Resolved: On balance, the use of artificial intelligence in art, music, and literature is undesirable (Resources for TX UIL LD)

Other resources: Facebook Group for Coaches | Film about the issue: The Wizard of AI (2023) Single Channel HD Video Essay, 19m 19s, subtitled | Humanity Amplified: The Fusion of Deep Learning and Human Insight to Shape the Future of Innovation (a comprehensive report that will help you understand what is up with AI)

Generating an image takes as much energy as charging an iPhone

Melissa Hekila, December 1, 2023, MIT Review, Making an image with generative AI uses as much energy as charging your phone,,is%20significantly%20less%20energy%2Dintensive.

Generating a single image takes as much energy as charging a phonte Each time you use AI to generate an image, write an email, or ask a chatbot a question, it comes at a cost to the planet. In fact, generating an image using a powerful AI model takes as much energy as fully charging your smartphone, according to a new study by researchers at the AI startup Hugging Face and Carnegie Mellon University. However, they found that using an AI model to generate text is significantly less energy-intensive. Creating text 1,000 times only uses as much energy as 16% of a full smartphone charge. Their work, which is yet to be peer reviewed, shows that while training massive AI models is incredibly energy intensive, it’s only one part of the puzzle. Most of their carbon footprint comes from their actual use. The study is the first time researchers have calculated the carbon emissions caused by using an AI model for different tasks, says Sasha Luccioni, an AI researcher at Hugging Face who led the work. She hopes understanding these emissions could help us make informed decisions about how to use AI in a more planet-friendly way. Luccioni and her team looked at the emissions associated with 10 popular AI tasks on the Hugging Face platform, such as question answering, text generation, image classification, captioning, and image generation. They ran the experiments on 88 different models. For each of the tasks, such as text generation, Luccioni ran 1,000 prompts, and measured the energy used with a tool she developed called Code Carbon. Code Carbon makes these calculations by looking at the energy the computer consumes while running the model. The team also calculated the emissions generated by doing these

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