TLDR: Hoel proposes a new theory for the function of dreams, inspired by machine learning’s concept of overfitting. He suggests dreams act as augmented samples of waking experiences to improve generalization and robustness of neural representations, preventing them from becoming too specific to waking experiences. It explains why dreams don’t become more realistic over time and offers insights for designing algorithms that mimic the phenomenology of dreaming to improve artificial neural networks. Hoel’s theory may lead to strategies for promoting robust learning through dream substitutions or other consciousness-altering experiences.
TLDR: Hoel proposes a new theory for the function of dreams, inspired by machine learning’s concept of overfitting. He suggests dreams act as augmented samples of waking experiences to improve generalization and robustness of neural representations, preventing them from becoming too specific to waking experiences. It explains why dreams don’t become more realistic over time and offers insights for designing algorithms that mimic the phenomenology of dreaming to improve artificial neural networks. Hoel’s theory may lead to strategies for promoting robust learning through dream substitutions or other consciousness-altering experiences.