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Descripción
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| ¿Biological learners have the ability to learn autonomously, in an ever changing and uncertain world. This property includes the ability to generate their own supervision, select the most informative training samples, produce their own loss function, and evaluate their own performance. More importantly, it appears that biological learners can effectively produce appropriate internal representations for composable percepts ¿¿ a kind of organizational scaffold ¿ ¿ as part of the learning process. By contrast, virtually all current approaches to machine learning typically require a human supervisor to design the learning architecture, select the training examples, design the form of the representation of the training examples, choose the learning algorithm, set the learning parameters, decide when to stop learning, and choose the way in which the performance of the learning algorithm is evaluated. This strong dependence on human supervision is greatly retarding the development and ubiquitous deployment autonomous artificial learning systems. Although we are beginning to understand some of the learning systems used by brains, many aspects of autonomous learning have not yet been identified.¿ | |
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Internacional
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Si |
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ISSN o ISBN
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978-1-4244-6917-8 |
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Entidad relacionada
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IEEE |
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Nacionalidad Entidad
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Sin nacionalidad |
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Lugar del congreso
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Barcelona-SPAIN |