On Saturday, December 11, 2021, at 11:41 PM, James Bowery wrote: > dynamical rather than statistical Isn't a dynamic model one that has its neural config settings adapt as it learns? For example, for a given prompt, if it sees only 3 new completions 1 thousand times ex. constantly only we went to room, or we went to stairs, or we went to table, then it learns quickly to predict those 3 things strongly since it is sure no other type of concept can come next but those 3.
Another example is setting lower layers for short matches to predict higher in the averaging of layers, because lower layers are more learnt, long sentences are rare. As it learns more, it can use higher layers more, though, so you set it to either use a formula that with time trusts higher layers more, or set it to use if it has been predicting good lately using the higher layer then do so again for next immediate prompt or similar prompt. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T22ce813ce07d9b1a-Mf8e601220eb2eac980ae6edc Delivery options: https://agi.topicbox.com/groups/agi/subscription
