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Deceitful Media
Deceitful Media
Artificial intelligence (AI) is often discussed as something extraordinary, a dream—or a nightmare—that awakens metaphysical questions on human life. Yet far from a distant technology of the future, the true power of AI lies in its subtle revolution of ordinary life.
Deceitful Media
Are Other AIs Possible?
Are Other AIs Possible?
We are seeing a generation of tools built without critically rethinking the purposes they are meant to serve or their role in the broader world. Could we do it differently?
Are Other AIs Possible?
What are good AI Films in 2025?
What are good AI Films in 2025?
From disrupting Hollywood to contextualizing media.
Write5Get unlimited access to the best of Medium for less than $1/week.Become a memberBecome a memberWhat are good AI Films in 2025?From disrupting Hollywood to contextualizing media.Fabian Mosele·Follow9 min read·18 hours agoListenShareMore2024 was the year of generative videos. From the announcement of Sora in February, to the first hands-on competitors like Luma’s Dream Machine, KLING and Gen-3 Alpha, to a second wave during the fall with Hailuo and Pika 2.0, plus the advent of open source models like Mochi, CogVideoX and Hunyuan.Big stars like Kanye, Snoop Dogg, Guns’N’Roses and Pink Floyd riding the wave with generated music videos, to advertisements like Fiverr, eToro, TED and even the Minions and the Olympics.A year of generative videos until Sora, the most anticipated model was released in December with underwhelming responses. During its unreleased 11 months, it hyper-curated whatever creatives could share, advertising it deceptively and culminating with some artist who momentarily leaked Sora on Huggingface by calling themselves OpenAI’s PR Puppets.Today we can generate videos of highly realistic scenes, something unbelievable in 2023. But Sora’s announcement, and what has come since, has been creating a false narrative that these tools couldn’t live up to: disrupting Hollywood. Many big names, naively or not, have been taking massive decision based on that. James Cameron becoming part of the board of Stable Diffusion, Tyler Perry putting his studio expansion funds on hold for fear of AI, companies firing staff believing they can replace them with AI… Meanwhile in reality big names like Toys’R’us and Coca Cola made shallow advertisements that no one liked. The web is flooded by a ridiculous amount of tutorials and spec ads on how to make films with AI instead of actually creating films with it. A media landscape stained by AI slop.So what’s in store for AI films in 2025? In this essay I will go through the biggest illusions the AI bubble has over video generative models, from its capabilities and creative empowerment, to the acknowledgment of limitations that we will not escape through “better models”. The thing is, it’s not “not there yet”. Stop using yet as if a magic spell will give these models some kind of super power. We have stunningly powerful models NOW with underlying flaws that we will always have. But this isn’t all about dropping shade on the field.On the contrary, having some well needed criticism enables us to see the beauty of generative media, the real ways in which it is changing our creative workflows and allowing us to make media that was unthinkable a few years ago.Illusion of the industry“People outside of AI just think that we click one button and the film is made. We actually generate lots and lots of videos before we curate what to put in our film…”That’s the common narrative from normie “AI Filmmakers” when accused of being uncreative. What they miss is that clicking one button, or clicking a few more buttons is pretty much the same thing. AI slop and shallow slideshows have been at the forefront of generative videos. Sorry but concatenating a bunch of generative videos don’t make you a filmmaker. Sigh.What we miss from big companies and AI evangelists preaching the idea of AI being the future of creativity, is the lack of acknowledgement of what goes into a creative process. Yes, generative tools are changing the landscape of media production in crazy ways. But it’s not as easy as “insert AI company slogan here”. Generative media models require a different approach to media production. It’s less like a camera, where you point and shoot, but more like a slot machine, where you try over and over until the machine produces something you like. Let’s focus on that a bit more, since it seems that nobody acknowledges this pretty important shift.Let’s say I want to make an ad for Coca Cola. For live-action I would need to hire many people, from camera to light and actors. Each shot needs to be planned thoroughly as it is going to be costly to re-shoot anything. If we make a 3D animated ad, as Coca Cola pioneered in the 90’s, we have more control over the set as it is all in our computers. It required some clever storytelling back than to make a good looking and heart warming story with the limited technology they had. But they managed to do so because they were good storytellers.Now if we use generative media, we also need to play with the limitations this tech has. How do we tell heartwarming stories if humans look uncanny? How do we plan to make it feel it’s all happening in one place if each clip is so different? Even if you are able to find clever solutions to these problems (something Wild Card, Secret Level and Silverside AI clearly didn’t), you are still bound to a slot machine.No matter what tool you use, it’s always about generating, seeing what comes out and trying it again until the model spits out something we can work with. Generating again and again, generating aga
What are good AI Films in 2025?
Model Plurality
Model Plurality
Current research in “plural alignment” concentrates on making AI models amenable to diverse human values. But plurality is not simply a safeguard against bias or an engine of efficiency: it’s a key ingredient for intelligence itself.
Model Plurality
AI Generated Business: The Rise of AGI and the Rush to Find a Working Revenue Model
AI Generated Business: The Rise of AGI and the Rush to Find a Working Revenue Model
By Brian Merchant In This Article Introduction OpenAI and the Generative AI Boom Silicon Valley Mythology, Distilled and Accelerated From “Safe AI” to AGI — and the Hype-Led Business Model Genesis Marketing AGI, Shipping Commercial AI The Dream of AGI and the Fully Automated Organization Acknowledgments Download the full report here INTRODUCTION In the spring of […]
AI Generated Business: The Rise of AGI and the Rush to Find a Working Revenue Model
The abject weirdness of AI ads | TechCrunch
The abject weirdness of AI ads | TechCrunch
"I'm trying to find holiday gifts for my sisters. I open a bunch of tabs, I want my wife's advice." That's Browser Company CEO, Josh Miller, in his
The abject weirdness of AI ads | TechCrunch
THE AI CON
THE AI CON
How to Fight Big Tech's Hype and Create the Future We Want
THE AI CON
Can Artificial Intelligence be biased? On the critique of AI's 'algorithmic bias' in the arts
Can Artificial Intelligence be biased? On the critique of AI's 'algorithmic bias' in the arts
This working paper is dedicated to artistic positions that critically deal with ‘artificial intelli- gence’ and automated pattern recognition through algorithms. Using a series of examples, it shows the social struggles that results from the distortions of bias and how artists react to it. Building on analyses by Harun Farocki and Hito Steyerl, projects by Adam Harvey and Jules LaPlace, Zach Blas and Jemima Wyman, Elisa Giardina Papa, Francis Hunger and Flupke, Erika Scourti, Mimi Onuoha, Nora Al-Badri, and Jan Nikolai Nelles are presented.
Can Artificial Intelligence be biased? On the critique of AI's 'algorithmic bias' in the arts
Liberation Stories | The New Press
Liberation Stories | The New Press
Over the past twenty years, social movements from DREAMers and the Movement for Black Lives, to queer and trans resistance, and domestic worker organizing, have helped tell a new story of America—an inclusive vision of our society that has galvanized a new and newly empowered generation. This achievement was no accident: movement leaders have honed communications techniques, political messages, and storytelling strategies in a new struggle for narrative power. Until now, these efforts have largely been piecemeal and disconnected from one another.
Liberation Stories | The New Press
(un)real data ☁️ – (🧊)real effects - Aksioma
(un)real data ☁️ – (🧊)real effects - Aksioma
Price: 20€Language: English (un)real data ☁️ – (🧊)real effects explores the inherent ambiguity of data as an opportunity to not only describe the world but strategically intervene in it. Is it possible to create specific real-world outcomes by modifying our data streams? Can we intentionally produce data to interact with an algorithmic environment that is […]
(un)real data ☁️ – (🧊)real effects - Aksioma
Notes from the Algorithmic Sublime
Notes from the Algorithmic Sublime
Remarks delivered in response to a question from students in Frank Shephard’s Algorithmic Sublime class at the New School for Social Research, New York City, on November 8, 2024.
Notes from the Algorithmic Sublime
Rethink design: A vocabulary for designing with AI | TU Delft OPEN Books
Rethink design: A vocabulary for designing with AI | TU Delft OPEN Books
Rethink Design – A vocabulary for designing with AI addresses the question of how designers can engage with AI. The book presents 17 terms that were developed through inquiries into, and explorations of, designing and living with massively interconnected, potentially autonomous, and seemingly intelligent technologies. Unlike older technologies, these do not wait for human action but engage the world proactively, making decisions, communicating, and sharing data at speeds and scales that challenge comprehension. As such they destabilise and undermine boundaries, often with disregard to moral imperatives, and reconfigure not only the material world but also our relationships with it, with each other, and with ourselves. The terms are organised into 5 sections, each oriented by a key question: How will we craft inclusive human-algorithm relations?, How will we design AI systems that benefit people and the planet?, How will we create equitable socio-economic models in the digital society?, How will we enable public deliberation on data and algorithms?, and How will we prototype responsible data-driven design practices? Taken together the terms provide a sense-making instrument, a map for navigating flexibly a complex, emergent terrain. Reflecting and responding to the dynamism of the field, the book aims to be agile and accessible, offering not the final word but a brief, critical and creative introduction – a set of complementary vistas, entry points, and insights.
Rethink design: A vocabulary for designing with AI | TU Delft OPEN Books
(1) Post | Feed | LinkedIn
(1) Post | Feed | LinkedIn
At Fantastic Futures I was asked for something that gave me hope about the future of generative AI and image collections. Here's what I mentioned. Spawning’s…
(1) Post | Feed | LinkedIn
(12) Post | Feed | LinkedIn
(12) Post | Feed | LinkedIn
You probably have seen many posts about ChatGPT offering search now to premium users. While it's framed as 'search,' it's an entirely different beast… | 15 comments on LinkedIn
(12) Post | Feed | LinkedIn
Intertwined Feedback Loops
Intertwined Feedback Loops
A series of intertwined feedback loops that unfold from several interrelated briefs, which are designed to facilitate intellectual and practical exploration.
Intertwined Feedback Loops
Synthetic Media Media
Synthetic Media Media
This project traces how media systems are using, interpreting, and anticipating Generative AI to create public life. We’re studying how the news industry frames Generative AI, when and why journalists are using it in their work, which policies and guidelines organizations are creating to regulate its use, and how people and infrastructures have the power to make Generative AI a public problem.
Synthetic Media Media
Understanding the Work of Dataset Creators
Understanding the Work of Dataset Creators
The work of the people who make datasets is crucial. They build the architectures of ground truth that shape AI systems. Yet there has been very little research that has focused on dataset creators or listened to what they have to say. In this project, we speak with 18 different dataset creators in a series of interviews that reveal the messy and contingent realities of dataset preparation. We hear about their practices and the shared challenges they face. We offer a set of actionable recommendations that would improve the practice of dataset creation while also building a more responsible AI ecosystem.
Understanding the Work of Dataset Creators
Bird in hand
Bird in hand
What can birding teach us about machine learning? And how is AI shaping how we interact with nature? Projects at the intersection of nature observation, citizen science, and machine learning offer useful case studies for examining systems of dataset production, model training and human feedback. They also present an alternative model to the extractive and exploitative “Big Data” approach to training machine learning algorithms, offering many possibilities as well as unique challenges for thinking through how we relate to AI systems.
Bird in hand