Hacer una imagen con IA consume tanta energía como cargar tu teléfono
A joint study by Hugging Face and Carnegie Mellon University has, for the first time, quantified the environmental impact of artificial intelligence, revealing that the true energy cost lies not only in model training but also in everyday use. The data show that tasks such as image generation are extremely energy-intensive, producing carbon emissions significantly higher than those generated during the development phase. This finding underscores the need to consider the cumulative carbon footprint over the technology’s entire lifecycle, beyond the initial creation costs. A critical dilemma is identified between versatility and efficiency: general-purpose generative models consume far more energy than models specialized for specific tasks. Using generalist tools for simple functions is inefficient and polluting, suggesting that optimizing energy consumption depends on selecting the most appropriate technological solutions for each need. This distinction is essential for reducing environmental impact without sacrificing functionality, promoting the use of fine-tuned models when generality is not required. For the open-data community, this study is relevant because it highlights transparency and traceability as pillars of sustainable AI. By making measurement methods and results public, the research fosters accountability among both developers and end users. The openness of these environmental data enables the community to make informed decisions, demand efficiency standards, and promote an AI ecosystem that balances technological innovation with sustainability, ensuring that digital progress does not compromise the environment.
Source: eju.tvPublished on 2023-12-02