deep learning...logic...continuous...semantics Let me guess, the continuous is for the deep learning neural layer function transformations that backprop is using for the gradient decent? So:
deep learning...logic...semantics But how do you feed a gradient descent based algorithm SEMANTICS ???? Transformers use if I'm correct things like semantics, attention..... But what is the actual cheesy meat to AGI? Is it semantics and attention etc OR is it gradient descent ex. Backprop? Backprop does nothing but let the actual AI mechanisms do their job. So: logic...semantics/attention/etc And this logic, why where do this come from ?? AND OR NOT? A>B? A=B? A brain only uses contextual windows of any length ex. predict the next word or a translate word based on the last 5/ or 4/ or n amount of words of the context given. Any higher complexity like ok look at this word 'cancer' in this sentence solo and analyze it or re read the sentence 5 times etc is just the original AI mechanisms working their thing... So: semantics/attention/etc ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T54594b98b5b98f83-Me7a2305ca72c2f7114d9aef8 Delivery options: https://agi.topicbox.com/groups/agi/subscription
