Mirror Particle construye modelos del mundo del comportamiento humano porque los LLM no son suficientes para predecir lo que hace la gente

Mirror Particle presents “world models” as a necessary evolution in response to current language models. While large language-based AI systems excel at generating text through statistical correlations, they lack the causal capacity to predict actual human behavior. This limitation hinders their effective use in applications that require individual-level precision, such as targeted advertising or the design of public policies, where predicting specific actions is more valuable than generating plausible narratives. The relevance of this proposal for the open data sector lies in the paradigm shift required for its development. World models demand an architecture that integrates observable behavioral data and specific contextual information, going beyond the text available on the web. This implies that the quality and openness of behavioral datasets are fundamental for training these causal representations, shifting the focus from the volume of text to the depth of inference about human decision-making in real-world environments. The article also highlights the complex ethical and regulatory implications of this technology. By enabling precise behavioral prediction, it raises the risk of manipulation and surveillance, necessitating strict legal frameworks. For the open data ecosystem, this underscores the urgent need to balance technical innovation with privacy protection, ensuring that access to personal data for predictive purposes complies with emerging regulations such as the AI Act, thereby preventing abusive uses of artificial intelligence.

Source: wwwhatsnew.com
Published on 2026-10-08

Mirror Particle construye modelos del mundo del comportamiento humano porque los LLM no son suficientes para predecir lo que hace la gente

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