Category: Tech

• Cinema Disrupted

  October 13, 2025

  

  Notes from a GenAI Filmmaking Sprint

  AI video swarms the internet. It’s been around for nearly as long as AI-generated images, however its recent leaps and bounds in terms of realism, efficiency, and continuity have made it a desirable medium for content farmers, slop-slingers, and experimentalists. That said, there are those who are deploying the newer tools to hint at new forms of media, narrative, and experience.

  I was recently approached by the Disrupt AI Film Festival, which will run in Melbourne in November. As well as micro and short works (up to 3 mins and 3-15 mins respectively), they also have a student category in need of submissions. So over the last few weeks I organised a GenAI filmmaking Sprint at RMIT University last Friday. Leonardo.Ai was generous enough to donate a bunch of credits for us to play with, and also beamed in to give us a masterclass in how to prompt to generate AI video for storytelling — rather than just social media slurry.

  

  I also shared some thoughts from my research in terms of what kinds of stories or experiences work well for AI video, and also some practical insights on how to develop and ‘write’ AI films. The core of the workshop as a whole was to propose a structured approach: move from story ideas/fragments to logline, then to beat sheet, then shot list. The shot list, then, can be adapted slightly into the parlance of whatever tool you’re using to generate your images — you then end up with start frames for the AI video generator to use.

  This structure from traditional filmmaking functions as a constraint. But with tools that can, in theory, make anything, constraints are needed more than ever. The results were glimpses of shots that embraced both the impossible, fantastical nature of AI video, while anchoring it with characters, direction, or a particular aesthetic.

  In the workshop, I remembered moments in my studio Augmenting Creativity where students were tasked with using AI tools: particularly in the silences. Working with AI — even when it is dynamic, interesting, generative, fruitful, fun — is a solitary endeavour. AI filmmaking, too, in a sense, is a stark contrast to the hectic, chaotic, challenging, but highly dynamic and collaborative nature of real-life production. This was a reminder, and a timely one, that in teaching AI (as with any technology or tool), we must remember three turns that students must make: turn to the tool, turn to each other, turn to the class. These turns — and the attendant reflection, synthesis, and translation required with each — is where the learning and the magic happens.

  This structured approach helpfully supported and reiterated some of my thoughts on the nature of AI collaboration itself. I’ve suggested previously that collaborating with AI means embracing various dynamics — agency, hallucination, recursion, fracture, ambience. This workshop moved away — notably, for me and my predilections — from glitch, from fracture or breakage and recursion. Instead, the workflow suggested a more stable, more structured, more intentional approach, with much more agency on the part of the human in the process. The ambience, too, was notable, in how much time is required for the labour of both human and machine: the former in planning, prompting, managing shots and downloaded generations; the latter in processing the prompts, generating the outputs.

  What remains with me after this experience is a glimpse into creative genAI workflows that are more pragmatic, and integrated with other media and processes. Rather than, at best, unstructured open-ended ideation or, at worst, endless streams of slop, the tools produce what we require, and we use them to that end, and nothing beyond that. This might not be the radical revelation I’d hoped for, but it’s perhaps a more honest account of where AI filmmaking currently sits — somewhere between tool and medium, between constraint and possibility.

• A Little Slop Music

  September 19, 2025

  The AI experiment that turned my ick to 11 (now you can try it too!)

  When I sit at the piano I’m struck by a simple paradox: twelve repeating keys are both trivial and limitless. The layout is simple; mastery is not. A single key sets off a chain — lever, hammer, string, soundboard. The keyboard is the interface that controls an intricate deeper mechanism.

  The computer keyboard can be just as musical. You can sequence loops, dial patches, sample and resample, fold fragments into new textures, or plug an instrument in and hear it transformed a thousand ways. It’s a different kind of craft, but it’s still craft.

  Generative AI has given me more “magic” moments than any other technology I’ve tried: times when the interface fell away and something like intelligence answered my inputs. Images, text, sounds appearing that felt oddly new: the assemblage transcending its parts. Still, my critical brain knows it’s pattern-play: signal in noise.

  AI-generated music feels different, though.

  In exploring AI, music, and ethics after the Velvet Sundown fallout, a colleague tasked students with building fictional bands: LLMs for lyrics and backstory, image and video generators for faces and promo, Suno for the music. Some students leaned into the paratexts; the musically inclined pulled stems apart and remixed them.

  Inspired, I tried it myself. And, wouldn’t you know, the experience produced a pile of Thoughts. And not insignificantly, a handful of Feelings.

  

  ---

  Ritual-Technic: Conjuring a Fictional AI Band

  1. Start with the sound

  • Start with loose stylistic prompts: “lofi synth jazz beats,” “Scandi piano trio,” “psychedelic folk with sitar and strings,” or whatever genre-haunting vibe appeals.
  • Generate dozens (or hundreds) of tracks. Don’t worry if most are duds — part of the ritual is surfing the slop.
  • Keep a small handful that spark something: a riff, a texture, an atmosphere.

  2. Conjure the band

  • Imagine who could be behind this sound. A trio? A producer? A rotating collective?
  • Name them, sketch their backstories, even generate portraits if you like.
  • The band is a mask: it makes the output feel inhabited, not just spat out by a machine.

  3. Add the frame

  • Every band needs an album, EP, or concept. Pick a title that sets the mood (Just North of Midnight, Spectral Mixtape Vol. 1, Songs for an Abandoned Mall).
  • Create minimal visuals — a cover, a logo, a fake gig poster. The paratexts do heavy lifting in conjuring coherence.

  4. Curate the release

  • From the pile of generations, select a set that holds together. Think sequencing, flow, contrasts — enough to feel like an album, not a playlist.
  • Don’t be afraid to include misfires or weird divergences if they tell part of the story.

  5. Listen differently

  • Treat the result as both artefact and experiment. Notice where it feels joyous, uncanny, or empty.
  • Ask: what is my band teaching me about AI systems, creativity, and culture?

  ---

  Like many others, I’m sure, it took me a while to really appreciate jazz. For the longest time, for an ear tuned to consistent, unchanging monorhythms, clear structures, and simple chords and melodies, it just sounded like so much noise. It wasn’t until I became a little better at piano, but really until I saw jazz played live, and started following jazz musicians, composers, and theorists online, that I became fascinated by the endless inventiveness and ingenuity of these musicians and this music.

  This exploration, rightly, soon expanded into the origins, people, stories, and cultures of this music. This is a music born of pain, trauma, struggle, injustice. It is a music whose pioneers, masters, apprentices, advocates, have been pilloried, targeted, attacked, and abused, because of who they are, and what they were trying to express. Scandinavian jazz, and European jazz in general, is its own special problematic beast. At best, it is a form of cultural appropriation, at worst, it is an offensive cultural colonialism.

  Here I was, then, conjuring music from my imaginary Scandi jazz band in Suno, in the full knowledge that even this experiment, this act of play, brushes up against both a fraught musical history, as well as ongoing debates and court cases on creativity, intellectual property, and generative systems.

  Play is how I probe the edges of these systems, how I test what they reveal about creativity, culture, and myself. But for the first time, the baseline ‘ickiness’ I feel around the ethics of AI systems became almost emotional, even physiological. I wasn’t just testing outputs, but testing myself: the churn of affect, the strangeness in my body, the sick-fascinated thrill of watching the machine spit out something that felt like an already-loaded form of music, again and again. Addictive, uncanny, grotesque.

  It’s addictive, in part, because it’s so fast. You put in a few words, generate or enter some lyrics, and within two minutes you have a functional piece of music that sounds 80 or 90% produced and ready to do whatever you want with. Each generation is wildly different if you want it to be. You might also generate a couple of tracks in a particular style, enable the cover version feature, and hear those same songs in a completely different tone, instrumentation, genre. In the midst of generating songs, it felt like I was playing or using some kind of church organ-cum-starship enterprise-cum-dream materialiser…. the true sensation of non-stop slop.

  What perhaps made it more interesting was the vague sense that I was generating something like an album, or something like a body of work within a particular genre and style. That meant that when I got a surprising result, I had to decide whether this divergence from that style was plausible for the spectral composer in my head.

  But behind this spectre-led exhilaration: the shadow of a growing unease.

  AI-generated music used to only survive half-scrutiny: fine as background noise, easy to ignore. They still can be — but with the right prompts and tweaks, the outputs are now more complex, even if not always more musical or artistic.

  If all you want is a quick MP3 for a short film or TikTok, they’re perfect. If you’re a musician pulling stems apart for remixing or glitch experiments, they’re interesting too — but the illusion falls apart when you expect clean, studio-ready stems. Instead of crisp, isolated instruments, you hear the model’s best guesses: blobs of sound approximating piano, bass, trumpet. Like overhearing a whole track, snipping out pieces that sound instrument-like, and asking someone else to reassemble them. The seams show. Sometimes the stems are tidy, but when they wobble and smear, you catch a glimpse of how the machine is stitching its music together.

  The album Just North of Midnight only exists because I decided to make something out of the bizarre and queasy experience of generating a pile of AI songs. It exists because I needed a persona — an artist, a creative driver, a visionary — to make the tension and the weirdness feel bearable or justified. The composer, the trio, the album art, the biographies: all these extra elements, whether as worldbuilding or texture, lend (and only lend) a sense of legitimacy and authenticity to what is really just an illusion of a coherent, composed artefact.

  For me, music is an encounter and an entanglement — of performer and instrument, artist and audience, instrument and space, audience and space, hard notes and soft feel. Film, by contrast (at least for me), is an assemblage — sound and vision cut and layered for an audience. AI images or LLM outputs feel assemblage-like too: data, models, prompts, outputs, contexts stitched together. AI music may be built on the same mechanics, but I experience it differently. That gap — between how it’s made and how it feels — is why AI music strikes me as strange, eerie, magical, uncanny.

  So what’s at stake here? AI music unsettled me because it plays at entanglement without ever truly achieving it. It mimics encounter while stitching together approximations. And in that gap, I — perhaps properly for the first time — glimpsed the promise and danger of all AI-generated media: a future where culture collapses into an endless assemblage of banal, plausible visuals, sounds, and words. This is a future that becomes more and more likely unless we insist on the messy, embodied entanglements that make art matter: the contexts and struggles it emerges from, the people and stories it carries, the collective acts of making and appreciating that bind histories of pain, joy, resistance, and creativity.

  ---

  Listen to the album Just North of Midnight in its complete strangeness on SoundCloud.

• From Caméra-Stylo to Prompt-Stylo

  August 13, 2025

  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.

• Spectral linguistics

  July 28, 2025

  

  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.

• Re-Wilding AI

  July 1, 2025

  Here’s a recorded version of a workshop I first delivered at the Artificial Visionaries symposium at the University of Queensland in November 2024. I’ve used chunks/versions of it since in my teaching and parts of my research and practice.