I'm writing something bigger, but I really want to release something new I discovered. I hope you can wrap your head around it for now.
Semantics/ Embeds/ Translation allows GPT-2 to recognize a sequence of text to various memories and "see" what word follows next. This helps, so much. It let's it generalize and gather more experience. It's very real and really works. Blender and PPLM built on GPT-2, at least it looks exactly like that. They force it to talk about certain topics like politics or flowers. They essentially gave it desires / a focus instead of talking about all sorts of domains, of which won't help it Survive. My big discovery is that we start off at birth wanting food and mating, and we recognize similar words by shared contexts, like money, farming, shelter, cars, science, etc, which get us our Survival needs to spread our genes, or AI designs (the new evolution). This infects the "money" node with reward now, and forces the AI to start blabbering about money all day....It makes it specialize and evolve new goals, so that it collects/ generates new data from a relevant domain. All output of the AI is only to control input, so that it doesn't intake data from random sources such as websites, topics, or lab tests. It specializes it's source so it's non-random inputs. What this reward updating is, is Semantics/ Embeds/ Translation. The only difference is it is saving checkpoints where to exploit, then searching there. So instead of GPT-2 asking "I will cure cancer by " it focuses on viewing that semantically as mostly (or starting off already at) "I will repair cells by ". RL for learning to walk does the exact same thing, it specializes its motor actions until it gets most reward/ acceleration. In our case, our reward is Prediction Accuracy. ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T3cd584667cb2384b-Mad265d8777db3359d4d8dde4 Delivery options: https://agi.topicbox.com/groups/agi/subscription
