https://community.singularitynet.io/t/pre-release-building-on-gpt-2s-successors-blender-and-pplm/2958/3
Notice by the end I solidify the concept that 1) more data improves prediction, 2) new data (exploring) does even more, and 3) favorite data (exploit) does even more! And we evolve/update our filters, unlike Blender/ PPLM which don't evolve/ update them. The same concept is done in RL for learning to walk, but it's more powerful if done for text/ vision! To make Blender/ PPLM more AGI-like, you force it to talk about food/breeding (survival) most, then it leaks in the embed space to related nodes. It's just generalization to past memories to help prediction, like done in GPT-2, BUT it must save/ update checkpoints! These desires/ forcing like in Blender/ PPLM, drive (as they call it) the model prediction/ attention. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T3cd584667cb2384b-M81a617d555dbcb43a4ac5d0e Delivery options: https://agi.topicbox.com/groups/agi/subscription
