Tag: writing

• 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.

• RIP Reviewer #2: Are All Peer Reviewers Dicks Now?

  October 8, 2025

  

  Civility, care, and the ethics of critique in academia

  Here are some (lightly edited, anonymous) highlights from some recent peer review reports I received on submissions to Q1 journals.

  “a rather basic, limited and under-referenced overview”
  “I do not see how it contributes any original scholarship to the field”
  “The claim that [XYZ] is nonsense.”

  … and these weren’t even from Reviewer 2!

  Perhaps more distressingly, the following quote from an editor:

  “The paper might be interesting but is not well prepared, and not technically accurate or insightful, as revealed in biting commentary from the best of two reviews”

  The editor tries to be encouraging while also defending the same “biting commentary”:

  “Authors may take advantage of these excellent and insightful review comments, and possibly compose a new paper for a possible future submission”

  You may be thinking “Suck it up, snowflake.”

  Sorry but no.

  I’ve had harsh reviews before. I’ve written harsh reviews before. But you never call someone’s work ‘nonsense.’ You never call someone’s work ‘unoriginal’ or ‘basic’, even if you may think it. You certainly never do so without providing any suggestions as to how to redress these critiques, as these reviewers neglected to do.

  I might take about half an hour to write a blog post. Maybe up to a day or so if it’s a bit longer, needs some referencing, editing or proofing etc. I don’t really care if people don’t read or don’t like this work. It’s mainly for myself. However, the articles that these comments received took between four and twelve months to write: you expect some level of engagement and at least basic common human courtesy in how responses are framed.

  Reviewers: don’t be a dick.

  Editors: shield contributors from harsh reviews.

  Academia is intimidating and gatekept enough without this actual nonsense.

• Understanding the ‘Slopocene’: how the failures of AI can reveal its inner workings

  July 8, 2025

  

  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 technology companies work 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.

  

  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.

  Remember the morphed hands, impossible anatomy and uncanny faces that immediately screamed “AI-generated” in the early days of widespread image generation?

  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.

  

  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.

• Give me your answer, do

  February 9, 2025

  

  For better or worse, I’m getting a bit of a reputation as ‘the AI guy’ in my immediate institutional sub-area. Depending on how charitable you’re feeling, this could be seen as very generous or wildly unfounded. I am not in any way a computer scientist or expert on emergent consciousness, synthetic cognition, language models, media generators, or even prompt engineering. I remain the same old film and media teacher and researcher I’ve always been. But I have always used fairly advanced technology as part of anything creative. My earliest memories are of typing up, decorating, and printing off books or banners or posters from my Dad’s old IBM computer. From there it was using PC laptops and desktops, and programs like Publisher or WordPerfect, 3D Movie Maker and Fine Artist, and then more media-specific tools at uni, like Final Cut and Pro Tools.

  Working constantly with computers, software, and apps, automatically turns you into something of a problem-solver—the hilarious ‘joke’ of media education is that the teachers have to be only slightly quicker than their students at Googling a solution. As well as problem-solving, I am predisposed to ‘daisy-chaining’. My introduction to the term was as a means of connecting multiple devices together—on Mac systems circa 2007-2017 this was fairly standard practice thanks to the inter-connectivity of Firewire cables and ports (though I’m informed that this is still common even through USB). Reflecting back on years of software and tool usage, though, I can see how I was daisy-chaining constantly. Ripping from CD or DVD, or capturing from tape, then converting to a useable format in one program, then importing the export to another program, editing or adjusting, exporting once again, then burning or converting et cetera et cetera. Even not that long ago, there weren’t exactly ‘one-stop’ solutions to media, in the same way that you might see an app like CapCut or Instagram in that way now.

  There’s also a kind of ethos to daisy-chaining. In shifting from one app, program, platform, or system, to another, you’re learning different ways of doing things, adapting your workflows each time, even if only subtly. Each interface presents you with new or different options, so you can apply a unique combination of visual, aural, and affective layers to your work. There’s also an ethos of independence: you are not locked in to one app’s way of doing things. You are adaptable, changeable, and you cherry-pick the best of what a variety of tools has to offer in order to make your work the best it can be. This is the platform economics argument, or the political platform economics argument, or some variant on all of this. Like everyone, I’ve spent many hours whinging about the time it took to make stuff or to get stuff done, wishing there was the ‘perfect app’ that would just do it all. But over time I’ve come to love my bundle of tools—the set I download/install first whenever I get a new machine (or have to wipe an old one); my (vomits) ‘stack’.

  * * * * *

  The above philosophy is what I’ve found myself doing with AI tools. I suppose out of all of them, I use Claude the most. I’ve found it the most straightforward in terms of setting up custom workspaces (what Claude calls ‘Projects’ and what ChatGPT calls ‘Custom GPTs’), and just generally really like the character and flavour of responses I get back. I like that it’s a little wordy, a little more academic, a little more florid, because that’s how I write and speak; though I suppose the outputs are not just encoded into the model, but also a mirror of how I’ve engaged with it. Right now in Claude I have a handful of projects set up:

  • Executive Assistant: Helps me manage my time, prioritise tasks, and keep me on track with work and creative projects. I’ve given it summaries of all my projects and commitments, so it can offer informed suggestions where necessary.
  • Research Assistant: I’ve given this most of my research outputs, as well as a curated selection of research notes, ideas, reference summaries, sometimes whole source texts. This project is where I’ll brainstorm research or teaching ideas, fleshing out concepts, building courses, etc
  • Creative Partner: This remains semi-experimental, because I actually don’t find AI that useful in this particular instance. However, this project has been trained on a couple of my produced media works, as well as a handful of creative ideas. I find the responses far too long to be useful, and often very tangential to what I’m actually trying to get out of it—but this is as much a project context and prompting problem as it is anything else.
  • 2 x Course Assistants: Two projects have been trained with all the materials related to the courses I’m running in the upcoming semester. These projects are used to brainstorm course structures, lesson plans, and even lecture outlines.
  • Systems Assistant: This is a little different to the Executive/Research Assistants, in that it is specifically set up around ‘systems’, so the various tools, methods, workflows that I use for any given task. It’s also a kind of ‘life admin’ helper in the sense of managing information, documents, knowledge, and so on. Now that I think of it, ‘Daisy’ would probably be a great name for this project—but then again…

  I will often bounce ideas, prompts, notes between all of these different projects. How much this process corrupts the ‘purity’ of each individual project is not particularly clear to me, though I figure if it’s done in an individual chat instance it’s probably not that much of an issue. If I want to make something part of a given project’s ongoing working ‘knowledge’, I’ll put a summary somewhere in its context documents.

  But Claude is just one of the AI tools I use. I also have a bunch of language models on a hard drive that is always connected to my computer; I use these through the app GPT4All. These have similar functionality to Claude, ChatGPT, or any other proprietary/corporate LLM chatbot. Apart from the upper limit on their context windows, they have no usage limits; they run offline, privately, and at no cost. Their efficacy, though, is mixed. Llama and its variants are usually pretty reliable—though this is a Meta-built model, so there’s an accompanying ‘ick’ whenever I use it. Falcon, Hermes, and OpenOrca are independently developed, though these have taken quite some getting used to—I’ve also found that cloning them and training them on specific documents and with unique context prompts is the best way to use them.

  With all of these tools, I frequently jump between them, testing the same prompt across multiple models, or asking one model to generate prompts for another. This is a system of usage that may seem confusing at first glance, but is actually quite fluid. The outputs I get are interesting, diverse, and useful, rather than all being of the same ‘flavour’. Getting three different summaries of the same article, for example, lets me see what different models privilege in their ‘reading’—and then I’ll know which tool to use to target that aspect next time. Using AI in this way is still time-intensive, but I’ve found it much less laborious than repeatedly hammering at a prompt in a single tool trying to get the right thing. It’s also much more enjoyable, and feels more ‘human’, in the sense that you’re bouncing around between different helpers, all of whom have different strengths. The fail-rate is thus significantly lowered.

  Returning to ethos, using AI in this way feels more authentic. You learn more quickly how each tool functions, and what they’re best at. Jumping to different tools feels less like a context switch—as it might between software—and more like asking a different co-worker to weigh in. As someone who processes things through dialogue—be that with myself, with a journal, or with a friend or family member—this is a surprisingly natural way of working, of learning, and of creating. I may not be ‘the AI guy’ from a technical or qualifications standpoint, but I feel like I’m starting to earn the moniker at least from a practical, runs on the board perspective.

• This algorithmic moment

  February 1, 2024

  

  ---

  So much of what I’m being fed at the moment concerns the recent wave of AI. While we are seeing something of a plateauing of the hype cycle, I think (/hope), it’s still very present as an issue, a question, an opportunity, a hope, a fear, a concept. I’ll resist my usual impulse to historicise this last year or two of innovation within the contexts of AI research, which for decades was popularly mocked and institutionally underfunded; I’ll also resist the even stronger impulse to look at AI within the even broader milieu of technology, history, media, and society, which is, apparently, my actual day job.

  What I’ll do instead is drop the phrase algorithmic moment, which is what I’ve been trying to explore, define, and work through over the last 18 months. I’m heading back to work next week after an extended period of leave, so this seems as good a way of any as getting my head back into some of the research I left to one side for a while.

  The algorithmic moment is what we’re in at the moment. It’s the current AI bubble, hype cycle, growth spurt, whatever you define this wave as (some have dubbed it the AI spring or boom, to distinguish it from various AI winters over the last century¹). In trying to bracket it off with concrete times, I’ve settled more or less on the emergence of the GPT-3 Beta in 2020. Of course OpenAI and other AI innovations predated this, but it was GPT-3 and its children ChatGPT and DALL-E 2 that really propelled discussions of AI and its possibilities and challenges into the mainstream.

  This also means that much of this moment is swept up with the COVID pandemic. While online life had bled into the real world in interesting ways pre-2020, it was really that year, during urban lockdowns, family zooms, working from home, and a deeply felt global trauma, that online and off felt one and the same. AI innovators capitalised on the moment, seizing capital (financial and cultural) in order to promise a remote revolution built on AI and its now-shunned sibling in discourse, web3 and NFTs.

  How AI plugs into the web as a system is a further consideration — prior to this current boom, AI datasets in research were often closed. But OpenAI and its contemporaries used the internet itself as their dataset. All of humanity’s knowledge, writing, ideas, artistic output, fears, hopes, dreams, scraped and plugged into an algorithm, to then be analysed, searched, filtered, reworked at will by anyone.

  The downfall of FTX and the trial of Sam Bankman-Fried more or less marked the death knell of NFTs as the Next Big Thing, if not web3 as a broader notion to be deployed across open-source, federated applications. And as NFTs slowly left the tech conversation, as that hype cycle started falling, the AI boom filled the void, such that one can hardly log on to a tech news site or half of the most popular Subs-stack without seeing a diatribe or puff piece (not unlike this very blog post) about the latest development.

  ChatGPT has become a hit productivity tool, as well as a boon to students, authors, copy writers and content creators the world over. AI is a headache for many teachers and academics, many of whom fail not only to grasp its actual power and operations, but also how to usefully and constructively implement the technology in class activities and assessment. DALL-E, Midjourney and the like remain controversial phenomena in art and creative communities, where some hail them as invaluable aids, and others debate their ethics and value.

  As with all previous revolutions, the dust will settle on that of AI. The research and innovation will continue as it always has, but out of the limelight and away from the headlines. It feels currently like we cannot keep up, that it’s all happening too fast, that if only we slowed down and thought about things, we could try and understand how we’ll be impacted, how everything might change. At the risk of historicising, exactly like I said I wouldn’t, people thought the same of the printing press, the aeroplane, and the computer. In 2002, Andrew Murphie and John Potts were trying to capture the flux and flow and tension and release of culture and technology. They were grappling in particular with the widespread adoption of the internet, and how to bring that into line with other systems and theories of community and communication. Jean-Francois Lyotard had said that new communications networks functioned largely on “language games” between machines and humans. Building on this idea, Murphie and Potts suggested that the information economy “needs us to make unexpected ‘moves’ in these games or it will wind down through a kind of natural attrition. [The information economy] feeds on new patterns and in the process sets up a kind of freedom of movement within it in order to gain access to the new.”²

  The information economy has given way, now, to the platform economy. It might be easy, then, to think that the internet is dead and decaying or, at least, kind of withering or atrophying. Similarly, it can be even easier to think that in this locked-down, walled-off, platform- and app-based existence where online and offline are more or less congruent, we are without control. I’ve been dropping breadcrumbs over these last few posts as to how we might resist in some small way, if not to the detriment of the system, then at least to the benefit of our own mental states; and I hope to keep doing this in future posts (and over on Mastodon).

  For me, the above thoughts have been gestating for a long time, but they remain immature, unpolished; unfiltered which, in its own way, is a form of resistance to the popular image of the opaque black box of algorithmic systems. I am still trying to figure out what to do with them; whether to develop them further into a series of academic articles or a monograph, to just keep posting random bits and bobs here on this site, or to seed them into a creative piece, be it a film, book, or something else entirely. Maybe a little of everything, but I’m in no rush.

  As a postscript, I’m also publishing this here to resist another system, that of academic publishing, which is monolithic, glacial, frustrating, and usually hidden behind a paywall for a privileged few. Anyway, I’m not expecting anyone to read this, much less use or cite it in their work, but better it be here if someone needs it than reserved for a privileged few.

  As a bookend for the AI-generated image that opened the post, I asked Bard for “a cool sign-off for my blog posts about technology, history, and culture” and it offered the following, so here you go…

  Signing off before the robots take over. (Just kidding… maybe.)

  ---

  Notes

  1. For an excellent history of AI up to around 1990, I can’t recommend enough AI: The Tumultuous History of the Search for Artificial Intelligence by Daniel Crevier. Crevier has made the book available for download via ResearchGate. ↩︎
  2. Murphie, Andrew, and John Potts. 2003. Culture and Technology. London: Macmillan Education UK, p. 208. https://doi.org/10.1007/978-1-137-08938-0. ↩︎