Share your questions here. Here's mine for now. OpenAI shows their 'net' can work for vision, text, and motor, like the brain does. It is therefore agnostic to the data type, like the brain is. But how does vision and text translate? Yes, they both have frequencies, similar synonyms, segmentation, reward, for nodes. But images, ignoring video, need robustness for rotation, scale, location. Does text have those? Text has only location offset ex. "the cat was not at" = "that cat I had was only at". I think I get it, location and stretching scale are just offsets. But how does vision be robust to 180 degree rotation? Explain very clearly, using a neural hierarchy. I know the features are relatively unmoved to each other, but doesn't the brain have to try rotating it different ways to get it in right way?
@Matt, if we were to attempt those prize compressors against image prediction/completion, how would we change the algorithm? How would my trie tree be? I know less about vision, so I wish others would fill me in using clear examples. I seen OpenAI used a Transformer to predict the rest of an image, see their blog post from months back if need. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tc67faac3048278cf-M578c73220feb73dd3878e1c5 Delivery options: https://agi.topicbox.com/groups/agi/subscription
