Lev Manovich is one of the outstanding scholars in the field of digital culture and media. Born in 1960, he became best known as a pioneer of the theory of new media. It is said that he wrote one of the most comprehensive and influential books ever done on the subject to date, “The Language of New Media” published as far back as 2001. He made a huge input into how digital technologies change visual culture and communication.
This is post will comment the chapter 5 of “Artificial Aesthetics” by Lev Manovich and Emanuele Arielli, which is about the intimate and most probably paradoxical relationship between artificial intelligence and media production. It specifically addresses the historical discourses on the evolution of generative media, current AI tool practices, many clarifying definitions of concepts related to the topic, and a discussion about the cultural perspectives on AI.
The Evolution of AI in Media
In an historical perspective, Manovich attributes development of generative media from an early AI proposal to the latest such tools as that of DALL-E and Midjourney. He points out throughout that these are not great leaps but rather incremental developments over decades of research and innovation. The historical perspective indicates a depth and complexity that may not be obvious when considering sudden technological changes.
Defining Terms and Concepts
First and foremost, Manovich clears up the important terminological questions, that is, “generative media”, “AI media”, “synthetic media”, and how interchangeable these notions are. Besides, he clearly classifies neural networks with other types and purposes, which may give the readers a good background about the technical side of generative media. In such a way, the technology is less opaque and more accessible to a broader audience.
Human Perspective on AI
It is particularly interesting when the discussion concerns how AI appears from a cultural perspective. The author draws attention to the fact that all technologies, which are called “AI” in one period, do stop falling into this category when they become widespread. Such an “AI effect” reflects the dynamics of the cultural artificial intelligence recognition process as a long wave of assimilation of the newest technology into everyday life.
AI and Modernism
Generative AI works inherently similar to the features of the modernist movement, according to Manovich. While modernism was a struggle to cut the umbilical cord to the past, it was also in debt to all that was history. A similar approach is taken with generative AI, as it works with a massive dataset of already existing media to produce a new output. This distinction may effectively bridge the gap between historical art movements and contemporary technological practices.
Art and Media Translation Database
The concept of database art and media translation is explicated through examples of artists working with AI to generate new works from particular sets of data: Manovich in his shining light on how AI can cause the bursting of the limits of old media to something new. To him, examples are the works of Refik Anadol, “Unsupervised” and Lev Pereulkov, “Artificial Experiments” showing how AI can reimagine and blow up cultural artifacts.
From Representation to Prediction
According to Manovich, AI is a tool of prediction and not one of the more classical techniques of representation. Going from simulation to prediction is a real change in the paradigm of how media is produced. He draws a line of development that reached this far and demonstrated that every stage built on the other. This pass really shows evolution to generative media done with the help of AI.
Limitations and Challenges
Although AI can do so much is this the best it can offer. According to Manovich, It has shortfalls. He says that AI media either would be more stereotypical than the norm or less than ideal. The creativity level with which humans are at a distant from AI makes creation of human-made art unique, which the AI has not yet fully challenged. The personal experiments he conducts with AI tools enforce these shortfalls and put the current state of AI in media creation in real world context.
Conclusion The chapter, Seven Arguments about AI Images and Generative Media, is an elaborate chapter that critically delves into the nexus between AI and media. Manovich tackles its history and brings through the importance of the analysis that can be put in place to bring out a very strong perspective on what the potential of generative media can be for the implication on the future of art and culture. His balanced approach to both the possibilities and the challenges makes this chapter a valuable contribution to the discussion on AI and creativity.