With my viz in the video (the image of the hierarchy), input goes up and activates nodes, and as well the predicted next word (parent nodes), but only the winner node (usually top candidate probability). The energy chaining the text in my design does not need to flow back down the net (generate output) to do this, because energy just leaks and keeps leaking, as you talk to yourself in your brain you hear the next word predicted and loops back into your net from bottom but maybe not as I just explained why...it would only loop back and activate the same node anyway. Another thing I said was you could duplicate the net and flip it so input goes up, output goes up out, not back down, but again, unneeded duplication and doesn't make it faster in this case.
Also, humans read text word by word level usually, not parallelly, hence far back nodes are losing energy, you must implement that in a parallel approach. Also as it talks to itself and humans it can only generate 1 word of the future and doesn't have it all yet. So for training on big data, you could do a parallel approach, but not for new data. Also the brain learns a a bi-directional context around a word feature, when it predicts the next word it only uses the left hand side past but its memory let's it see into the future before write next words, so in this sense learning a whole sentence fed in in parallel doesn't make the hierarchies any different, it increments frequencies (strength), adds nodes/connections, etc, same way as non-parallel approach, and the brain is predicting by looking ahead and is also using bi-directional network storage to recognize the feature it is looking at too. So, learning data in parallel seems to work (storage-wise, all data/ relationships are/ can be captured), and prediction of new words/data is done in the net by leaking activity, bi-directional translation and future look ahead still work for prediction too. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Tf06e133ecd7df7c9-M51e0c7dfe56f52acad0aff96 Delivery options: https://agi.topicbox.com/groups/agi/subscription
