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The Brain as a Universal Learning Machine

This article presents an emerging architectural hypothesis of the brain as a
biological implementation of aUniversal Learning Machine. I present a rough but
complete architectural view of how the brain works under the universal learning
hypothesis. I also contrast this new viewpoint - which comes from computational
neuroscience and machine learning - with the older evolved modularity hypothesis
popular in evolutionary psychology and the heuristics and biases literature.
These two conceptions of the brain lead toverydifferent predictions for the
likely route to AGI, the value of neuroscience, the expected differences between
AGI and humans, and thus any consequent safety issues and dependent strategies.



(The image above is from a recentmysterious postto r/machinelearning, probably
from a Google project that generates art based on a visualization tool used to
inspect the patterns learned by convolutional neural networks. I am especially
fon ....

Google Deepmind , Ray Kurzweil , Memory Networks , Universal Learning , Two Viewpoints , Less Wrong , Stochastic Gradient Descent , Deep Learning , Turing Machine , Universal Learning Machine , Neural Turing , Basal Ganglia , Basal Ganglia Cortical , Long Short Term Memory ,