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Evaluación de Inteligencia Artificial

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Evaluación de Inteligencia Artificial

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Preguntas clave sobre IA

Created by

Jhon Christian Alejo Pocoma
Jhon Christian Alejo Pocoma
Bolivia

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Evaluación de Inteligencia Artificial
 

Evaluación de Inteligencia ArtificialOnline version

Preguntas clave sobre IA

by Jhon Christian Alejo Pocoma
1

¿Qué definición resume mejor la inteligencia artificial?

2

¿Qué es el aprendizaje automático?

3

Diferencia principal entre aprendizaje supervisado y no supervisado es...

4

¿Qué es una red neuronal artificial?

5

¿Qué describe el overfitting?

6

¿Qué preocupación ética es clave en IA?

7

¿Para qué sirve el procesamiento de lenguaje natural (NLP)?

8

¿Qué estudia la visión por computadora?

9

¿Qué aprende un agente por aprendizaje por refuerzo?

10

Cuáles son desafíos actuales de IA?

Explicación

La IA busca emular capacidades humanas como aprendizaje y razonamiento, no solo ejecutar instrucciones fijas.

El aprendizaje automático se basa en datos para aprender patrones y hacer predicciones.

En aprendizaje supervisado hay entradas y salidas etiquetadas; en no supervisado solo hay datos sin etiquetas.

Las redes ajustan conexiones (pesos) para aprender a mapear entradas a salidas.

El overfitting reduce la capacidad de generalización a datos nuevos.

Sesgos en datos o modelos pueden llevar a decisiones discriminatorias.

NLP permite tareas como traducción, resumen y extracción de información.

La visión por computadora permite reconocer objetos, escenas y acciones en imágenes.

El agente explora y recibe recompensas para mejorar sus decisiones.

Se busca que los modelos sean fiables, comprensibles y seguros.

 
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