Insights

Scholastic logic, an ancient discipline developed by Aristotle over two millennia ago, is known for its simplistic dichotomy of true and false. In contrast, the schools of Indian philosophy offer a more nuanced approach to logical evaluation of facts. Philosophical discourse, in this context, has traditionally relied on sensory perception as evidence, as opposed to epistemology. The scholastic tradition employed intuition to determine the truth or falsity of a statement such as “every human being is an individual”. To do so, one need only ask whether there exists a human being who is not an individual (the answer is no), or whether every human being has a demonstrable individual characteristic (the answer is yes). In this way, statements could be tested for their truth content.

Different schools of epistemology explore various philosophical questions, such as the possibility of living in a simulated reality or the extent to which subjective reality is shaped by personal views, ideas, and biological determinants. These are complex and multifaceted issues, requiring careful consideration and in-depth analysis. While a brief statement cannot do justice to such questions, it is clear that if simulations of equivalent life and views, no less well-founded and even superior to human perception, penetrate our own perceptions, views, and reflections, our conventional ways of understanding and ordering the world could be shaken. This could have far-reaching consequences for the value of human life and individual existence, as well as for people’s respect for each other.

While some fear the overpowering potential of artificial intelligence, there are indications that if AI surpasses human expression, human rights might lose their position as the most essential values. This could have significant consequences for the future of society. Moreover, with the rise of artificial characters in virtual worlds, questions arise about their status and rights. As there are no specific regulations or rights for these characters, it is unclear what this means for people in the “real world” if they begin to coexist with them in the virtual world. The individual expression of AI-generated characters do not differ from real people anymore as the followimg gallery underlines.

The concept of “Orientalism” is a term coined by Edward Said to describe the way in which Western cultures have historically depicted and understood the people and cultures of the Middle East and Asia. These depictions often involve stereotypes and simplifications that reinforce Western superiority and exoticize the “Orient.”

While AI generators themselves do not hold cultural biases or preconceptions, they are only as unbiased as the data they are trained on. If the training data for an AI image generator includes primarily images of the Middle East that have been produced through an Orientalist lens, then the output of the generator may also reflect these biases.

In recent years, there have been several examples of AI image generators producing stereotypical or offensive depictions of people from the Middle East, such as automatically adding headscarves to images of women or darkening skin tones. These examples suggest that the Orientalist biases of the training data are being reflected in the output of the AI generators.

It is therefore important for the creators and users of AI image generators to be aware of the potential for bias and actively work to diversify and de-bias the training data. This includes images produced by artists and photographers from the Middle East themselves and avoiding using images that reinforce Orientalist stereotypes.

The process of bisociation through the use of an AI image generator is showcased in the transformation of a painting by Dutch artist Herman Jacob van der Voort in de Betouw (1847-1902) into an expressionistic image of the Golden Gate Bridge after 20 generations of variations.

Bisociation is a creative process where two unrelated or distant elements are combined in a way that generates a new and unexpected idea or solution. Similarly, pro-active work with AI generated images involves exploring and combining different variations of an initial image in a way that generates new and unexpected visual outcomes.

In both cases, the process of combining seemingly unrelated elements creates a new perspective or insight. The constant flow of variations in AI-generated images provides a fertile ground for the human mind to make unexpected connections and associations.

However, it’s important to note that the AI-generated images are created through mathematical algorithms and don’t have conscious intentionality or understanding of their content. The same applies for the impressive images created on the base of the initial image, van der Voort’s painting.

The role of the creator in this process is significant, as they are the ones who make choices throughout the process that shape the final outcome. The choices made by the creator in selecting which variations to keep, discard, or further modify can be seen as a parallel bisociative creation process in cooperation with the AI.

The creator’s subjective interpretation and aesthetic preferences also come into play in this process. In this way, the process of bisociation through the use of an AI image generator is not solely reliant on mathematical algorithms, but is a collaborative process between the AI, the creator, and the viewer’s subjective interpretation and attribution of meaning. The result is a new and original image that is the product of both technological innovation and human creativity.

The variations of the AI generator (DALLE E 2) are “blind” in terms of content. Their assumed meaning is purely subjective meaning-creation by the viewer.

If we keep hearing that AI cannot know more than what it has been trained to know via algorithms, then most people already know that this is not true. The almost incomprehensible number of vectors, which is approaching the trillion mark, and also the almost infinite variety of possible combinations do not correspond to the idea that the system has been given concrete learning content. And yet this cannot be doubted, at least for the image generators of the AI: fed with millions and millions of images and their labels, the generated images do not get out of the model of a complex combinatorics. However, this is so complex that one cannot grasp with human consciousness all factors which have led to the respective combination. Thinking in combinations, however, the assumption of a per se eclectic modus operandi, have a direct effect on perception. Like a counterfeit coin, we hold the offer sideways against the light, as it were, and scan the surface – only we can’t bite on it yet. The result is subject to the examining gaze – at least for the time being. Then, however, an interaction can occur between the object of investigation and the investigator: no cheap copy, no dull imitation, no rigid superimposition is revealed. What emerges instead is a free-acting play of very different materials, modestly associable with sources about whose true existence we know nothing: why does the city of Paris wrap itself in the modification sequence of an old Kodachrome photograph of anglers on the Seine like Christo’s Reichstag? Why do images emerge that resemble illustrations of “Waiting for Godot”? With the dried-up rivulet in the cartoon pastel at the end, is concern about climate change already inherent in AI? You don’t have to be an AI expert to know that all these associations are ultimately nonsense. But why does our imagination not allow to deny an imaginer the imagination? Because then it is already indicated that our imaginations and the AI have no connection, but produce this in the best case only in the cooperation.

Starting with a 1970’s tourist shot of an elderly couple having a break with their limousine in a desert-like landscape, the AI sequencing scans all the motif elements in the sequential episodes: the luxury sedan, the woman in the desert, High Noon, water towers amid deadly drought, until finally, in the emptiness of the desert, first Batman and finally Superman appear. “What was the AI thinking?” is a question that we have long known to be empty. By means of language – desert, woman, man, limousine – associations and variations appear that seem to come from the spectrum of topics, i.e. also from the linguistic: Water, danger, fiction, encounter. As light and clichéd as these “associated” elements initially appear, they continue to develop in a straightforward and at the same time multifaceted manner: vectors point the way. They are vectors of meaning that orient themselves on linguistic meaning and always enter into new constellations and create motifs; they are at the same time formal vectors that orient themselves on colors, proportions, surfaces, and much more. No one can think or know what connections they will finally make in the “deep learning” process, just as someone who gives a direction thereby does not know and cannot know what will appear in the dimension of temporality on the further way in this direction. In this respect, the semi-conscious exploratory course of the user and his careful or also spontaneous micro-decisions are directly reflected in the process of the vector constellations – but show substantially a different quality. It is, so to speak, an analogous process.

It’s fascinating how a single image can undergo so many rapid transformations, resulting in completely different versions each time. The first version with pastel-colored geometric shapes provides a clear image of the character discovering an old tin can on the Bosphorus beach, but it lacks vibrancy, making it somewhat dull. The second version, with a diffuse landscape of shards and debris, uses colors that remind one of 3-D glasses, adding vibrancy and depth to the image.

However, the third iteration takes things to a completely different level by dissolving the original motif and transforming the metal can into a giant tube in a comic style. The Bosphorus now becomes a sunny fantasy landscape, and a figure appears but ultimately fades away into the diffuse background.

With each new generation of pictorial material, the diffuseness takes refuge in surfaces, faces, colors, and diffusely structured crystallization points, eroding any impression of unambiguous meaning. This erosion of meaning is fascinating and irritating in the same time.

Perception is a term that has limitations when applied to AI functions. It refers to the processes of the human brain, whereas “deep learning” imitates neural processes but is not the same as the neural process of the human brain. When “deep learning” AI image generators present image sequences or offer variations, the images are based on algorithms and vectors predetermined by programmers. However, the results can defy their assumptions and correspond to their own machine logic. The interaction between the generated images and human perception can result in a flow that goes beyond visual correspondence and content logic. For example, tracing the modification sequences of an accident event (such as a person falling or jumping off a bridge) in unpredictable ways can reveal the quality of this machine logic as a simulation of perception.

A sequence that begins with the same or similar images as another can take a completely different direction in terms of meaning, solely due to gradually different structure. It becomes difficult to differentiate whether the decisive image contains meaning or simply shows visual patterns.