Machine Teaching para Sistemas Autonomos Industriales

 
Spanish Introductory and overview AI & Machine Learning

Machine Teaching es un nuevo enfoque complementario del Machine Learning que puede ser utilizado por quienes no tienen experiencia en IA. Con Machine Teaching, un problema complejo se divide en habilidades individuales y da a la IA pistas importantes sobre cómo aprender más rápido, centrándose en la extracción de conocimientos de los expertos, en lugar de sólo de los datos. Mientras que el Reinforcement Learning tradicional es un enfoque que requiere mucho tiempo y mucho ensayo y error, Machine Teaching acelera y mejora el proceso de aprendizaje e incluso permite a los ingenieros reutilizar los pasos individuales. Dado que las empresas no pueden permitirse desconectar equipos críticos o arriesgarse a dañar un sistema mientras la IA aprende, el proceso de Reinforcement Learning se lleva a cabo en entornos simulados seguros y rentables, que pueden reproducir millones de escenarios diferentes del mundo real, incluidas situaciones límite como el fallo de un sensor, para que la IA pueda aprender a adaptarse. El Machine Teaching también facilita la comprensión y la auditoría del comportamiento del sistema de control autónomo una vez desplegado, lo que es crucial para las aplicaciones de seguridad crítica.

Speaker

Patricio Cofre

EY Partner and Microsoft MVP/RD

Master of Engineering Management, Northwestern University and Master of Engineering Sciences, Catholic University of Chile. Patricio has developed and led projects in analytics for various companies in Latin America. He has specialized in Data Lakes and Artificial Intelligence. Patricio is co-founder of Metric Arts, a Big Data and Analytics specialized firm, recently acquired by EY (Ernst and Young). He is also lecturer at the Catholic University of Chile and Florida University in USA. In 2016 Patricio was recognized as Microsoft Artificial Intelligence MVP and Regional Director.

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