Bias and Error Mitigation in Software-Generated Data: An Advanced Search and Optimization Framework Leveraging Generative Code Models

Data generation and analysis is a fundamental aspect of many industries and disciplines, from strategic decision making in business to research in the physical and social sciences. However, data generated using software and algorithms can be subject to biases and errors. These can be due to problems with the original software, default settings that do not align with the specific needs of the situation, or even deeper problems with the underlying theories and models. This paper proposes an advanced search and optimization framework aimed at generating and choosing optimal source code capable of correcting errors and biases from previous versions to address typical problems in software systems specializing in data analysis and generation, especially those in the corporate and data science world. Applying this framework multiple times on the same software system would incrementally improve the quality of the output results. It uses Solomonoff Induction as a sound theoretical basis, extending it with Kolmogorov Conditional Complexity, a novel adaptation, to evaluate a set of candidate programs. We propose the use of generative models for the creation of this set of programs, with special emphasis on the capabilities of Large Language Models (LLMs) to generate high quality code

Generación y extracción automática de metadatos : el proyecto DCS

The DCS or Dublin Core Services Project (Http://www.describethis.com) is exposed. It is a set of services and applications for the automatic extraction and generation of metadata. This software tries to support the development and extension of the application of the Dublin Core format

Espacios y destinos turísticos en tiempos de globalización y crisis

2 volúmenesXII Coloquio de Geografía del Turismo, Ocio y Recreación de la Asociación de Geógrafos Españoles. Colmenarejo (Madrid), del 17 al 19 de junio de 2010.Este libro ha sido editado con la colaboración económica del Ministerio de Ciencia e Innovación (ref. CS02010-10416-E)