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IA: fundamentos básicos

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Conceptos clave de IA

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IA: fundamentos básicos
 

IA: fundamentos básicosOnline version

Conceptos clave de IA

by Marcelo Dominguez Ricardez
1

¿Qué es la inteligencia artificial (IA)?

2

Diferencia entre IA débil y IA fuerte

3

¿Qué es el aprendizaje automático?

4

Supervisado vs no supervisado

5

Redes neuronales son…

6

¿Qué es un conjunto de datos sesgado?

7

Sobreajuste (overfitting)

8

Entrenamiento vs inferencia

9

Ética en IA

10

Ejemplos de IA en la vida diaria

Explicación

La IA busca emular aprendizaje, razonamiento y percepción, no simples cálculos.

La IA débil está orientada a una tarea; la fuerte tendría capacidades similares a la humana.

El aprendizaje automático utiliza datos para ajustar modelos.

El supervisado aprende con datos etiquetados; el no supervisado sin etiquetas.

Las redes neuronales ajustan pesos para reconocer patrones.

El sesgo en datos puede producir decisiones injustas o erróneas.

Overfitting ocurre cuando el modelo no generaliza a datos nuevos.

Entrenamiento crea el modelo; la inferencia usa el modelo ya entrenado.

La ética aborda sesgos, privacidad y seguridad en IA.

Aplicaciones como asistentes, sugerencias y reconocimiento usan IA.

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