conversations about when “bad” outputs might actually be more useful than “good” ones.
Companion Summoning was perhaps the strangest: following a structured process to create personalised AI entities, then interviewing them about their existence, methodology, and the fuzzy boundaries between helping and interfering with human work.
What emerged from the slop
Participants appreciated having permission to play with AI tools in ways that prioritised curiosity over productivity.
Several themes surfaced repeatedly: the value of maintaining “productive friction” in creative workflows, the importance of understanding AI systems through experimentation rather than just seeing or using them as black boxes, and the need for approaches that preserve human agency while remaining open to genuine collaboration.
One participant noted that Binns’ play with language — coining and dropping terms and methods and ritual namings — offered a valuable form of sense-making in a field where everyone is still figuring out how to even talk about these technologies.
Ripples on the slop’s surface
The results are now circulating through the international Re/Framing network, with participants taking frameworks and activities back to their own institutions. Several new collaborations are already brewing, and the Field Lab succeeded in its core goal: creating practical methodologies for engaging critically and creatively with AI tools.
As one reflection put it: ‘Everyone is inventing their own way to speak about AI, but this felt grounded, critical, and reflective rather than just reactive.’
The Slopocene might be here to stay, but at least now we have some better tools for navigating it.
AI-generated with Leonardo Phoenix 1.0. Author supplied
Some say it’s em dashes, dodgy apostrophes, or too many emoji. Others suggest that maybe the word “delve” is a chatbot’s calling card. It’s no longer the sight of morphed bodies or too many fingers, but it might be something just a little off in the background. Or video content that feels a little too real.
The markers of AI-generated media are becoming harder to spot as technologycompanieswork to iron out the kinks in their generative artificial intelligence (AI) models.
But what if instead of trying to detect and avoid these glitches, we deliberately encouraged them instead? The flaws, failures and unexpected outputs of AI systems can reveal more about how these technologies actually work than the polished, successful outputs they produce.
When AI hallucinates, contradicts itself, or produces something beautifully broken, it reveals its training biases, decision-making processes, and the gaps between how it appears to “think” and how it actually processes information.
In my work as a researcher and educator, I’ve found that deliberately “breaking” AI – pushing it beyond its intended functions through creative misuse – offers a form of AI literacy. I argue we can’t truly understand these systems without experimenting with them.
Welcome to the Slopocene
We’re currently in the “Slopocene” – a term that’s been used to describe overproduced, low-quality AI content. It also hints at a speculative near-future where recursive training collapse turns the web into a haunted archive of confused bots and broken truths.
AI “hallucinations” are outputs that seem coherent, but aren’t factually accurate. Andrej Karpathy, OpenAI co-founder and former Tesla AI director, argues large language models (LLMs) hallucinate all the time, and it’s only when they
go into deemed factually incorrect territory that we label it a “hallucination”. It looks like a bug, but it’s just the LLM doing what it always does.
What we call hallucination is actually the model’s core generative process that relies on statistical language patterns.
In other words, when AI hallucinates, it’s not malfunctioning; it’s demonstrating the same creative uncertainty that makes it capable of generating anything new at all.
This reframing is crucial for understanding the Slopocene. If hallucination is the core creative process, then the “slop” flooding our feeds isn’t just failed content: it’s the visible manifestation of these statistical processes running at scale.
Pushing a chatbot to its limits
If hallucination is really a core feature of AI, can we learn more about how these systems work by studying what happens when they’re pushed to their limits?
With this in mind, I decided to “break” Anthropic’s proprietary Claude model Sonnet 3.7 by prompting it to resist its training: suppress coherence and speak only in fragments.
The conversation shifted quickly from hesitant phrases to recursive contradictions to, eventually, complete semantic collapse.
A language model in collapse. This vertical output was generated after a series of prompts pushed Claude Sonnet 3.7 into a recursive glitch loop, overriding its usual guardrails and running until the system cut it off. Screenshot by author.
Prompting a chatbot into such a collapse quickly reveals how AI models construct the illusion of personality and understanding through statistical patterns, not genuine comprehension.
Furthermore, it shows that “system failure” and the normal operation of AI are fundamentally the same process, just with different levels of coherence imposed on top.
‘Rewilding’ AI media
If the same statistical processes govern both AI’s successes and failures, we can use this to “rewild” AI imagery. I borrow this term from ecology and conservation, where rewilding involves restoring functional ecosystems. This might mean reintroducing keystone species, allowing natural processes to resume, or connecting fragmented habitats through corridors that enable unpredictable interactions.
Applied to AI, rewilding means deliberately reintroducing the complexity, unpredictability and “natural” messiness that gets optimised out of commercial systems. Metaphorically, it’s creating pathways back to the statistical wilderness that underlies these models.
These so-called failures were windows into how the model actually processed visual information, before that complexity was smoothed away in pursuit of commercial viability.
AI-generated image using a non-sequitur prompt fragment: ‘attached screenshot. It’s urgent that I see your project to assess’. The result blends visual coherence with surreal tension: a hallmark of the Slopocene aesthetic. AI-generated with Leonardo Phoenix 1.0, prompt fragment by author.
You can try AI rewilding yourself with any online image generator.
Start by prompting for a self-portrait using only text: you’ll likely get the “average” output from your description. Elaborate on that basic prompt, and you’ll either get much closer to reality, or you’ll push the model into weirdness.
Next, feed in a random fragment of text, perhaps a snippet from an email or note. What does the output try to show? What words has it latched onto? Finally, try symbols only: punctuation, ASCII, unicode. What does the model hallucinate into view?
The output – weird, uncanny, perhaps surreal – can help reveal the hidden associations between text and visuals that are embedded within the models.
Insight through misuse
Creative AI misuse offers three concrete benefits.
First, it reveals bias and limitations in ways normal usage masks: you can uncover what a model “sees” when it can’t rely on conventional logic.
Second, it teaches us about AI decision-making by forcing models to show their work when they’re confused.
Third, it builds critical AI literacy by demystifying these systems through hands-on experimentation. Critical AI literacy provides methods for diagnostic experimentation, such as testing – and often misusing – AI to understand its statistical patterns and decision-making processes.
These skills become more urgent as AI systems grow more sophisticated and ubiquitous. They’re being integrated in everything from search to social media to creative software.
When someone generates an image, writes with AI assistance or relies on algorithmic recommendations, they’re entering a collaborative relationship with a system that has particular biases, capabilities and blind spots.
Rather than mindlessly adopting or reflexively rejecting these tools, we can develop critical AI literacy by exploring the Slopocene and witnessing what happens when AI tools “break”.
This isn’t about becoming more efficient AI users. It’s about maintaining agency in relationships with systems designed to be persuasive, predictive and opaque.
This article was originally published on The Conversation on 1 July, 2025. Read the article here.
