Researching & developing
generative content in a post-truth era.

A paradigm shift in information commons.

The creative industry has often walked a thin line between its commitment to cause emotion, spread fervor and the often equivocal effects its works have in the general sensibility. Currently, the rampant expansion of machine learning technologies and the spreading of deep neural network technologies allowing for an unprecedented degree of realism posit new challenges to an increasingly interconnected public sphere.

Accessible to everyone, these technologies weaponize user creativity by secularizing knowledges and skills that till then could only be afforded by the entertainment industry. As recent cases of deepfakes and other manipulated videos have shown, these technologies risk undermining the trust on the information commons. How can we ensure a critical distance that highlights the novelty and empowerment of these tools but prevents the elevation of these experimentation fields to the status of unquestioned authority?

Encourage a critical distance by highlighting the empowerment of generative media.

As Ilan Manouach’s previous work and research has shown, comics are particularly amenable to programmatic processes. From his early comic book appropriations to the latest book based on the orchestrated work of hundreds of comics artists, each project can be easily described as a set of instructions, in a programmatic fashion that highly resembles the bottom-up algorithmic processes of deep neural nets in machine learning.

Within the computational creativity literature, different algorithms and model architectures have proposed effective ways of exploring the creative potential of a machine. Deep neural networks have recently played a transformative role in advancing artificial intelligence across various application domains. In Applied Memetic we are particularly interested in Generative Adversarial Networks (GAN) and their ability to generate novel images by emulating the probability distribution of given training datasets.

We lay the groundwork for technical expertise in machine learning for a broad range of applications involving graphic narratives.


Our primary motivation is to apply a GAN-derived model, to the generation of sequential comics art. Generative Adversarial Networks are one of the most successful image synthesis programming architectures in the past few years. This model pits two or more neural networks against each other in adversarial training to produce generative models. The first network, called the generator (G), generates samples that are intended to come from the same probability distribution as the training data. The other network, denoted as the discriminator (D), examines the samples to determine whether they are coming from the dataset (real) or not (fake). The two networks compete in what is known in AI as unsupervised learning, unfolding through a zero-sum game, until the generated samples are indistinguishable from those that are in the dataset.

Graphic narratives are not only important in comics or in general domains of artistic expression. They are tools whose multimodal expressive communication has become our primary modalitiy in sharing and shaping representation of our worlds. From data infographics and communication strategies to community building and graphic journalism there is a story to be told. Our skills and acquired sets of knowledges can find multiple applications in a graphic narrative-rich Internet environment.


Applied Memetic: Production

Produce the first printed and largely distributed serialized graphic novel generated with deep neural networks.

What are the technological forms of disruptive innovation that have shaped the arts industry?

“The most common response to what is called the digital revolution, might be the impulse to not change, no matter how ‘different’ the world out there seems to be”, writes Marjorie Perloff in her study of modern, avant-garde poetry through the lenses and challenges of pop culture and advertisement. Away from the deeply technophobe celebration of the advent of formal medium possibilities we document how ‘new communications technologies increasingly require subjectivities that are rich in knowledge’ (Lazzarato, 1996) and how the arts reflect the massive shifts that occur in the reconfiguration of labor for a globally, interconnected precariat of artists.

Our paper explores the creative use of operations that don’t conventionally account for the production of comic books. It acknowledges the matter-of-factness of the available technological tools as an acceleration of the dissolution of industry’s entrenched roles and their old-fashioned values of artistic integrity. The paper will reflect on the conceptual ramifications of automation in the comics industry and examine how technology both increasingly allows for more sophisticated uses of machinic production and pushes the boundaries of the medium.

Applied Memetic: Research

Substantiate our research with a paper reflecting on the production process.

We highlight the urgency for a media-savvy, internet-literate citizenship.

We will explore the manifold ways artists can leverage the increasingly reduced costs of media manipulation tools. During the workshops we will trace a historical overview of media manipulation techniques and how they have been deployed from artists to influence public opinion. We examine a variety of cases where art has battled for the attention economy: from fabricated sponsored content and astroturfing to the outright deception of shallow fakes and sophisticated generative processes that reshape the threats of reputation damage and FUD (fear, uncertainty and doubt).

We assess through a case-by-case study, how anonymous, decontextualized citizen generated content can or cannot act as a popular ultimate ‘proof’. Help develop basic forensic skills and fact-checking and determine provenance and authentication of content and most importantly: help regain trust in the information commons and vouch for an ethical creative use of generative media.

Applied Memetic: Education

We provide specially designed workshops for raising awareness on generative media.