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Hacia una inteligencia artificial eficiente y fiable a través de principios neuromórficos

Bipin Rajendran1, Osvaldo Simeone1, Bashir Al-Hashimi2

  • 1Northeastern University London , London, UK.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|February 28, 2026
PubMed
Resumen
Este resumen es generado por máquina.

La inteligencia artificial (IA) necesita nuevos principios más allá de las grandes redes neuronales para ser más eficiente y fiable. La adopción de conceptos de ingeniería neuromórfica inspirados en el cerebro puede conducir a un desarrollo de IA sostenible.

Palabras clave:
6Gaprendizaje profundocomputación neuromórficaaprendizaje automático cuánticocuantificación de la incertidumbre

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Área de la Ciencia:

  • Inteligencia Artificial
  • Ingeniería Neuromórfica
  • Computación Sostenible

Sus antecedentes:

  • La IA actual se basa en grandes redes neuronales entrenadas en GPU, lo que genera altos costos y consumo de energía.
  • Este enfoque centrado en el hardware corre el riesgo de favorecer algoritmos adecuados para el hardware actual sobre otros intrínsecamente superiores.
  • Los modelos de IA existentes a menudo carecen de fiabilidad, no cuantifican la incertidumbre y producen resultados incorrectos con confianza.

Objetivo del estudio:

  • Proponer un cambio de los paradigmas actuales de IA hacia sistemas más eficientes y fiables.
  • Esbozar los principios clave de la ingeniería neuromórfica para diseñar la IA futura.
  • Explorar cómo la computación inspirada en el cerebro puede abordar las limitaciones de la IA actual.

Principales métodos:

  • Discusión de seis principios neuromórficos centrales: modelos recurrentes con estado, sparsidad dinámica extrema, aprendizaje sin retropropagación, toma de decisiones probabilística, computación en memoria y codiseño hardware-software.
  • Revisión de la investigación previa relevante en cada área de principio.
  • Identificación de direcciones futuras de investigación.

Principales resultados:

  • Identificación de seis principios neuromórficos clave aplicables a algoritmos, arquitecturas y hardware de IA.
  • Potencial de estos principios para guiar el desarrollo de sistemas de IA más eficientes, fiables y sostenibles.
  • Destacando la sinergia entre la ingeniería neuromórfica y el avance de la IA.

Conclusiones:

  • Lograr una IA eficiente y fiable requiere la adopción de principios neuromórficos.
  • La computación inspirada en el cerebro ofrece un camino para superar las limitaciones de la escalada actual de la IA.
  • El desarrollo futuro de la IA debería integrar el codiseño algorítmico, arquitectónico y de hardware informado por la neurociencia.