CAMINHANDO POR MILHÕES DE DIMENSÕES
DOI:
https://doi.org/10.56238/arev8n3-119Palavras-chave:
Aprendizado de Máquina, Geometria do Erro, Alta Dimensão, Otimização, Inferência Bayesiana, Sistemas SociotécnicosResumo
Este trabalho propõe uma interpretação geométrica do aprendizado de máquina como um processo de navegação em um espaço de parâmetros de alta dimensão, cujo relevo é induzido pela função de erro. Redes neurais são analisadas como sistemas que exploram uma paisagem abstrata de otimização, onde arquitetura, dados, algoritmos de treinamento e fontes de incerteza moldam a topologia da aprendizagem. A abordagem estabelece conexões entre dinâmica de otimização, capacidade de generalização e inferência bayesiana, sugerindo que tais fenômenos podem ser compreendidos sob uma estrutura geométrica unificada. Para além do domínio técnico, discute-se como essa perspectiva influencia a interpretação, a governança e o impacto sociotécnico de sistemas de inteligência artificial, oferecendo uma linguagem conceitual integrada para analisar sua atuação em contextos humanos e computacionais.
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