wk8 11.11/11.13: Artificial Neural Networks | CNN | Style Transfer, Artificial Intelligence
Give a brief response to any of the material covered in this week's presentations
wk8 11.11/11.13: Artificial Neural Networks | CNN | Style Transfer, Artificial Intelligence
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shashank86
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Re: wk8 11.11/11.13: Artificial Neural Networks | CNN | Style Transfer, Artificial Intelligence
Calculating Empires (Ars Electronica)
Calculating Empires immediately stood out to me because it doesn’t just show AI as a tool or as a cultural trend. It explains the entire anatomy behind what we call “AI.” I really liked how the work breaks down everything that happens from the moment you type a prompt to the moment an LLM produces a response. It turns something that is usually treated as magical into something mechanical, industrial and heavily interconnected. This actually makes the term “artificial intelligence” feel inaccurate, because the work shows how these systems are neither artificial nor intelligent in the way we imagine. They are infrastructures, pipelines and layers of computation powered by real materials, energy and human labor.
The piece also helped me understand why today’s AI models feel limited and why achieving full imagination-to-output realism is almost impossible. It shows that the current LLM ecosystem is basically feeding on already existing data, recycling and reorganizing what has been previously encoded. Engineers are already saying that training new models is becoming harder because we have stopped producing truly new material and rely too much on existing models to make more material. This work exposes that loop. It turns the entire AI narrative into something closer to a global extraction system than a creative engine.
What I also appreciate is that Calculating Empires visualizes this entire structure in a way that an AI model itself would never be able to. The artwork is visually clear, conceptually strong and creatively complete. It proves, in a very direct way, how much of AI output is non-original. Even if you asked an LLM to explain itself, it would never produce such a coherent, emotionally intelligent and truthfully grounded visualization. That is why this piece, almost accidentally, becomes evidence against the idea that AI is “intelligent.” It is really a reflection of us, our systems and how we behave.
The work also made me think about how humans are not very different. We call ourselves original thinkers, but most of our behavior, responses, habits and beliefs are trained by our surroundings. We imitate parents, communities, culture, social rules, media, and internalize what is “right” or “wrong.” We are also a model trained on previous data. So part of me thinks LLMs might eventually mirror this aspect of human learning. But still, the artwork makes it clear that the scale, cost and impact of AI systems are enormous. It goes beyond just algorithms and talks about the entire network: water usage, rare earth minerals, servers, labor, supply chains and geopolitical power. It is a complete ecosystem, not just a clever piece of software.
All of this makes the artwork feel not just interesting but necessary. It presents AI not as a futuristic fantasy but as a global organism, a huge infrastructure that needs to be understood before being blindly celebrated. It gives me a realistic perspective on the gap between imagination and reality in the AI world.
Calculating Empires immediately stood out to me because it doesn’t just show AI as a tool or as a cultural trend. It explains the entire anatomy behind what we call “AI.” I really liked how the work breaks down everything that happens from the moment you type a prompt to the moment an LLM produces a response. It turns something that is usually treated as magical into something mechanical, industrial and heavily interconnected. This actually makes the term “artificial intelligence” feel inaccurate, because the work shows how these systems are neither artificial nor intelligent in the way we imagine. They are infrastructures, pipelines and layers of computation powered by real materials, energy and human labor.
The piece also helped me understand why today’s AI models feel limited and why achieving full imagination-to-output realism is almost impossible. It shows that the current LLM ecosystem is basically feeding on already existing data, recycling and reorganizing what has been previously encoded. Engineers are already saying that training new models is becoming harder because we have stopped producing truly new material and rely too much on existing models to make more material. This work exposes that loop. It turns the entire AI narrative into something closer to a global extraction system than a creative engine.
The work also made me think about how humans are not very different. We call ourselves original thinkers, but most of our behavior, responses, habits and beliefs are trained by our surroundings. We imitate parents, communities, culture, social rules, media, and internalize what is “right” or “wrong.” We are also a model trained on previous data. So part of me thinks LLMs might eventually mirror this aspect of human learning. But still, the artwork makes it clear that the scale, cost and impact of AI systems are enormous. It goes beyond just algorithms and talks about the entire network: water usage, rare earth minerals, servers, labor, supply chains and geopolitical power. It is a complete ecosystem, not just a clever piece of software.