So we have a context of 9 tic tac toe squares with 2 of your Xs in a row and his Os all over the place, you predict something probable and rewardful, the 3rd X to make a row. GPT would naturally learn this, Blender would also the reward part too, basically.
As for a FORK, this is like two-of favorite meals. Give me some fries.....or I could have said Give me some cake. I predict them about 50% each, based on how rewardful and popular they are seen in the data. In that case 50% the time I choose fries, then next time cake because fries has been inhibited and fired its neural energy now, changing the distribution. It's ok to pursue logic but I can't help but point out this sound exactly like my and Transformer AI. In fact, both those are same, simply the approach is different to solve the efficiency problem. In this case, I don't see how yours would be efficient, it seems like a GOFAI no? Isn't it GOFAI? This is not something that scales like GPT, AFAIK your logic based approach is focusing on a few rules and disregards how many resources it needs (compute doesn't matter, memory neither). *_How does your approach, to predict B for some context A, be efficient like GPT? There is a lot to leverage when given a context, and GPT leverages it. Or, if you intend to use Transformer+logic, why? Transformer already does all methods you mentioned to leverage context._* ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T74958068c4e0a30f-Madc00c6b2628f6dd840d2df0 Delivery options: https://agi.topicbox.com/groups/agi/subscription
