I have found something pretty big after lots of research into AGI. It’s something you should add to your bag/ belt as it’s pretty straightforward and a key part to AGI.
PPLM and Blender are essentially if you will a GPT-2 but can basically recognize certain features (NSFW) or (about cars) and act bad or good on them (ex. shut down user if abusive), and, can drive which words to predict / ignore as well ex. talk about cars and dogs but not snakes. Blender also trained on chat logs, wiki, and empathy datasets, and decides how long of a response to generate and when to end that ex. not “birds fly using” but “birds fly using wings.”.
So the thing I want to share. You can see the image attached I made, to help understand. https://workupload.com/file/R22egDqDHbj
I have realized a very large next step for Blender/ PPLM. I want to keep it short here but fully detailed still. So you know how GPT-2 recognizes the context prompt to many past experiences/ memories, right? It generalizes / translates the sentence, and may decide bank=river, not TDbank. Well this is one of the things that helps it a lot. Now, you know how humans are born with low level rewards for food and mates, right? Well through semantic relation, those nodes leak/ update reward to similar nodes like farming/ cash/ homes/ cars/ science. Then it starts talking/ driving all day about money, not just food. It specializes/ evolves its goal / domain. Why? Because it’s collecting/ generating new data from specific sources/ questions / context prompts, so that it can answer the original root question of course. It takes the installed question wanting an outcome ex. “I will stop ageing by _” and is what I said above: “recognizes the context prompt to many past experiences/ memories” except it permanently translates into a narrower domain to create a “checkpoint(s)”. So during recognizing a Hard Problem context prompt / question we taught it/installed like “I will stop ageing by _” - it jumps into a new translation/ view and creates a new question / goal “I will create AGI by _”. It’s semantics, it’s gathering related predictions from similar memories, same thing, just that it is picking specific semantic paths, updating, just like RL. RL for text (prediction is objective).