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 fond of the wierd figures riding the cart in the lower left. )
1. Intro: Two viewpoints on the Mind 2. Universal Learning Machines 3. Historical Interlude 4. Dynamic Rewiring 5. Brain Architecture (the whole brain in one picture and a few pages of text) 6. The Basal Ganglia 7. Implications for AGI 8. Conclusion
INTRO: TWO VIEWPOINTS ON THE MIND Few discoveries are more irritating than those that expose the pedigree of ideas.
-- Lord Acton (probably)
Less Wrong is a site devoted to refining the art of human rationality, where rationality is based on an idealized conceptualization of how minds should or could work. Less Wrong and its founding sequences draws heavily on the heuristics and biases literature in cognitive psychology and related work in evolutionary psychology. More specifically the sequences build upon a specific cluster in the space of cognitive theories, which can be identified in particular with the highly influential "evolved modularity" perspective of Cosmides and Tooby.